Citation
Genetic Studies on the Novel Spotted Wilt Resistance of Peanut [Arachis Hypogaea L.] Cultivar, Florida-Ep-113

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Title:
Genetic Studies on the Novel Spotted Wilt Resistance of Peanut [Arachis Hypogaea L.] Cultivar, Florida-Ep-113
Creator:
Tseng, Yu-Chien
Place of Publication:
[Gainesville, Fla.]
Florida
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University of Florida
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english
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1 online resource (156 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agronomy
Committee Chair:
TILLMAN,BARRY
Committee Co-Chair:
WANG,JIANPING
Committee Members:
DUFAULT,NICHOLAS S
ROWLAND,DIANE L
GEZAN,SALVADOR
Graduation Date:
4/30/2016

Subjects

Subjects / Keywords:
Breeding ( jstor )
Breeding value ( jstor )
Chromosomes ( jstor )
Diseases ( jstor )
Genomes ( jstor )
Heritability ( jstor )
Infections ( jstor )
Peanuts ( jstor )
Phenotypic traits ( jstor )
Quantitative trait loci ( jstor )
Agronomy -- Dissertations, Academic -- UF
breeding -- heritability -- mas -- peanut -- tswv
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Agronomy thesis, Ph.D.

Notes

Abstract:
Spotted wilt caused by tomato spotted wilt virus (TSWV) is one of the major diseases affecting peanut (Arachis hypogaea L.) production in the Southeastern USA. Occurrence, severity, and symptoms of spotted wilt disease are highly variable from season to season making it difficult to efficiently evaluate breeding populations for resistance. Molecular markers linked to spotted wilt resistance could overcome this problem and allow selection of resistant lines regardless of seasonal conditions. The heritability of spotted wilt resistance is also important in helping breeders to predict breeding values of future generations. A total of 163 F2 progenies were derived from a cross between Florida-EPTM'113', a TSWV resistant cultivar and Georgia Valencia, a susceptible cultivar. The F2:3, F2:4, and F2:5 populations were phenotyped by visual rating and/or immunostrip test in two different locations, Marianna and Citra, FL. More than 2500 markers were screened through the whole peanut genome against two parental lines. Around 100 markers flanking known QTLs were tested and the polymorphic markers were used to genotype the whole F2 population. The QTL analysis showed that 14 markers on linkage group A01 were linked with a TSWV resistant QTL region, which was the same QTL region identified previously. This QTL is validated by different populations and fine mapping will be conducted by utilizing F5:6 population. More markers located within the region will be developed in order to obtain markers closely linked to spotted wilt resistance. Multi-environment trial (MET) and bivariate analysis were conducted. The results indicated that heritability of TSWV resistance using immunostrip measurement was higher than the overall plot visual rating on single site analysis. The MET analysis also supported the results: both the type B genetic correlation and heritability of immunostrip results (correlation: 0.84; heritability: 0.69) were higher than visual rating (correlation: 0.75; heritability: 0.39), which suggested that the selection based on the immunostrip can be more efficient regardless of the seasonal impacts (years, locations, high/low disease pressure). ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2016.
Local:
Adviser: TILLMAN,BARRY.
Local:
Co-adviser: WANG,JIANPING.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2017-05-31
Statement of Responsibility:
by Yu-Chien Tseng.

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Source Institution:
UFRGP
Rights Management:
Copyright Yu-Chien Tseng. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
5/31/2017
Classification:
LD1780 2016 ( lcc )

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GENETIC STUDIES ON SPOTTED WILT RESISTANCE IN PEANUT [ Arachis hypogaea L.] CULTIVAR, FLORIDA EP TM By YU CHIEN TSENG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2016

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2016 Yu Chien Tseng

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To my lovely family To my country, Taiwan Lokah Formosa

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4 ACKNOWLEDGMENT I would first and foremost to thank my advisor and co advisor, Dr. Barry Tillman and Dr. Jianping Wang, for all the enlightening guidance, selfless support, and endless patience throughou t my Ph.D career. Without their encouragement and instruction, I could not have completed this work. Additionally, I would like to express my gratitude to my committee members, Dr. Diane Rowland, Dr. Salvador Gezan and Dr. Nichola s Dufault for all the instruction and encouragement. Special appreciation goes to Spurthi Nayak Andrea Villa Liping Wang Lubin Tan Jian Song Wenlan Tian Ze Peng and Dev Paudel for all the assistance. Moreover, I would like to thank the peanut breeding groups in Marianna and Citra FL : Mark Gomillion Justin Mckinney Steven Thorn ton, Brad Peeler, James Crawford Tracey Smith Glenda Smith and George Person for offering the support when needed I would also like t o thank my friend s in Taiwan and in Gainesville FL for the ir friendship. Thanks to Ahan Yang Yih Feng Hsieh Jane Yeh Debbie Tsai Joy Song Jude Chung Ron Chi Kuo and Pei Wen Huang for their valuable encouragement and spiritual support. Their friendships strengthen my will and confidence to finish this study. Special thank goes to Te Sheng Wei Jimmy Huang, Dingyu Wang and Freddy Lim Your great contribution and enthusiasm to the society always remind me to be humble and have a solid mind to pu rsue the things I love. Last but certainly not the least, I give thanks to my family. Their endless love and support are deeply appreciated. I am grateful for my home country of Taiwan, which has continuously inspired me to become a better person Tso kok Ti un, Ti un B su

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5 TABLE OF CONTENTS Page ACKNOWLEDGMENT ................................ ................................ ................................ .................. 4 LIST OF TABLES ................................ ................................ ................................ ........................... 8 LIST OF FIGURES ................................ ................................ ................................ ....................... 10 LIST OF ABBREVIATIONS ................................ ................................ ................................ ........ 13 ABSTRA CT ................................ ................................ ................................ ................................ ... 15 CHAPTER 1 LITERATURE REVIEW ................................ ................................ ................................ ....... 17 Cultivate Peanut ................................ ................................ ................................ ...................... 17 Tomato Spotted Wilt Virus ................................ ................................ ................................ ..... 19 Pe anut Variety with Spotted Wilt Resistance ................................ ................................ ......... 22 Molecular Markers in Peanuts ................................ ................................ ................................ 25 Linkage Map ................................ ................................ ................................ ........................... 27 Quantitative Trait Loci (QTL) and Marker Assisted Selection (MAS) ................................ .. 28 Heritability ................................ ................................ ................................ .............................. 29 Genetic Correlation ................................ ................................ ................................ ................. 32 Breeding Values ................................ ................................ ................................ ...................... 33 2 UTILIZING IMMUNOASSAYS TO EVALUATE THE VIRAL DEVELOPMENT IN FLORIDA EP TM ................................ ................................ ................................ ............ 35 Introduction ................................ ................................ ................................ ............................. 35 Materials and Methods ................................ ................................ ................................ ........... 39 Experimental Design ................................ ................................ ................................ ....... 39 Tissue Collection ................................ ................................ ................................ ............. 40 Immunostri p Testing ................................ ................................ ................................ ....... 41 Statistical Analysis ................................ ................................ ................................ .......... 41 Results ................................ ................................ ................................ ................................ ..... 42 Data Analysis ................................ ................................ ................................ ................... 42 Peanut Varieti es ................................ ................................ ................................ ............... 42 Assessment Dates ................................ ................................ ................................ ............ 43 Tissue types ................................ ................................ ................................ ..................... 43 Peanut Variety and Assessment Dates ................................ ................................ ............ 43 Peanut Varieties and Tissue Types ................................ ................................ .................. 44 Assessment Dates and Tissue Types ................................ ................................ ............... 44 Peanut Varieties, Assessment Dates and Tissue Types ................................ ................... 45 Florida EP TM ................................ ................................ ................................ ... 45 Florida 07 ................................ ................................ ................................ ................. 45

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6 Georgia Green ................................ ................................ ................................ .......... 45 Georgia Valencia ................................ ................................ ................................ ...... 46 Discussion ................................ ................................ ................................ ............................... 46 Disease Progress ................................ ................................ ................................ ....... 46 The Source of Spotted Wilt Resistance ................................ ................................ .... 48 TSWV Infection on Different Tissues ................................ ................................ ..... 48 Virus Movement and The Mechanism of Spotted Wilt Resistance ......................... 49 Resistance in Florida EP TM ................................ ................................ ............. 51 Asymptomatic Infection ................................ ................................ ........................... 52 Compar ison Among Varieties ................................ ................................ .................. 53 Destructive Sampling by Immunostrip ................................ ................................ .... 54 Temperature and Physiological Function ................................ ................................ 55 Conclusion ................................ ................................ ................................ .............................. 55 3 MAPPING GENES CONTROLLING SPOTTED WILT RESISTANCE IN PEANUT CULTIVAR FLORIDA EP TM ................................ ........ 64 Introduction ................................ ................................ ................................ ............................. 64 Materials and Methods ................................ ................................ ................................ ........... 68 Plant Material and Experimental Design ................................ ................................ ......... 68 Rating for Disease Resistance ................................ ................................ ......................... 71 SSR Genotyping ................................ ................................ ................................ .............. 72 SNP Markers Development and Validation ................................ ................................ .... 73 Linkage and QTL Analysis ................................ ................................ ............................. 74 Results ................................ ................................ ................................ ................................ ..... 75 Disease Rating Distribution in the Segregation Populations ................................ ........... 75 Phenotypic Correlation ................................ ................................ ................................ .... 76 SSR Marker Screening ................................ ................................ ................................ .... 77 Polymorphic Marker Screening ................................ ................................ ....................... 78 Local SNP Marker Development ................................ ................................ .................... 79 Linkage Map Construction ................................ ................................ .............................. 79 QTL Analysis ................................ ................................ ................................ .................. 79 Discussion ................................ ................................ ................................ ............................... 80 One Gene Controlling Resistance ................................ ................................ ................... 80 One Putative Major QTL ................................ ................................ ................................ 82 Spotted wilt resistance QTLs ................................ ................................ ........................... 82 Peanut Germplasm Diversity ................................ ................................ ........................... 85 Marker Assisted Selection (MAS) ................................ ................................ ................... 86 Advanced Technology ................................ ................................ ................................ ..... 87 Conclusion ................................ ................................ ................................ .............................. 88 4 HERITABILITY OF SPOTTED WILT RESISTANCE IN Florida EP TM DERIVED POPULATIONS ................................ ................................ ................................ 110 Introduction ................................ ................................ ................................ ........................... 110 Materials and Methods ................................ ................................ ................................ ......... 113 Plant Material ................................ ................................ ................................ ................ 113

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7 Disease Evaluation Methods and Data Collection ................................ ........................ 115 Single Site Analysis (Univariate Model) ................................ ................................ ...... 116 Multi Site Analysis (Univariate Model) ................................ ................................ ........ 118 Bivariate Analysis (Multi variate Model) ................................ ................................ ..... 119 Heritability ................................ ................................ ................................ ..................... 119 Genetic Correlation ................................ ................................ ................................ ....... 120 Best Linear Unbiased Prediction (BLUP) ................................ ................................ ..... 121 Results ................................ ................................ ................................ ................................ ... 121 Heritability ................................ ................................ ................................ ..................... 121 Single site analysis ................................ ................................ ................................ 121 Multi site analysis ................................ ................................ ................................ .. 122 Genetic Correlation ................................ ................................ ................................ ....... 123 Type A c orrelation ( r A ) ................................ ................................ .......................... 123 Type B correlation ( r B ) ................................ ................................ ........................... 123 Best Linear Unbiased Prediction (BLUP) ................................ ................................ ..... 124 Visual rating ................................ ................................ ................................ ........... 124 Immunostrip testing ................................ ................................ ................................ 125 Discussi on ................................ ................................ ................................ ............................. 125 Heritability Inflation ................................ ................................ ................................ ...... 125 Type A Correlation/ Type B Correlation ................................ ................................ ...... 127 Breeding Value (BLUPs) ................................ ................................ .............................. 129 The Application in Heritability ................................ ................................ ..................... 130 Heritability in Genomic Era ................................ ................................ .......................... 130 Dynamic Heritability ................................ ................................ ................................ ..... 132 Conclu sion ................................ ................................ ................................ ............................ 133 5 SUMMARY ................................ ................................ ................................ .......................... 13 9 LIST OF REFERENCES ................................ ................................ ................................ ............. 143 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ....... 156

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8 LIST OF TABLES Table P age 2 1 Partial analysis of variance results for spotted wilt of peanut assessed on four pe anut varieties in four tissue types over five assessment dates during 2012 and 2014 at NFREC, FL ................................ ................................ ................................ ........................ 57 2 2 Partial analysis of variance results for spotted wilt of peanut assessed on four peanut varieties in four tissue types over five assessment dates at NFREC FL ........................... 57 3 1 The number of plot, replication and check at different sites in F 3 F 4 and F 5 populations. ................................ ................................ ................................ ........................ 90 3 2 The information about sites and phenotyping methods on F 3 F 4 and F 5 populations. ...... 90 3 3 The phenotyping correlation table among different datasets and the number indicated the Spearman's rank correlation coef ficient. ................................ ................................ ...... 90 3 4 The number of SSR primers screened, amplification and polymorphic ratio at different linkage groups. ................................ ................................ ................................ .... 91 3 5 The alignment and no alignment ratio of SSR primers aligned to two Arachis reference genomes. ................................ ................................ ................................ ............ 91 3 6 The alignment information of sequence reads from Florida EP TM and Georgia Valencia ................................ ................................ ................................ ............................. 92 3 7 The positions and depths of putative SNPs on A01 chromosome between Florida EP ................................ ................................ ............................... 92 3 8 The SNP primer sequence, amplicon size and Tm for validation. ................................ ..... 92 3 9 The positions of A01 markers on physical (Mb) and linkage map (cM). .......................... 93 3 10 The literature sour ces of SSR primers screened. ................................ ............................... 94 3 11 .............................. 95 3 1 2 The positions, flanking markers, LOD values, PVE (%) and additive effects of putative QTLs on A01 chromosome. ................................ ................................ ................. 97 4 1 The number of plot, replication and check at different sites in F 3 F 4 and F 5 populations. ................................ ................................ ................................ ...................... 135 4 2 The information about sites and phenotyping methods on F 3 F 4 and F 5 populations. .... 135 4 3 Genetic variance, error variance and heritability estimated by single site analysis of visual rating from PSREU and NFREC in 2012, 2013 and 2014. ................................ ... 135

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9 4 4 Genetic variance, error variance and heritability estimated by single site analysis of immunostrip testing from NFREC in 2012 and 2013. ................................ ..................... 136 4 5 Genetic variance, error variance and heritability estimated by multiple site analysis of visual rating and immunostrip testing at different environments. ............................... 136 4 6 Type A correlation estimated by bivariate analysis between visual rating and immunostrip testing from NFREC in 2012 and 20 13. ................................ ..................... 136 4 7 Type B correlation estimated by multiple site analysis of visual rating and immunostrip testing at different environme nts. ................................ ............................... 136 4 8 Maximum, minimum, range and average breeding value (BLUP) predicted by visual rating at PSREU and NFREC in 2012, 201 3 and 2014. ................................ .................. 137 4 9 Maximum, minimum, range and average breeding value (BLUP) predicted by immunostrip testing at NFREC in 2012, and 2013. ................................ ......................... 137

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10 LIST OF FIGURES Figure P age 2 1 Three prefilled Agdia sample bags with three negative (single red line) results for TSWV ................................ ................................ ................................ ............................... 58 2 2 Three prefilled Agdia sample bags with three positive (two red line) r esults for TSWV ................................ ................................ ................................ ............................... 58 2 3 Incidence of TSWV infection as determined by immuostrip testing from four peanut varietie s at NFRE C in 201 2 and 2014. ................................ ................................ .............. 59 2 4 Incidence of TSWV infection results by immuostrip testing from five assessment dates at NFREC in 2012 and 2014 ................................ ................................ .................... 59 2 5 Incidence of TSWV infection results by immuostrip testing from four tissue types at NFREC in 2012 and 2014. ................................ ................................ ................................ 60 2 6 TSWV infection detected by immunostrip at different assessment dates from four different peanut varieties planted at NFREC in 2012 and 2014. ................................ ....... 60 2 7 TSWV infection detected by immunostrip at different tissue types from four different peanut varieties planted at NFREC in 2012 and 2014. ................................ ...................... 61 2 8 TSWV infection detected by immunostrip at different assessment dates from four different tissue types planted at NFREC in 2012 and 2014. ................................ .............. 61 2 9 TSWV infection detected by immunostrip in Florida EP TM assessment dates from four different tissu e types planted at NFREC in 2012 and 2014. ................................ ................................ ................................ ................................ ... 62 2 10 TSWV infection detected by immunostrip in Florida 07 at different assessment da tes from four different tissue types planted at NFREC in 2012 and 2014. ............................. 62 2 11 TSWV infection detected by immunostrip in Georgia Green at different assessment dates from four different tissue types planted at NFREC in 2012 and 2014. .................... 63 2 12 TSWV infection detected by immunostrip in Georgia Valencia at different assessment dates from four different tissue types planted at NFREC in 2012 and 2014. ................................ ................................ ................................ ................................ ... 63 3 1 Three prefilled Agdia sample bags with three negative (single red line) results for TSWV.. ................................ ................................ ................................ .............................. 98 3 2 Three prefil led Agdia sample bags with three positive ( two red line) results for TSWV. ................................ ................................ ................................ .............................. 98

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11 3 3 TSWV infection results on F 2:3 population by visual rating at PSREU in 2012. .............. 99 3 4 TSWV infection results on the checks of F 2:3 population by visual rating at PSREU in 2012. ................................ ................................ ................................ .............................. 99 3 5 TSWV infection results on F 2:3 population by visual rating at NFREC in 2012. ............ 100 3 6 TSWV infection results on the checks of F 2:3 population by visual rating at NFREC in 2012. ................................ ................................ ................................ ............................ 100 3 7 TSWV infection results on F 2:3 population by immunostrip testing at NFREC in 2012. ................................ ................................ ................................ ................................ 101 3 8 TSWV infection results on the checks of F 2:3 population by immunostrip testing at NFREC in 2012. ................................ ................................ ................................ ............... 101 3 9 TSWV infection results on F 2:4 population by visual rating at PSREU in 2013. ............ 102 3 10 TSWV infection results on the checks of F 2:4 population by visual rating at PSREU in 2013. ................................ ................................ ................................ ............................ 102 3 11 TSWV infection results on F 2:4 population by visual rating at NFREC in 2013. ............ 103 3 12 TSWV infection results on the checks of F 2:4 population by visual rating at NFREC in 2013. ................................ ................................ ................................ ............................ 103 3 13 TSWV infection results on F 2:4 population by immunostrip testing at NFREC in 2013. ................................ ................................ ................................ ................................ 104 3 14 TSWV infection results on the checks of F 2:4 population by immunostrip testing at NFREC in 2013. ................................ ................................ ................................ ............... 104 3 15 TSWV infection results on F 2: 5 population by visual rating at NFREC in 2014. ............ 105 3 16 TSWV infection results on the checks of F 2:5 population by visual rating at NFREC in 2014. ................................ ................................ ................................ ............................ 105 3 17 PAGE gel image s for 12 plants using polymorphic SSR mar kers located on A01 chromosome. ................................ ................................ ................................ ................... 106 3 18 PAGE gel image s for 12 plants using polymorphic SSR markers located on A09 & A10 chromosomes. ................................ ................................ ................................ .......... 106 3 19 Physical and linkage map showing the position of SSR markers on A01 chromosome. ................................ ................................ ................................ .................... 107 3 20 Linkage group with SSR marker positions and the detected QTLs showing by different icons indicated different phenotyping datasets. ................................ ................ 108

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12 3 21 Linkage group with SSR marker positions and the detected QTLs showing by different color peaks indicated different phenotyping datasets. ................................ ...... 109 4 1 Three prefilled Agdia sample bags with three negative (single red line) results for TSWV . ................................ ................................ ................................ ............................ 138 4 2 Three prefilled Agdia sample bags with three positive (two red line) results for TSWV. ................................ ................................ ................................ ............................ 138

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13 LIST OF ABBREVIATIONS AFLP Amplified fragment length polymorphism BLUP Best linear unbiased prediction cM DAP DNA ELISA Centi morgans Day after planting Deoxyribonucleic acid The enzyme linked immunosorbent assay HR LG LOD H ypersensitive response Linkage group L og of odds MAS MABC Mb MET NFREC NGS Marker assisted selection Marker assisted backcrossing Megabase Multi environment trial North Florida Research and Education Center Next generation sequencing PAGE Polyacrylamide gel electrophoresis PSREU PVE QTL RAPD Plant Science Research and Education Unit P henot ypic variation explained Q uantitative trait loci Random amplified polymorphic DNA REML RFLP Restricted maximum likelihood Restriction fragment length polymorphism RIL RT PCR Recombinant inbred line Reverse transcription polymerase chain reaction

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14 RT RT PCR SNP SSR TSWV R everse transcriptase real time polymerase chain reaction Single nucleotide polymorphism Simple s equence repeat Tomato spotted wilt virus

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15 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy GENETIC STUDIES ON SPOTTED WILT RESISTANCE IN PEANUT [ Arachis hypogaea L.] CULTIVAR, FLORIDA EP TM By Yu Chien Tseng May 2016 Chair: Barry Tillman Cochair: Jianping Wang Major: Agronomy Spotted wilt caused by tomato spotted wilt virus (TSWV) is on e of the major diseases affecting peanut ( Arachis hypogaea L.) production in the s outheastern United States Resistance has been identified, but occurrence, severity, and symptoms of spotted wilt disease are highly variable from season to season making it difficult to efficiently evaluate breeding populations for resistance. Growers use integrated disease management, including many different management factors to control spotted wilt; however, plant cultivar is the most important factor. Although resistant cultivars have been developed, the peanut cultivar Florida EP TM has shown a much higher level of resistance than other cultivars. The heritability of spotted wilt resistance in Florida EP TM is important in helping breeders to predict breeding values of future generations. Molecular markers linked to spotted wilt resistance in Florida EP TM will allow selection of resistant lines effectively regardless of seasonal conditions. The first objective of this research was to evaluate viral development in Florida EP TM Four tissues (leaf, root crown, old leaf and young leaf) were collected from four varieties ( Florida EP TM Florida 07, Georgia Green and Georgia Valencia) at five time points. Immunoassays were conduc ted to detect viral presence. Florida EP TM had the lowest

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16 infection frequency of 16% compared to 67% for Florida 07, 83% for Georgia Valencia and 100% for Georgia Green. Florida EP TM had significantly reduced TSWV infection and appeared to dela y the movement of the virus throughout the plant. The mechanisms are not understood, but could be relate d to interference in virus transmission from vectors and the inhibition of viral movement within the plants. The second objective was to map the genetic components linked to resistance in Florida EP TM A population segregating for TSWV resistance was developed from a cross between Florida EP TM and Georgia Valencia, a susceptible cultivar. The F 2:3 F 2:4 and F 2:5 populations were phenotyped b y visual rating and/or immunostrip testing in two different locations, Plant Science Research and Education Unit ( PSREU ) and North Florida Research and Education Center ( NFREC ) FL. A total of 2,431 markers across the whole peanut genome were screened agai nst these two parental lines. One major quantitative trait loci ( QTL ) was identified on A01 chromosome and had up to 22.7% phenot ypic variation explained (PVE) and 9.0 LOD ( log of odds ) value. Two flanking markers, AHGS4584 and GM672, were linked to this spotted wilt resistan ce QTL. The third objective was to estimate the heritability of disease resistance in Florida EP TM Heritability can d etermine the potential of a population respond ing to selection. The F 2:3 F 2:4 and F 2:5 populations were evaluated for disease by visual rating and immunostrip testing of root crown tissue. Multi environment trial (MET) and bivariate analysi s were conduct ed. Both type B genetic correlation and heritability of immunostrip results (correlation: 0.84; heritability: 0.69) were higher than that of visual rating (correlation: 0.75; heritability: 0.39), suggesting that the selection based on the immunostrip can b e more efficient regardless of the seasonal impacts (years, locations, and disease pressure)

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17 CHAPTER 1 LITERATURE REVIEW Cultivate Peanut Cultivated peanut ( Arachis hypogaea L.) is an important annual legume, which is grown mainly in semi arid tropic and sub tropic areas in the world (Naidu et al. 1999) Almost all the wild Arachis speci es, Arachis cardenasii A. diogoi and A. batizocoi for example are diploid (2n=20) ; however, cultivated peanut is an allotetraploid legume (genome AABB, 2n=4x=40). The Arachis species originates from South America within the range of Brazil, Bolivia Par aguay, Argentina and Uruguay (Valls and Simpson, 1994) The A and B genomes most likely came from two wild Arachis species. A. duranensis (A genome) and A. ipaensis (B genome). The hybridization of two diploid species and spontaneous chromosome duplication resulted in the isolation of cultivated peanut from the wild species (Kochert et al., 1991) More than 42 million tons of peanuts in the world were produced in 2013 China is the largest peanut producer in the world followed by India, Nigeria and the United States ( FAO Statistical Databases 2015, http://faostat.fao.org/faostat/ ). In some developing countries of Asia, Africa and South America, peanut is the principal source of food protein, cooking oil and vitamins (Savage and Keenan 1994). It has higher oil content (45 52%) than many other oilseed crops and its edible oil and protein are highly nutritious for human consumption. Be sides direct human consumption, peanut can be used for animal feed as a protein rich forage for cattle (C ook and Crosthwaite, 1994; Revoredo and Fletcher, 2002). In the United States, peanut is the second most important legume crop. The major peanut products are peanut butter, candies with peanut inside, roasted peanuts and boiled

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18 peanuts. In 2013, the total United States peanut production was over 1.8 million tons and the production value was 808 million dollars (FAO Statistical Databases 2015; United States Department of Agriculture National Agricultural Statistics Service USDA NASS 2014). Peanuts are grown in three distinct regions; Southeast (Florida, Georgia, Alabama, and Mississippi), Southwest (Texas, Oklahoma, and New Mexico) and the Virginia Carolinas (North Carolina, South Carolina, and Virginia). The t otal harvested acreage was over 1.3 million acre s in 2014, and 1.57 million in 2015 (Southwest Farm Press). Georgia was the leading peanut production state, accounting for almost 50% of the acreage (777,000 acres) and produced 1,736,595 tons of peanuts in 2015. Alabama was the second, producing 3 29,975 tons on 197,000 acres Florida was the third, 328,000 tons on 180,000 acres Peanuts produced in the United States are classified into four market types from two subspecies. The major difference between the two subspecies, hypogaea and fastigiata are the absence and presence of flowers on the main stems. Flowers can be observed only on the lateral branches of hypogaea subspecies ( two botanical varieties: hypogaea and hirsut a ) I n contrast, subspecies fastigiata (four botanical varieties: fastigiata, p eruviana, aequatoriana, and vulgaris ) can produce flowers on the main stem (Hammons 1973). A. hypogaea var. hypogaea includes the Virginia and Runner market types. The second subspecies, A. hypogaea var. fastigiata includes two botanical varieties of econ omic importance: vulgaris the Spanish market type, and fastigiata the Valencia market type. The four market types are mainly classified by botanical varieties, pod size and the uses in the market (Knauft et al., 1987).

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19 Virginia peanuts have the largest pods and elongated seeds, while Runner peanuts have medium size seed compared to the Virginia type. Spanish types have smaller round seeds compared to runner types and Valencia is intermediate in size and shape with a high percentage of pods containing 3 o r more seeds (Putnam et al. 2012). Runner t ypes are primarily produced in s outheast region and are the predominant type peanuts in the United States The m ajor ity of them are used to make peanut butter (Knauft et al., 1987). Virginia types are produced more in Virginia Carolinas regions for roasted peanuts (Knauft et al., 1987). Spanish types are used to make candy and are mainly grown in Southwest regions (Knauft et al., 1987). Valencia types are primarily pro duced in Southeast regions and New Mexico for roasted and boiled peanuts (Knauft et al., 1987). Tomato Spotted Wilt Virus Tomato spotted wilt virus (TSWV) (genus Tospovirus family Bunyaviridae ). is one of the major pathogens of peanut ( Arachis hypogaea L .) and causes the disease known as spotted wilt which seriously af fects peanut production in the s outheastern U nited States. TSWV in peanut was first reported in Brazil (Cos ta, 1941) Spotted wil t impact peanut production in South America as severely as it did in North America. In the United States, TSWV was first reported in Texas in 1971 (Halliwell and Philley, 1974) and increasingly became a serious disease problem. In 1985, the yield reductions caused by spotted wilt approached approximately 50% in southern Texas (Black and Smith, 1987) In 199 7, the production losses due to spotted wilt in Georgia were estimated to be around $40 million USD (Bertrand, 1998) The typical spotted wilt foliar symptoms incl ude concentric ringspots, chlorosis and necroses on leaflets as well as mosaic patterns and stunting (Culbreath et al., 2003 ) TSWV is only transmitted by thrips but has a broad host range, including both di cots and

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20 monocots in at least 92 plant families. More than 1000 plant species including many economically important field crops such as tobacco and peanut, and vegetables such as tomato, pepper, potato, and eggplant are its hosts (Jones and Baker, 1991; Peters, 1998) Western flower thrips (Frankliniella occidentalis) and tobacco thrips (Frankliniella fusca) are the two major TSWV transmitting vec tors in peanut (Todd et al., 1990; Mitchell and Smith, 1993) Thrips acquire TSWV d u ring the larval stage by feeding on infected host plants, but only transmit the virus during adult age. TSWV particles are retained inside the vectors in a persistent manner which mean s that the viral genome can replicate inside the thrips (German et al., 1992) TSWV can be detected on pods and seed coats; however, no virus can be detected on embryos. S eeds with obvious spotted wilt symptoms on seed coats were planted, but the resulting peanut plants were still virus free. This demonstrated that TSWV i s not transmitted by seeds (Pappu et al., 1999). Mechanical inoculation could be utilized to transmit virus; how ever, it is difficult and had low efficiency (Baldessari, 2008; Mandal et al., 2001). The absence of visible foliar symptoms did not necessarily indicate no virus infection. Asymptomatic infections were observed by using immunoassays (Murakami et al., 2006 ). Some spotted wilt resistant varieties, which had low disease incidence and severity based on foliar symptoms, were found to have a high frequency of TSWV infection based on immunoassays Peanut varieties, Florida 07 showed 44% infection frequency and Georgia Green was 67% ( Mckinney, 2013 ). C ontrol of spotted wilt disease in peanut relies on a combination of methods which alone have little effect, but when comb ined can significantly reduce the risk of loss from spotted wilt. The factors affecting the severity of spotted wilt are peanut variety,

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21 planting date, plant population, row pattern, crop rotation, tillage, and so on. The methods involving in these factors are all combined into an integrated disease management tool for spotted wilt suppressio n (Hagan et al., 1991; Gorbet and Shokes, 1994; Brown et al., 1995; Brow n, 1999; Culbreath et al., 2003 ) Integrated disease management is an effective method to control spotted wilt and peanut cultivar is the most important factor to control the disease. Peanut Rx is an index to help growers to minimize peanut disease in the s outheastern United States (Culbreath et al., 2010) In Peanut Rx system it includes many different factors, for examples, plant variety, planting date, plant population, row pattern, crop rotation and tillage. Every factor has different range of index points and the higher point means the higher chance for disease infection. H ost resistance (peanut variety) is the most important factor to reduce disease risk. It has higher index point range (from 5 to 50) than other factors like plant ing date (0 to 30), plant population (0 to 25), row pattern (0 to 15), crop rotation (5 to 25) or tillage (0 to 15). Hence, the development of spotted wilt resistance variet ies has become a major breeding objective in peanut breeding programs in the United States Cultivars with moderate levels of field resistance have been developed (Branch, 2002, 2007, 2010; Gorbet and Shokes, 2002; Gorbet, 2007; Gorbet and Tillman, 2008, 2009; Holbrook et al., 2008) ; however all of them can suffer yield loss when disease pressure is high (Culbreath and Srinivasan, 2011 ) No peanut variety has been found to be immune to TSWV In addition, breeding for resistance to spotted wilt is hampere d by inconsistent performance of breeding lines due to seasonal variability in disease incidence and severity in the field with natural inoculation Therefore, peanut breeders

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22 need new sources of resistance and methods of identifying spotted wilt resistanc e that are independent of the environment which will and deliver cultivars with superior resistance to spotted wilt disease. Peanut Variety w ith Spotted Wilt Resistance The single most important factor in manag ement of spotted wilt is using a cultivar resistant with in the integrated program. It is highly desirable for peanut breeders to find genotypes with greater levels of resistance (Culbreath et al., 2005 ; Tillman et al., 2007) Southern Runner (Gorbet et al., 1987) was released in 1984 by the University of Florida Peanut Breeding Program and it was the first cultivar reported to show moderate level of field resi stance to TSWV (Black and Smith, 1987; Culbreath et al., 2003) Southern Runner was derived from a cross between PI 203396 and the widely grown cultivar, PI 2 03396 is a typical hypogaea botanical germplasm accession and the spotted wilt resistance characteristics can be traced back to this PI accession. Later, many other cultivars, which also provided moderate spotted wilt resistance, have been released and all of them contain PI 203396 or PI 203395 within their pedigrees (PI 203396 and PI 203395 came fr om the same original accession), f or example, UF MDR 98, C99 R, ViruGard, Georgia Green, Georgia Browne, Georgia 01R and DP 1 (Branch, 1994, 1996, 2002; Gorbet and Shokes, 2002a; b; Gorbet and Tillman, 2008) Georgia Green was a very popular cultivar and dominated the southeastern peanut market for a long time (Culbreath et al., 2003) In 2005, Georgia Green accounted for 77% certified seed acreage in Ala bama, Florida and Georgia. Under severe spotted wilt epidemics, Georgia Green showed severe susceptibility and had yield loss even though it had a m oderate level of resistance to spotted wilt (30 index points) C99 R showed better

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23 spotted wilt resistance t han Georgia Green by mechanical inoculation under controlled environmental conditions (Mandal et al., 2001) Florida 07 and Tifguard (Holbrook et al., 2008; Gorbet and Tillman, 2009) were two cultivars derived from C 99R and showed higher resistance than Georgia Green (each with 10 index points) Hence, t hey provided much more flexibility in disease management (Culb reath and Srinivasan, 2011) PI 576638 is another source of TSWV resistance and it is a hirsuta botanical type line introduced from the highlands of Mexico. In the United States before 1992, only three accessions were identif ied as hirsuta type lines in the United States National Peanut Germplasm Collection. In 1993, 18 hirsuta accessions were collected and added to the National Peanut Germplasm Collection. PI 576638 has been used in crosses and provided breeders a new source of resistance to TSWV (Barrientos Priego et al., 2002; Culbreath et al., 2005 ) It was reported that several breeding lines with PI 576638 in their pedigree have better TSWV resistance than the lines derived from PI 203396 The two PI accessions may contain different resistant genes and have different resistan ce mechanisms (Culbreath et al., 2005 ) NC94022 resulted from a cross between N91026E and PI 576638 made by Dr. Tom Isleib in North Carolina. N 91026 E was an early maturing Virginia type line and it was moderately susceptible to TSWV (Culbreath et al., 2005 ) The s potted wilt resistance of NC94022 was much greater than Georgia Green and also better than C 11 2 39 and C 11 186, which were resistant to TSWV (Culbreath et al., 2005 ) Baldessari (2008) reported that NC94022 showed the greatest spotted wilt resistance among 10 cultivars or breeding lines (F 435HO, NemaTAM, SunOleic 97R, Georgia Green, ANorden, C 99R, NC94002, DP 1, AP 3 and Georgia 02C) Due to the excellent

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24 resistan ce characteristics of NC94022, cross es utilizing NC94022 as a parental line in peanut breeding programs were been initiated (Culbreath and Srinivasan, 2011) In 2009, the University of Florida Peanut Breeding Program identified a breeding line, UFT 113 with superior s potted wilt resistance. Later, it was released as a variety named Florida EP TM ( Tillman and Gorbet, 2012 ) Florida EP TM was derived from a cross between NC94002 and ANorden (Gorbet, 2007b) Currently, no varieties are completely immune to spotted wilt (Culbreath et al., 2010) ; however, Florida EP TM displayed a p romising spotted wilt resistance in the field and better than all other resistan t varieties in peanut seed market s (Mckinney, 2013) Integrated disease management combinin g different control factors has been applied to spotted wilt management (Culbreath et al., 2003) and more resistance varieties can provide better flexibility to control the disease (Culbreath and Srinivasan, 2011) Florida EP TM has been tested under earlier planting date (April) and reduced seed density (13.1 seed per meter). Both conditions favor for spotted wilt epidemics, however, Florida EP TM prove d to have resistance sufficient to obviate th e high risk situations presented by earlier planting date and lower seed density (Mckinney, 2013) In addition to evaluating f oliar symptomology TSWV infection was conside red. Immunosstrip testing was conducted to detect virus inside the plant root crown since foliar symptomology cannot always represent disease incidence and is not always reliable, which may underestimate the actual viral amount. Florida EP TM had sig nificant ly lower infection (less than 10%) than other two cultivars, Florida 07 (44%) and Georgia Green (67%) by immunostrip testing. Other studies have reported several breeding lines

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25 also show low TSWV infection frequency but never as low as Florida EP T M (Mckinney, 2013) Compared to current popular runner type cultivars in the s outheastern United States, Florida EP TM has relatively low yield and may not be desirable to peanut growers H owever, the excellent resistance characteristics make it a great parental line for breeding new, spotted wilt resistant cultivars In addition, it can be utilized to generate a mapping population in order to develop molecular makers linked to the TSWV resistance and it can help to accelerate the process of selecting resistant lines in breeding program s The resistan ce mechanism is still unknown and molecular markers might aid in elucidat ing the genetic control of resistance in Florida EP TM Molecular Markers in Peanuts Traditional phenotypic evaluation coupled with molecular marker analysis can accelerate the selection of breeding lines, improving the success of breeding programs. Nevertheless, in spite of the economic agricultural importance, the molecular genetics and genomics research in peanut are at a beginning stage compared to other legumes, such as soybean and common bean. There are tremendous morphological and physiological differences among peanut germplasm accessions, such as seed size, hull thickness, pod yield, growth habit, maturation time and seed color However, the ability of genetic markers to detect polymorphisms in peanut is very low (Hopkins et al., 1999). Traditionally, peanut breeders utilized conventional breeding method s to select resistant plants. However, e xpression of spotted wilt disease is highly variable at different locations and from season to season making it difficult to efficiently identify resistant genotypes M olecular markers linked to spotted wilt resistance could overcome this problem and allow identification of resistant lines r egardless of seasonal conditions.

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26 Recently, a large number of genomic tools and resources were developed, which increased the potential of using molecular markers for selection to accelerate the peanut cultivar improvement (Varshney et al. 2007). Much pro gress in peanut genomics ha s been made in the past few years (Pandey et al., 2012; Varshney et al. 2013). Specifically, two ancestor al genomes, A genome from A. duranensis and B genome from A. ipaensis have been sequenced and annotated (Peanut Genomics Initiative, http://www.peanutbase.org/ ), which provided a fundamental resource for molecular marker development Recent studies have reported that the polymorphi c molecular markers which can detect the divergence in cultivated peanut derived from restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLPs) and random amplified polymorphic DNA (RAPDs) are extremely rare (Kochert e t al, 1991; Kochert et al. 1996; Subramanian et al, 2000). Co dominance, multi allele, abundant polymorphism, PCR based simple analysis, and transferability from other species are the major advantages of simple sequence repeat (SSR) markers (Ferguson et al 2004; He et al., 2003; Weber, 1990). The u tility of SSR markers in many other crops has been demonstrated (Lelley et al, 2000; Danin Poleg et al, 2001) and SSR markers can be a potential marker resource for peanut genetic studies (Gautami et al. 2012; Q in et al. 2012). Qin et al. (2012) screened 4,576 SSR markers to construct a linkage map with 324 polymorphic markers. Another linkage map was comprised of 895 SSR markers by using recombinant inbred line (RIL) and backcross population s (Gautami et al., 2 012). Single nucleotide polymorphisms (SNPs) is another type of molecular marker, which is very abundant and distributed throughout the whole genome. SNP and SSR

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27 markers can be applied together for genetic research. A genetic map with 598 SSR markers and 1054 SNP markers w as constructed and became the first high density linkage map for A. duranensis ( Nagy et al., 2012). SNPs can be detected in high throughput systems (Pandey et al., 2012) such as GoldenGate assay, infinium SNP array from Illumina Inc (San Diego, United States ), KASPar assay from KBiosciences (Hertfordshire, United Kindom ), or next generation sequencing. Linkage Map A linkage map is a genetic map show ing the position of known genes or genetic markers in relative order and distance to each other determined by recombinati on frequency. C onstruction of a genetic linkage map is important for molecular breeding, map based cloning, structural genomics and comparative genomics. It can provide the basic framework for identifying the genes and quantitative trait loci (QTLs), which are important morphological, physiological or agronomic traits. Due to the limited polymorphic marker sources and relatively complex allotetraploid structure, a few Arachis species genetic linage maps have been developed. The first genetic map was establi shed by using RFLP markers from a cross between A. stenosperma and A. cardenasii two diploid A genome species (Halward et al., 1993). The map was derived from an interspecies hybridization and 117 RFLP markers were distributed among 11 linkage groups, cov ering 1,063 centi morgans ( cM ) In 2005, Moretzsohn et al. published the first SSR based Arachis linage map, also from an interspecies hybridization between two A genome species ( A. duranensis and A. stenosperma ). The linkage map consisted of 204 polymorphic SSR markers and had 11 linkage groups, covering 1,230.89 cM of total map distance. The genetic maps from the RILs developed from cross between two cultivated peanut ( Arachis hypogaea

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28 L.) were constructed a fe w years ago (Varshney et al. 2009; Hong et al. 2010). Varshney et al. (2009) developed the first SSR based genetic linkage map for cultivated peanut. A total of 135 SSR markers were mapped into 22 linkage groups, covering 1,270.5 cM. Since the number of ma rker s was limited, there were six linkage groups with only two SSR markers. Another SSR based linkage map for cultivated peanut was constructed with 175 SSR markers in 22 linkage groups and the total length is 885.4 cM (Hong et al., 2010) Qin et al. ( 2 012) constructed an integrated genetic linkage map by combin in g previous genetic maps and two new RIL mapping populations. A total of 324 SSR markers were anchored on this integrated map covering 1,352.1 cM with 21 linkage groups. According to the integ rated map, two major QTLs for TSWV resistance were identified. Recently a consensus map was set up by using the common markers from other linkage maps. The map contained 3,694 markers and covered 2,651 cM, which included 20 linkage groups (Shirasawa et al. 2013) Quantitative Trait Loci (QTL) and Marker Assisted Selection (MAS) Quantitative trait loci (QTL) analysis based on the linkage map is critical in identifying markers linked to agronomically important traits. Several QTLs for different important trai ts have been identified in peanut, such as drought tolerance (Gautami et al., 2012; Ravi et al., 2011), disease resistance (TSWV, rust, late leaf spot, nematode, aphid vector of rosette disease, Cylindrocladium black rot and early leaf spot) (Herselman et al., 2004; Khedikar et al., 2010; Nagy et al., 2010; Qin et al., 2012; Simpson, 2001; Stalker and Mozingo, 2001; Sujay et al., 2012; Wang et al., 2013), and nutritional quality (oleic/linoleic acid and aflatoxins ( Aspergillus flavus )) (Liang et al., 20 09; Sarvamangala et al., 2011). These QTL regions are associated with specific flanking markers and had

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29 different level s of phenotypic variation explained (PVE). The associations between genetic markers and traits are the most important information to cond uct marker assisted selection (MAS) Tight linkage between markers and the genes controlling these traits can help faste n and accurately detect lines with traits of interest by genotyping using specific markers. Several studies reported that MAS and/or ma rker assisted backcrossing (MABC) have been applied to peanut cultivar development. The f irst successful MAS example in peanut happened in 1999. Root knot nematode [ Meloidogyne arenaria (Neal) Chitwood] resistance was introgressed from wild peanut species through backcross breeding method into a cultivar 'Tamrun 96'. Using restriction fragment length polymorphism (RFLP) marker s to conduct MAS and the first nematode w as developed and released (Simpson and Starr, 2001) Because COAN had low yield under a disease free environment, the same RFLP markers were used for additional two backcrossing generation s w as been released with greater yield than COAN and the same nematode resistance as present in CAON ( Simpson et al., 2003). Another example by using SSR markers to conduct MAS was reported in 2012 (Sujay et al., 2012). The nematode resistant QTL with up to 82.62% PVE had been conducted by MABC through four SSR markers. More and more QTLs have been identi fied and validated. It is believed that more makers tightly linked to traits should be developed and be utilized to conduct MAS efficiently. Heritability around a century ago. It was described as the resemblance between offspring and their parents. Heritability studies evaluate the genotypic consistency of traits from one

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30 generation to another generation (Falconer, 1960; Lynch and Walsh, 1998) The o bserved phenotype (P) is determined by two factors genotype (G) and environmental facto rs (E). The variance of the phenotype ( ) is a summation of the variance of genotype ( ) and the variance of environment ( ). Heritability is defined as a ratio of genetic variance over the total phenotypic variance There are two types of heritability. Broad sense heritability (H 2 ) is defined as the portion of total genetic variance ( ) to total observed phenotypic variance ( ). In this case, the genetic variance ( ) can be expressed as a sum of the variance of genetic additive effec ts (breeding values; ); the variance of genetic dominance effects ( ) (interactions between alleles at the same locus), and the variance of epistatic effects ( ) (interactions between alleles at different loci). The second type is narrow sense heritabilit y which refers to the portion of additive genetic variance ( ) to total phenotypic variance ( ) (Lush 1949) value of their progeny (Falconer, 1960; Lynch and Walsh, 1998)

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31 It is obvious that different environment s affect estimation of heritability. If the environmental variance is greater than the genotypic variance, heritability will be low and selection of traits based on the phonotypical performance will be less reliable. On the other hand, if the proportion of environmental variance is small in rela tion to genetic variance heritability will be high and phenotypic selection will be efficient (Briggs and Knowles, 1977) The interaction between genotype and environment ( ) is ignored in the previous equation of phenotype partition, but in practice, there is often an interaction between genotype and environment (G*E) This means that genotypes perform differently in different environments. G*E interactions are easily ignored, because they are difficult to estimate. If G*E exists, the phenotype variance partitioning formula is: The statistical methodology of parti tioning variance and estimating heritability was well developed across species (Lynch and Walsh, 1998) Heritability values will vary, depending on the method used in computation (Robinson et al., 1949) One standard method used to estimate heritability is the variance component method from an analysis of variance (Warner, 1952) This i s because the variance component method has good adaptability to different situations (Holland et al., 2003) Another method is parent offspring regression. Traditionall y, a simple experimental design with balanced data was conducted for heritability estimation, such as simple regression of offspring and parental phenotypes (Lynch and Walsh, 1998) If the design is unbalanced and complex, linear mixed model analysis is performed to estimate additive genetic effects and e nvironmental effects. The a dvantages of this method include

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32 efficiency and incorporation pedigree information. Restricted maximum likelihood (REML) is a method of variance component estimation used to fit linear mixed models and can generate an estimate of unbiased variance and covariance parameters (Gil mour et al., 1995) The most important meaning of heritability for breeders is how much genetic variability as a portion of phenotypic variability can be delivered from parent to offspring R is the ob served selection response (the difference between the phenotypic mean across generations) and S is the observed selection differential (the difference between the mean of the original population and the mean of individuals selected for breeding), h 2 is nar row sense heritability (Falconer, 1960) The equation shows that h 2 is a critical determinant in obtaining the expected response from selection. Genetic C orrelation Genetic character and environmental character are two characters affecting correlation. The genetic cause of correlation is mainly pleiotropy. Pleiotropy is property of a gene that can influence two or more phenotypic traits (Falconer, 1960) Phenotypic co rrelation includes the genetic and the environmental deviations. Genetic correlation is the correlation of breeding values and b reeding value is the summation of total additive effects (Falconer, 1960; Lynch and Walsh, 1998) Compared to phenotypic correlation, genetic correlation is more meaningful for breeders. E nvironmental correlation is the correlation of environmental deviations together with non additive genetic deviations. It is necessarily to distinguish two causes of phenotypic correlatio n (Falconer, 1960)

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33 There are several types of genetic correlation. Type A and Type B genetic correlations are the focus in this study. T ype A correlation estimates different traits measured on the same individuals (Burdon, 1977) and can be obtained through restricted maximum likelihood (REML) method using linear mixed model (Schaeffer et al., 1978) in the bivariate analysis. The genetic variance and co variance are calculated and the correlation between traits depicts the relationship between two traits. Traits culd be positively or negatively correlated. T ype B genetic correlation is conducted by measuring the same trait, on the same individual, but in different environments (Robertson, 1959; Yamada, 1962) Muti s ite analysis is utilized to obtain the genetic variance and covariance structure. Similar to t ype A correlation, t ype B can be estimated by linear mixed models (Holland et a l., 2003) The range of t ype B correlation is from zero to one and the value implies the level of G*E interaction across all environments evaluated. High t ype B correlation indicates the G*E interaction is small, therefore, breeders can possibly make se lection only based on one location and do not need to worry about potential loss of genetic gain from the other locations (Falconer, 1960; Lu et al., 2001) Breeding V alues REML coupled with linear mixed models can help to calculate the best linear unbiased prediction (BLUP) of breeding values. Breeding value is the summation of total additive effects (Falconer, 1960; Lynch and Walsh, 1998) The new approach provides more accurate estimation of breeding value based on the variance and covariance structure (Henderson, 1976; Holland et al., 20 03) When analyzing the linear mixed model, genetic effects are treated as random effects and environmental effects are treated as fixed effects. Based on the pedigree, it utilizes a complex genetic relationship structure

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34 of the population and every ind ividual has different degree of correlation with e ach other. The correlations are assumed to be caused only by additive effects (Henderson, 1976). Each individual contributes equally to the prediction of breeding value because the information from the rela tives is shared according to the geneti c relationship structure, thus achieving the optimum prediction (Panter and Allen, 1995; Resende and Barbosa, 2006) The REML/BLUP procedure has been widely conducted in different modern breeding programs. Initially, it was used mainly in animal breeding (Henderson, 1976) and later, it was used to improve tree breeding programs (White and Hodge, 1988) Now, the REML/BLUP procedure has been widely utilized in crop breeding to estimate breeding values, for examples, in maize (Bernardo, 1996) wheat (Crossa et al., 2006) soybean (Panter and Allen, 1995a; b) common bean (Chiorato et al., 2008) barley (Bauer et al., 2006) peanut (Pattee et al., 2001) sunflower (Reif et al., 2013) and strawberry (Paynter et al., 2014) to name a few It helps breeders to efficiently make selection and achieve better genetic gain s

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35 CHAPTER 2 UTILIZING IMMUNOASSAYS TO EVALUATE THE VIRAL DEVELOPMENT IN FLORIDA EP TM Introduction Cultivated Peanut ( Arachis hypogaea L.) is an annual legume and the mos t comment growing areas are semi a rid tropic and sub tropic regions in the world (Naidu et al., 1999) The United States is the fourth largest production country in the world ( F AO Statistical Databases 2015, http://faostat.fao.org/faostat/ ) and the total annual peanut production in the world were more than 42 million tons in 2013. Peanut has higher oil content (45 52%) and editable for human consumption. Peanut proteins are nutritious and are the principal so urce in some developing countries (Savage and Keenan 1994) Peanut is an allotetraploid with two genome s (genome AABB, 2n=4x=40). The genomes A and B in cultivated peanut most likely came from two wild diploid Arachis species, A. duranensis (A genome) and A. ipaensis (B genome). Spotted wilt disease is caused by Tomato spotted wilt virus (TSWV) (genus Tospo virus family Bunyaviridae ). In the United States, it can be a severe disease that significantly affects peanut ( A. hypogaea L.) production. TSWV in peanut was first reported in Brazil (Costa, 1941) Spotted wil t did no t impact peanut production in South America as severely as it has in North America. In the United States, TSWV was first reported in Texas in 1971 (Halliwell and Philley, 1974) and grew into a serious disease problem. In 1985, the yield reductions caused spotted wilt were approaching 50% in southern Texas (Black and Smith, 1987) In 1997, the production losses due to spotted wilt in Georgia were estimated to be around $40 million USD (Bertrand, 1998) The typical symptoms of spotted wilt on peanuts are yellowing, stunting, concentric ringspots, chlorosis, and necrosis of various sizes and shapes on leaflets

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36 (Culbreath et al., 2003 ) TSWV has a very wide h ost range including both dicots and monocots in at least 92 families. More than 1000 plant species including many economically important field crops such as tobacco and peanut, and vegetables such as tomato, pepper, potato, and eggplant are its hosts (Jones and Baker, 1991; Peters, 1998) TSWV is transmitted only by thrips and there are two predominant thrips vectors, namely: Tobacco thrip s ( Frank liniella fusca ) and Western flower thrip s ( Frankliniella occidentalis ) (Todd et al., 1990; Mitchell and Smith, 1993) Thrips acquire TSWV during the larval stage by feeding on infected host plants, but only transmit the virus during adult st age s TSWV particles are retained inside the vectors in a persistent manner which mean s that they can replicate their viral genomes inside the thrips (German et al., 1992) Many factors affect the severity of spotte d wilt including peanut variety, planting date, plant population, row pattern, crop rotation and tillage. No single method can effectively control the impact or severity of spotted wilt. Methods involving these major factors have been combined into an inte grated tool to manage the risk of spotted wilt in peanut (Hagan et al., 1991; Gorbet and Shokes, 1994; Brown et al., 1995; Brow n, 1999; Culbreath et al., 2003 ) Host resistance is the most important factor in managing disease risk. Hence, development of spotted wilt resistance h as become a major breeding objective in peanut breeding programs in the United States Several cultivars have been released showing moderate resistance to spotted wilt (Branch, 2002, 2007, 2010; Gorbet and Shokes, 2002; Gorbet, 2007; Gorbet and Tillman, 2008, 2009; Holbrook et al., 2008) Ho wever no peanut variety has b een found to be immune to TSWV and all varieties can suffer significant yield losses when the disease pressure is high (Culbreath and Srinivasan, 2011 )

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37 Florida EP TM is a new runner type variety released by the University of Florida Peanut Breeding Program that has superior spotted wilt resistance ( Tillman and Gorbet, 2012 ) It was derived from a cross betwee n NC94022 and ANorden. NC94022 is a breeding line with excellent field resistance to spotted wilt. This resistance was theorized to have come from PI 576638, a varietal type of peanut known as hirsuta ( A. hypogaea subsp. hypogaea var. hirsuta ) (Barrientos Priego et al., 2002) The hirsuta types might provide a special resource for spotted wilt resistance (Culbreath et al., 2005) NC94022 resulted from a cross between PI 576638 and N91026E made by Dr. Tom Isleib in North Carolina Later tests in Florida showed that NC94022 had much greater spotted wilt resist ance than the standard cultivar, Georgia Green (Culbreath et al., 2005) Florida EP TM has been tested under favorable conditions for spotted wilt epidemics i.e. earlier planting date (April) and reduced seed density (13.1 seed per meter). It showed excellent resistance to spotted wilt perhaps sufficient to obviate the high risk situations caused to susceptible cultivars, earlier planting date and lower seed density (Mckinney et al., 2013) Immunostrip test of TSWV and visual rating showed a significantly lower infection frequency (less than 10%) on both foliar symptomology and systematic infection. The other two cultivars tested, Florida 07 and Georgia Green had 44% and 67% infection frequency, respectively. O ther studies have reported several breeding lines that have lower TSWV infection, but the frequency was not as low as in Florida EP TM (Mckinney et al., 2013). Much research is focused on the virus itself, for example, the viral structure and genetics but less research on the interaction between the virus and the host. Both the mechanisms and genetics related to the spotted wilt resistance in peanut remain to be

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38 determined. The TSWV is mainly transmitted by adult viruliferous thrips, growing from larv ae, which fed on TSWV infected plants. After the infected thrips feed on the initial bud terminals (folded quadrifoliates), the presence of TSWV can be detected in the newly developed leaves. Su bsequently, the TSWV moves down the plant a nd accumulates in t he root crown. It is then transported back to young leaves leading to systemic spread (Kresta et al., 1995; Rowland et al., 2005; Murakami et al., 2006) The first spotted wilt symptoms have been observed as early as 30 days after planting (DAP) under high disease pressure and different varieties have clearly distinct responses. The disease progress is slow at the beginning, but increase s throughout the remainder of the growing season (Culbreath et al., 1992) Florida EP TM displays a significantly lower TSWV incidence than other cultivars which are considered to be field resistant to spotted wilt H owever, t he mechanism of resistance is still unknown. In order to determine the disease development process and viral movement in Florida EP TM it is necessary to record spotted wilt incidence at different time periods compared to other cultivars with varying resistance to the disease. The assessment of viral development pattern in Florida EP TM and other existing cultivars with different levels of resistance is a prerequisite to explore the mechanism of resistance. In order to address this issue, an expe riment was designed to use asymptomatic infection the im munostrip tests to detect the presence of TSWV. The objectives of this study were 1) to evaluate disease progression in Florida EP TM throughout the growing season by immunoassays, 2) to compare the disease incidence and severity among peanut varieties based on different tissue types, and 3) to

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39 gain an understanding of the mechanism controlling spotted wilt resistance in Florida EP TM Materials and Methods Experimental D esign Field experiments were conducted at the North Florida Research and Education i n 2012 and 2014. Chipola loamy sands and Orangeburg loamy sands are t wo major type s of soils at the NFREC farm. Before the experiments were performed, maize ( Zea mays L.) and cotton ( Gossypium hirsutum L.) were planted for crop rotation. The fields were managed similarly to commercial peanut production and the standard IFAS Extension recommendation ; however, no in furrow insecticide was applied in order to maximize the occurrence of spotted wilt disease The field plots were planted in mid April, which is a window of high risk for spotted wilt as earlier planting dates tend to have more severe disease pressure. Overhead center pivots provided irrigation as needed. The experimental design was a randomized complete block (RCBD) with four varieties and three replications. The experiment was condu cted in 2012 and 2014. Each plot was 1.8 m wide and 4.5 m long and had two rows of the same variety spaced 0.9 m apart The seed planting density was one seed per 0.3 m. The f our cultivars utilized were Florida EP TM ( Tillman and Gorbet, 2012 ) Florida 07 (Gorbet and Tillman, 2009) Georgia Green (Branch, 1996) and Georgia Valencia (Branch, 2001) Florida 07 was release d by University of Florida in 2006. It is a high oleic fatty acid, runner type peanut with medium late maturity and resistant to spotted wilt (Gorbet and Tillman, 2009)

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40 Georgia Green is a runner market type variety released in 1995 by the University of Georgia Coastal Plain Experimental Station (Branch 1996) At release, it had moderate resistance to spotted wilt, but it was more susceptible than Florida 07 (Culbreath et al., 2008) According to the Peanut Rx spotted wilt risk index Geo rgia Green has 30 points and Florida 07 scored 10 points (Culbreath et al., 2010) where higher points translate to an increased risk of spotted wilt. Georgia Valencia was develop ed at the University of Georgia Coastal Plain Experiment Station in 2000. It is a large podded valencia market type peanut ( A. hypogaea subsp. fastigiata var. fastigiata ) used for boiling peanut in fresh markets in the southeastern United States. It is a spotted wilt susceptible variety (Branch, 2001) Tissue C ollection Four types of tissues were collected: young leaf, ol d leaf, stem, and root crown Young leaf was collected from the first unfold ed leaf on the main stem. Ol d leaf was the last nodal position leaf which still attached to the main apex stem. Stem was the internode between the first node and the second node counted from the base. Root crown was collected under the soil surface after removing the lateral roots. Tissue collecti o n time points were 30 d ays after planting (DAP), 60, 90, 120 and prior to h arvest. Four individual plants were randomly selected from each plot and entire plants were dug up. Y oung leaves, old leaves, stems, and root crown s were collected from each plant, separately and same types of tissues from four different plants were pooled together for testing. The selected plants at each date were destructively sampled. A total of 240 samples were collected including four varieties with four tissue types in five collection time points and three replicates. All the tissues were put into fre eze drier

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41 machine to remove water and were stored under room temperature conditions with silica gel to control moisture. Immunostrip T esting Tissues (young leaf, old leaf, stem, and root crown ) from the four varieties were tested for presence of TSWV using ImmunoStrip Kits (Agdia Inc., Elkhart, IN, United States ) for a total of 240 samples The kits contained TSWV specific monoclonal antibodies as the capture reagent and are used as an on site tool to qu ickly identify virus in plants. Immunostrip Kits were stored at 4C until the testing began. Plant tissues were weighed and 0.4 grams of each sample was placed into sampling bags that contain SEB1 (sample extraction buffer1). Leaves were ground by pestle; stems and roots were crushed by hammer within the sampling bag. Then, the test strips were inserted into the bags ensuring that the strips were immersed in the SEB1/plant tissue fluid. Results were evident within 5 to 30 minutes. The strip ha d two indicati on lines. The upper line was a control line and the lower line was test line. If only the upper line (control line) displayed, no T SW V was detected (Figure 2 1); however, if two lines (control line and test line) were displayed, TSWV was detected in the sa mple (Figure 2 2). If neither line was displayed, the test was invalid. Score for the tissue was 1 if the virus was detected or 0 if no virus was detected. Statistical A nalysis Data were analyzed by using the GLIMMIX procedure of SAS 9.4 (SAS version 9.4; SAS Institute, Cary, NC) The factor of y ear, time, tissue, and variety were considered fixed effects, as were the year*time, year*tissue, year*variety, time*tissue, time*variety, tissue*variety, and time*tissue*variety interaction effect. Since the data is categorical with a binomial distribution, a generalized linear model was applied with logit

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42 function. The first analysis showed that the year and year related interactions were not significant (p>0.05), so the year factor was removed, which means two ye ars data were combined together and replication number was increased from three to six. After that, same statistical analysis was conducted again. Least squares means (LS means) were computed to evaluate the treatment mean for time, tissue, and variety eff ect. The Bonferroni method was utilized for multiple comparison adjustment and if p value was less than 0.05, treatment pairs were considered different from each other. Results Data A nalysis The analysis of variance results with p values of each main f a ctor and the interactions is shown in T able 2 1. Except year and year related interactions, other main factors ( time, tissue and variety ) and interactions ( time*tissue, time*variety, tissue*variety and time*tissue*variety ) significant ly (p<0.001) affected the presence of TSWV Two year data (2012 and 2014) were combined and all main factors and interactions were still statistically significant on affecting the TSWV presence (p<0.001) (Table 2 2). Peanut V arieties TSWV infection in Florida EP TM was consistently low across different tissue and time points with an average of 1.72% infection frequency tested by immunostrip. Georgia Valencia had the highest infection frequency with an average of 28% which is comparable to 25% in Georgia Green, and much higher than Florida 07 of 11% infection frequency The infection frequency among a ll the varieties was

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43 significantly different (p<0.001) from each other Florida EP TM had the lowest infection frequency (Figure 2 3). A ssessment D ates At 30 d ays after planting (DAP), no TSWV was detected in any of the four varieties tested. At 60 DAP, a n average of 9% samples combined across tissue types were TSWV positive Disease incidence progressed with time and reached to 12% at 90 DAP 31% at 120 DAP, and it remained near the same level (30%) at harvest date ( ~ 140 DAP). All assessment dates were significantly different (p<0.001) on TSWV infection frequency (Figure 2 4). Tissue types Root crown s showed the highest infection frequenc y 33% (p<0.001) among all tissue types and old leaves had the lowest infection frequency 4% (p<0.001). The infection frequency of young leaves (13%) and stems (14%) were intermediate to the level s between root crowns and old leaves Th ere was no signific ant difference (p >0.05) in incidence of TSWV infection between young leaves and stems (Figure 2 5). Peanut Variety and Assessment D ates Florida EP TM clearly showed a lower infection frequency as compared to the other three varieties and it was the most consistent among different assessment dates with infection frequencies below 5% for all dates. Georgia Green and Georgia Valencia had higher frequency of infection As time progressed the frequency of infection increased as well. There was no difference in infection frequency (zero) among varieties at 30 DAP (p>0.05 ) At 60 DAP, Infection frequencies of the four varieties separated into three groups, an d there was no difference in infection (p>0.05) between Florida 07 and Georgia Valencia. At 90 DAP, Georgia Green had highest infection rate followed by

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44 Georgia Valencia and the lowest infection frequency were observed in Florida 07 and Florida EP TM At 120 DAP, all the varieties had significantly different infection (p<0.001). At harvest, Florida EP TM still had the lowest TSWV infection There was no difference in infection frequency between Georgia Green and Georgia Valencia at harvest. Florid a 07 had intermediate level infection (23%) (Figure 2 6). Peanut Varieties and Tissue T ypes Florida EP TM had the lowest TSWV infection frequency on all tissue types (old leaf, young leaf, stem, and root crown) The TSWV infection in Florida EP TM was observed only on roots, and not on any leaves or stems. Georgia Green and Georgia Valencia displayed relatively higher infection frequency among all tissues. Florida 07 was at the intermediate level. On roots, all cultivars differed in infection frequency (p<0.001) and roots showed highest infection frequency among all tissue type s. On stems, there was no difference between Florida 07 and Florida EP TM (p>0.05) and between Georgia Green and Georgia Valencia (p>0.05). On young leaves, all cult ivars had different infection frequencies (p<0.001) and Florida EP TM had the lowest infection frequency. On old leaves, Florida EP TM had the lowest (p<0.001) infection frequency among all varieties and there was not significant difference betwe en the rest three cultivars (p>0.05) (Figure 2 7). Assessment Dates and Tissue T ypes Roots showed the highest infection frequency and disease severity increased with time. Old leaves showed virus infections at 120 DAP only and no infection on other assessm ent dates. At 30 DAP, no TSWV was detected on any type of tissues At 60 DAP, roots and stems had the highest infection frequency (p<0.001) followed by young leaves and old leaves. At 90 DAP, there was no difference among infection of roots, stems and

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45 young leaves (p>0.05). Old leaves showed no TSWV infection. At 120 DAP, roots had the highest infection rate (63%, p<0.001) among all tissues with three times higher than at 60 DAP (17%) and 90 DAP (21%). At harvest, all tissues had different levels of inf ection (p<0.001). Roots had the highest infection frequency 64%, (p<0.001) and old leaves had the lowest frequency (0%, p<0.001) (Figure 2 8). Peanut Varieties, Assessment Dates and Tissue T ypes Florida EP TM Virus infection was not detected at 30, 60, and 90 DAP in Florida EP TM The TSWV was detected only at120 DAP and harvest. TSWV was detected only in root crowns not in the other three types of tissues (Figure2 9). Florida 07 All tissues were TSWV fr ee at 30 DAP TSWV infection was observed at 60 DAP, but the infections were observed only i n root crowns and stem s which were similar with each other in terms of infection frequency (p>0.05). At 90 DAP, no TSWV was detected in any type of tissues. It cou ld be a sampling error, an escape from the virus and caused no infection. At 120 DAP and harvest, the infection frequency increased on both roots and young leaves (p<0.001). Stems showed no virus at 120 DAP and harvest. TSWV was detected on old leaves at 1 20 DAP, however, at harvest, no TSWV was detected in old leaves (Figure 2 10) Georgia Green At 30 DAP, no TSWV was detected in Georgia Green of all tissue types At 60 DAP, roots had the highest infection frequency (33%, p< 0.001) with no infection observed in old leaves and an infection frequency in stems and young leaves of 17%. At 90 DAP, the infection frequency on roots and young leaves was the same as at 60 DAP.

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46 However, the infection frequency of stems increased from 1 7% to 33% ( p<0.001) from 60 DAP to 90 DAP TSWV was not detected in old leaves at 90 DAP. At 120 DAP, the infection frequency in roots increased, from 33% to 100% ( p<0.001) Old leaves began to show TSWV infection at 120 DAP The infection frequency of st ems and young leaves increased from 60 DAP to harvest. At harvest, the infection frequency in roots and old leaves decreased, however, the infection frequency in young leaves and stems increased (Figure 2 11). Georgia Valencia At 30 DAP, no TSWV was detect ed At 60 DAP, 17 % of stems and roots were TSWV positive and no virus was detected in leaves. At 90 DAP, the infection frequency in roots and young leaves increased whereas the infection frequency of stems and old leaves was the same as that of 60 DAP. At 120 DAP, the infection frequency in all tissues increased. Infection in roots increased from 50% to 83% (p<0.001) from 90 DAP to 120 DAP and TSWV was present in old leaves. Stems and young leaves both had 50% infection frequency. At harvest, roots, stem a nd young leaves had the same level of TSWV infection as at 120 DAP, however, TSWV was not detected in old leaves (Figure 2 10). Discussion Disease P rogress Results of the present research assembled the disease progress described by Culbreath et al. (1992). At the beginning, disease progress was slow but the infection increased with time and appeared throughout the rest of the season. Rate of progress was found to depend on the variet y in question Since the disease pressure was low, no TSWV was det ected in any of four varieties at 30 DAP. The pathogen was first detected at 60

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47 DAP. The exact date of TSWV infection was not known and can only be estimated to be between 30 and 60 DAP. A report from Murakami et al. (2006) showed similar results, where they did not detect virus using ELISA at 30 DAP under low disease pressure in 1998 while they observed around 8% spotted wilt infection rate at 60 DAP. However, under high disease pressure in 1999, 10% plants were detected with TSWV at 30 DAP (Murakami et al., 2006). Rowland et al. (2005) also reported that there was almost no TSWV detected by ELISA at 25 DAP compared to the epidemics observed at 43 DAP Even though the assessment dates were not exactly the same as in different reports, it is still appare nt that after certain time point, TSWV incidence in the plant increases dramatically as the season progresses. In our study between 60 and 120 DAP, the infection frequency of plants jumped from 10% to 30%. The infection frequencies were also much higher a t 100 DAP (Murakami et al., 2006) and at 70 DAP (Rowland et al., 2005) compared to the previous assessment dates. In this study, the root crown was observed to have much higher TSWV frequency across all four varieties as compared to leaves and stems. It h as been reported that TSWV was easily detected in underground portions (roots) (Culbreath et al., 1991) If plants were infected early in the season, stunting was common and could result in plant death. Root systems were affecte d more by late season infection which resulted in yield loss (Culbreath et al., 1992 ; Lyerly e t al., 2002) H owever, in this research, TSWV was detected in roots at an early stage (60 DAP). We could not detect TSWV on roots of Florida EP TM until 120 DAP when the TSWV was detected on root crown s only and at a much lower frequency than all o ther cultivars.

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48 The Source of Spotted Wilt R esistance 1, SunOleic 95R, and Southern Runner) were selected and leaves and roots were separated for testing. DP 1 was a newly released cultivar with better field resistance to spotted wilt at that time (Gorbet and Tillman, 2008) and the resistance in DP 1 came from an accession, PI 203396. It is a common source of spotted wilt resistance in runner type peanuts and many varieties with moderate spotted wilt resistance have PI 203396 in their pedigr ee, including C 99R ( Gorbet and Shokes, 2002 ), Georgia 01R ( Branch, 2002 ), Florida 07 ( Gorbet and Tillman, 2009 ) and Tifguard ( Holbrook et al., 2008 ). The resistance in Florida EP TM possibly came from another accession, PI 576638. It is believed tha t the lines derived from PI 576638 have better TSWV resistance than those derived from PI 203396 (Culbreath et al., 2005) These two PI accessions may contain different resistance genes and might have different resistant mechanisms (Culbreath et a l., 2005) The spotted wilt epidemics were detected at 60 DAP in DP 1 on both tissues (leaves and roots). With increasing time, disease severity became higher and at harvest, the root infection frequency were around 75% (Murakami et al., 2006). Although the environments were different when the experiments of Florida EP TM and DP 1 were conducted, under either low or high disease pressure, DP 1 showed much higher TSWV infection frequency (75%) than Florida EP TM which had less than 20% frequency of TSWV infection. In fact, the infection frequency of DP 1 was similar to that of Florida 07 found in the present study. TSWV Infection on Different T issues In peanut, TSWV is transmitted by thrips, and these vectors prefer to feed on younger and softer plant tissues which is why thrips are commonly found in the terminals

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49 (Smith Jr and Samsgj, 1977) At early stages of plant development, the virus distribution was not uniform throughout individual plant and was concentrated mainly on the leaf terminals (Kresta et al., 1995; Hoffmann et al., 1998) After local infection from thrips feeding, the virus moves down to the roots. Then, TSWV is either accumulated in the root crowns or goes back to the growing points, causing systemic infection (Kresta et al., 1995) In the present study, only Georgia Green showed TSWV infection in three tissues: roots, stems and leaves at an early stage (60 DAP) comparted to Georgia Valencia with TSWV present in root and stem, Florida 07 with TSWV present in root only and TSWV unde tected in Florida EP TM Since the virus can be most easily detected in roots, it is believed that virus must have the ability to complete both short and long distance movements and the virus can be transported from initial infection sites to roots (Harries and Ding, 2011) Virus Movement and The Mechanism o f Spotted Wilt Resistance Short distance movement is slow, cell to cell movement and the virus needs to spread through plasmodesmata (Gunning and Overall, 1983) Long distance movement is a rapid migration aft er virus particles leave their original infection site. Long distance movement is usually by phloem, and in a few cases, by xylem (Atabekov and Dorokhov, 1984) In toba c co ( Nicotiana tabacum ), TSWV caused discrete individual spots by local infection, while viruses were transporte d to root system where they trigger systemic infection. The symptoms of systemic infection are necrosis on leaves and apical buds. In this study, the intital thrip feeding time points were around 30 to 60 DAP and after that the virus moved down to roots. T his is apparently why the three more susceptible

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50 varieties (Florida 07, Georgia Green, Georgia Valencia ) had more TSWV infection on roots, but few on leaves. It was hypothesized that peanut varieties which have greater TSWV field resistance, restrict long distance movement of the virus, thereby minimizing systemic infection (Mandal et al., 2002) The progress of spotted wilt epidemics depends on peanut genotypes (Culbreath et al., 1997; Murakami et al., 2006). Although Florida EP TM has relatively low TS WV infection (5%) compared to other genotypes, it is still not immune to TSWV. The distinct differences between Florida EP TM and other cultivars is the lack of TSWV detected in stems and leave s the reduction in the frequency of root crown infection and the delayed onset of TSWV detection. Since TSWV is transmitted by thrips, it was reported that some resistant genes were involved in restricting vector infestations and block virus spread from vectors to plants (Maule et al., 2007) One vector related resistance mechanism is non preference by thrips. This kind of plants. Thrip feeding sites were observed on Florida EP TM and were similar to other varieties, which indicated that Florida EP TM might not apply avoidance resistance mechanism However, TSWV is propagatively transmitted by persistent manner so virus can replicate within the thrips on midguts before transmitting to plants (Ul lman et al., 1997; Riley et al., 2011) Thrips with or without virus replication inside the midguts could possibly change the vector feeding preference E ven Florida EP TM can observe feeding sites similar to other varieties, since thrips population s are difficult to trace, the feeding sites were bit ten by adult viruliferous thrips or not are unknown.

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51 Another type of vector related resistance is host interference with virus transmission F or example, the tomato Mi 1 gene is a plant R gene belonging to NBS LRR group and can interfere the virus transmission from two virus vectors, potato aphid ( Macrosiphum euphorbiae ) and whitefly ( Bemisia tabaci ) (Vos et al., 1998) Florida EP TM showed a reduction in the frequency of infected plants. At harvest time, all cultivars, including the moderately resistant Florida 07 had over 60% infection in root crowns compared to less than 20% root crown infection in Florida EP TM and the vector interference could be the reason for the frequency reduction. Two TSWV resistance genes have been identified i n other crops. B oth of them are single dominant genes, Sw 5 in tomato ( Stevens et al., 1991 ) and Tsw in pepper (Boiteux and De Avila, 1994) The mechanisms of Sw 5 and Tsw are to trigger hypersensitive response (HR) and cause local lesions to block further virus movement on leaves (Black et al., 1991; Moury et al., 1997; Rosell et al., 1998) HR is a common resistance mechanism in Tospovirus and helps to terminate viral infection rapidly to prevent infection of adjacent cells from initial virus entry (Flor, 1942) H owever in peanut, the responses in resistant varieties appear to be very different. The resistant varieties did not show HR, but they delayed virus accumulation in the root c rowns and transportation from roots back to leaves (Murakami et al., 2006). Resistance in Florida EP TM Florida EP TM displayed a very slow and infection progress resulting in very low infection frequency compared to other cultivars The TSWV w as detected in other cultivars at 60 DAP, but TSWV was not detected in Florida EP TM until 120 DAP, a delay of a t least 60 days. Also, the frequency of detection was much lower in Florida EP TM Florida EP TM appears to have a unique mechanism to delay short

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52 and/ or long distance pathogen movement. There are two basic requirements to establish a systemic infection: (1) hosts have to support virus replication and (2) the virus must move through plasmodesmata/va scular vessels to other cells/organs (Cruz et al., 1998) The mechanism of resistance of Florida EP TM and the gene ( s ) responsible have not been identified The mechanism could be interference of viral genome replication or the restriction of movement or both The retarded systemic infection indicates a spotted wilt resistance charac teristic in Florida EP TM compared to other cultivars which are Since the infection frequency in all other tested genotypes is much higher than in Florida EP TM it appears that they are more tolerant of the virus rather than resistant. Florida EP TM also appears to minimize virus infection in all parts of the plant both of which result very low to no disease incidence prior to peanut harvest. Asymptom atic I nfection Although no foliar visual symptoms appeared, asymptomatic infection commonly occurs in the field (Culbreath et al., 1992; 2003). In this study, immunostrip was utilized to detect asymptomatic infection and capture the presence of TSWV. The Immunostrip technique can be easily conducted in the field and does not require specialized training. Dang et al (2009) reported no statistical difference between RT PCR (Reverse transcription polymerase chain reaction) and ELISA (enzyme linked immunosorb ent assay) results, when diagnoses were performed in the field. Two methods were comparable and similar results were reported on Prunus necrotic ring spot virus and Prune dwarf virus in almond (Mekuria et al., 2003; Dang et al., 2009) TSWV titers were estimated by RT RT PCR ( reverse transcriptase real time polymerase chain reaction) and the copy numbers of TSWV N gene, which produce

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53 nucleoprotein, were quantified (Shr estha, 2011) Several cultivars were evaluated in the greenhouse, including NC 94022, a spotted wilt resistant breeding line and the Florida EP TM Although NC94022 showed a greater TSWV N gene copy numbers than other cultivars, such as Georganic, Georgia 06G, and Tifguard, very few symptoms appeared on NC 94022. The infection frequency and titer amounts in peanut genotypes were not correlated and even resistant varieties could possibly have high TSWV titer (Shrestha, 2011) Comparison Among V arieties This study revealed that the resistance on Florida EP TM In order to deeply understand the resistant mechanism and the genes involved in resistance, artific ial inoculation should be conducted by viruliferous thrips and assessment date has to be reduced to a daily basis in order to track TSWV movement accurately. It is believed that the delayed movement of TSWV caused the difference s between resistant and susceptible plants. Immunostrip and RT PCR could only test the presence or absence of virus. RT RT PCR could quantify virus titer level in a sophisticated way and trace the virus movement specifically. Florida 07 is a spotted wilt r esistant variety; however it still had high infection incidence especially in the late season. At 30 and 90 DAP, no virus was detected, but roots and stems showed infection at 60 DAP. Since sampling was destructive, different samples were collected at eac h time point. At 60 DAP, the positive immunostrip result indicated at least one of the 12 samples contained TSWV, but none had TSWV at 90 DAP. This suggests that, at an early stage, Florida 07 can be infected. Compared to other susceptible cultivars (Geor gia Green and Georgia Valencia), Florida 07 has better field resistance to spotted wilt, but because a high percentage of plants are infect ed the

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54 resistance may not be stable, meaning that it can be overcome in situations where the disease is severe prese nce After thrips feed on leaves, the virus moves to and accumulates in roots. R oots have highest infection frequency of any tissue types (Kresta et al., 1995; Mandal et al., 2002). Infections in young leaves and stems indicate systemic infection and viru s movement back to the growing points from roots. Young leaves were near the growing points and stems were collected on the main stem, close to the ground. TSWV w as not detected on old leaves at 30, 60, 90 DAP and harvest, but it was detected at 120 DAP, o n Florida 07, Georgia Green, and Georgia Valencia. The reason is not clear, but environmental conditions could affect resistance mechanism. Destructive Sampl ing by Immunostrip Immunostrip is a more sensitive and accurate measuring method than directly to evaluate the disease severity according to visible foliar symptoms Immunostrip has the ability to detect asymptomatic infection (Culbreath et al., 2005) however, the experiment conducted by i mmunostrip is more expensive. Also, the sampling is destruct ive, so if the experiment is condu cted using root crown tissues in early season, the yield will decrease in late season because of the loss of plants through immunostrip testing. Compared to other tissues, root crown can detect more viruses, but it is the unique organ and cannot be replaced. In this study, Florida EP TM can only detect virus on root crown and other tissue types (young leaves, old leaves and stems) were all virus free. At harvest, in young leaves, Florida 07 was 22% infection and both Georgia Green and Georgia Valencia were 50%. Using young leaves as major measuing tissues by immunostrip testing is a good alternative method to maintain plants alive and still keep the accurancy of detecting a sy mptomatic infection.

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55 Temperature and Physiological F unction Temperature plays an important role i n disease development progression. In peanut, different temperature ranges were shown to affect spotted wilt disea se severity and result in either local infection or systemic infection (Mandal et al., 2002). In tobacco, development of TSWV is affected by temperature such that it may favor translocation or replication depending on the temperature (Hull, 1989; Llamas Llamas et al., 1998) Spotted wilt infection caused different physiological responses among different cultivars, for example, higher photosynthesis, transpiration, and water efficiency were observed in resistant cultivars. This indicated that resistant cultivars could maintain high physiological function (Rowland et al., 2005). The physiological function in Florida EP TM should be evaluated to see if it has the ability to maintain its apparent resistan ce mechanism under different conditions. Conclusion The infection frequency of TSWV was low during the early season, but increased with time. The TSWV infection frequency in Florida EP TM 18 % compared to over 60% for other cultivars. It has apparently derived its resistance from an accession, PI 576638. The immunostrip method can detect the presence of the virus in plants with visible foliar symptoms and i n those that are asymptomatic. Compared to other varieties TSWV infection of Florida EP TM was observed only later in the season and only in the root tissues. The resistance in Florida EP TM might not be related to HR and non preference of thrips The interference of virus transmission (vector mediated resistance) might be one of the possible resistance mechanisms, since there w as an 80 % point r eduction in frequency of infected plants.

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56 Systemic infection can be observed in less than 20 % of tissue samples of Florida EP TM Additionally, short or/and long distance movement was apparently delayed and helped to minimize virus infection in root crowns and other tissues Compared to other varieties, virus detection was delayed by at least 120 days in Florida EP TM This characteristic is more similar to resistance and helps plants to maintain normal functions under disease pressure until harvest. Further investigation of host viral interaction could provide deep insights. Breeding for re sistance to spotted wilt is hampered by seasonal variability in disease incidence and severity and would be a good candidate for marker assisted selection, the subject of Chapter 3 herein. Other molecular techniques like RT RT PCR can be applied to trace v irus movement and quantity more accurately in Florida EP TM Florida EP TM has a novel resistance to spotted wilt not found in other cultivars and can red uce disease impact and become another resistance source for breeders to develop new, spotted wilt resistant cultivars.

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57 Table 2 1. Partial analysis of variance results for spotted wilt of peanut assessed on four peanut varieties in four tissue types over five assessment dates during 2012 and 2014 at NFREC, FL Effect Degree of Freedom F Value Pr > F year 1 0.1 2 0.7297 assessment date 4 503.93 <. 0001 tissue type 3 140.89 <. 0001 peanut variety 3 218.12 <. 0001 year*date 4 0.65 0.6301 year*tissue 3 0.99 0.3954 year*variety 3 2.34 0.0734 tissue*variety 9 40.22 <. 0001 date*tissue 12 71.53 <. 0001 date*variety 12 121.21 <. 0001 date*tissue*variety 36 37.53 <. 0001 Table 2 2. Partial analysis of variance results for spotted wilt of peanut assessed on four peanut varieties in four tissue types over five assessm ent dates at NFREC, FL Effect Degree of Freedom F Value Pr > F assessment date 4 4527.18 <.0001 tissue type 3 503.25 <.0001 peanut variety 3 2570.56 <.0001 tissue*variety 9 79.02 <.0001 date*tissue 12 196.87 <.0001 date*variety 12 163.16 <.0001 date*tissue*variety 36 44.70 <.0001

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58 Figure 2 1. Three prefilled Agdia sample bags with three negative (single red line) results for TSWV. Photo taken by Yu Chien Tseng. Figure 2 2. Three prefilled Agdia sample bags with three positive (two red line) results for TSWV. Photo taken by Yu Chien Tseng.

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59 Figure 2 3. Incidence of TSWV infection as determined by immuostrip testing from four peanut varietie s at NFREC in 2012 and 2014. Different letters ind icate d different groups (p <0.05) Figure 2 4. Incidence of TSWV infection results by immuostrip testing from five assessment dates at NFREC in 2012 and 2014. Different letters ind icated different groups (p <0.05) a b c d 0% 5% 10% 15% 20% 25% 30% 35% Florida-EP-113 Florida-07 Georgia Green Georgia Valencia Incidence of TSWV infection Peanut variety a b c e d 0% 5% 10% 15% 20% 25% 30% 35% 30 60 90 120 harvest Incidence of TSWV infection Days after Planting

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60 Figure 2 5. Incidence of TSWV infection resul ts by immuostrip testing from four tissue types at NFREC in 2012 and 2014. Different letters ind icated different groups (p <0.05) Figure 2 6. TSWV infection detected by immunostrip at d ifferent assessment dates from four different peanut varieties planted at NFREC in 2012 and 2014. Asterisks indicated significant difference (p <0.05) at each assessment date a b b c 0% 5% 10% 15% 20% 25% 30% 35% Old Leaf Young Leaf Stem Root Crown Incidence of TSWV infection Plant part * * 0% 10% 20% 30% 40% 50% 60% 70% 30 60 90 120 harvest Incidence of TSWV infection Days after planting Florida-07 Floirda-EP-113 Georgia Green Georgia Valencia

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61 Figure 2 7: TSWV infection detected by immunostrip at different tissue types from four different peanut varieties planted at NFREC in 2012 and 2014. Asterisks indicated significant difference (p <0.05) at each tissue types Figure 2 8. TSWV infection detected by immunostrip at d ifferent assessment dates from four different tissue types planted at NFREC in 2012 and 2014. Asterisks indicated significant difference (p <0.05) at each assessment date * * 0% 10% 20% 30% 40% 50% 60% 70% Old Leaf Young Leaf Stem Root Crown Incidence of TSWV infection Plant part Florida-07 Floirda-EP-113 Georgia Green Georgia Valencia * * 0% 10% 20% 30% 40% 50% 60% 70% 30 60 90 120 harvest Incidence of TSWV infection Day after planting Old Leaf Young Leaf Stem Root Crown

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62 Figure 2 9. TSWV infection detected by immunostrip in Florida EP TM at d ifferent assessment dates from four different tissue types planted at NFREC in 2012 and 2014. Asterisks indicated significant difference (p <0.05) at each assessment date Figure 2 10. TSWV infection detected by immunostrip in Florida 07 at d ifferent assessment dates from four different tissue types planted at NFREC in 2012 and 2014. Asterisks indicated significant d ifference (p <0.05) at each assessment date * 0% 20% 40% 60% 80% 100% 30 60 90 120 harvest Incidence of TSWV infection Day after planting Old Leaf Young Leaf Stem Root Crown * 0% 20% 40% 60% 80% 100% 30 60 90 120 harvest Incidence of TSWV infection Days after planting Old Leaf Young Leaf Stem Root Crown

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63 Figure 2 11. TSWV infection detected by immunostrip in Georgia Green at d ifferent assessment dates from four different tissue types planted at NFREC in 2012 and 2014. Asterisks indicated significant difference ( p <0.05) at each assessment date Figure 2 12. TSWV infection detected by immunostrip in Georgia Valencia at d ifferent assessment dates from four different tissue types planted at NFREC in 2012 and 2014. Asterisks indicated significant difference (p <0.05) at each assessment date * * 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 30 60 90 120 harvest Incidence of TSWV infection Days after planting Old Leaf Young Leaf Stem Root Crown * * 0% 20% 40% 60% 80% 100% 30 60 90 120 harvest Incidence of TSWV infection Days after planting Old Leaf Young Leaf Stem Root Crown

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64 CHAPTER 3 MAP PING GENES CONTROLLING SPOTTED WILT RESISTANCE IN PEANUT CULTIVAR FLORIDA EP TM DERIVED POPULATIONS Introduction Cultivated Peanut ( Arachis hypogaea L.) is a member of the legume family and is cultivated mainly in semi arid tropic and sub tropic areas in the world (Naidu et al., 1999) The four largest peanut produc ing countries are China India, Nigeria and the United States ( FAO Statistical Databases 2015, http://faostat.fao.org/faostat/ ). More than 42 million tons of peanuts were produc ed in 2013 Peanut seed ha ve high con centration of oil (45 52%) and protein (about 25%) which are the principal source in some developing countr ies (Savage and Keenan 1994) Cultivated peanut is an allotetraploid crop (genome AABB, 2n=4x=40), although al most all the wild Arachis species, Arachis cardenasii A. diogoi and A. batizocoi for example are diploid (2n=20). The Arachis species originates from South America within the range of Brazil, Bolivia, Paraguay, Argentina and Uruguay (Valls and Simpson, 1994) The genomes A and B in cultivated peanut most likely came from two wild diploid Arachis species, A. duranensis (A genome) and A. ipaensis (B genome). The hybridization of the two wild diploid species followed by spontaneo us chromosome duplication resulted in the isolation of cultivated peanut from other wild species (Kochert et al., 1991) Peanut production is significantly affected by spotted wilt disease caused by Tomato spotted wilt virus (TSWV) (genus Tospovirus family Bunyaviridae ), specifically in the United States. In 1985, the yield reductions caused spotted wilt were approaching 50% in southern Texas (Black and Smith, 1987) In 1997, the production losses due to spotted wilt in Georgia were estimated to be around $40 million USD (Bertrand, 1998) The typica l symptoms of spotted wilt on peanuts are yellowing, stunting, concentric ringspots, chlorosis, and necrosis of various sizes and shapes on leaflets (Culbreath et al., 2003) TSWV has a very wide host range including

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65 both dicots and monocots in at least 92 families (Jones and Baker, 1991; Peters, 1998) TSWV is transmitted only by thrips and there are two predominant thrip vectors, namely, tobacco thrip s ( Frankliniella fusca ) and western flower thrip s ( Frankliniella occidentalis ) (Todd et al., 1990; Mi tchell and Smith, 1993) Many factors affect the severity of spotted wilt including peanut variety, planting date, plant population, row pattern, crop rotation and tillage. No single method can effectively control the impact or severity of spotted wilt disease Methods involving these major factors have been combined into an integrated tool to manage the risk of spotted wilt in peanut (Hagan et al., 1991; Gorbet and Shokes, 1994; Brown et al., 1995; Brown, 1999; Culbreath et al., 2003) Integrated disease management is an effective method to control spotted wilt and peanut cultivar is the most important factor. Peanut Rx is an index table to help growers to minimize peanut disease in the southeastern United States (Culbreath et al., 2010) In Peanut Rx system it includes many different factors, for examples, plant variety, planting date, plant population, row pattern, crop rotation and tillage. Every factor has different range of index points and the higher point means the higher chance for disease infection. H ost resistance (peanut variety) is the most important factor to reduce disease risk. It has higher index point range (from 5 to 50) than other factors like planting date (0 to 30), plant population (0 to 25), row pattern (0 to 15), crop rotation (5 to 25) or tillage (0 to 15). Hence, the development of spotted wilt resistance varieties has become a major breeding objective in peanut breeding programs in the United States Traditionally, peanut breeders utilized conventional breeding method s to select resi stant plants. However, e xpression of spotted wilt disease is highly variable from season to season reducing selection efficien cy The molecular markers linked to spotted wilt resistance could

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66 overcome this problem and allow detecti o n of resistant lines reg ardless of seasonal conditions. Recently, gene t ic and genomic tools and resources were developed for peanut which increased the potential of using molecular markers for selection to accelerate the peanut cultivar improvement ( Varshney et al 2007). M uch p rogress ha s been made in this area the past few years (Pandey et al., 2012; Varshney et al. 2013). Specifically, two ancestor genomes, A genome from A. duranensis and B genome from A. ipaensis have been sequenced and annotated (Peanut Genomics Initiative, http://www.peanutbase.org/ ), which provided a fundamental resource for molecular marker development Simple sequence repeat (SSR) markers are a valu able marker type for diversity evaluation, linkage analysis, gene mapping, comparative genomics, and many other genetic studies. Their abundance in the genome, co dominance, multi ple allele s high polymorphism, PCR based simple analysis, and transferabilit y from other species made SSR markers a great choice (Ferguson et al., 2004; He et al., 2003; Weber et al., 1990) for peanut genetic studies (Gautami et al. 2012; Qin et al. 2012). Single nucleotide polymorphisms (SNPs) is another type of molecular marke r, which is very abundant and distributed throughout the whole genome. SNPs can be detected in high throughput systems (Nagy et al., 2012; Pandey et al., 2012) such as GoldenGate assay, infinium SNP array from Illumina Inc (San Diego, CA), KASPar assay from KBiosciences (Hertfordshire, UK), or next generation sequencing. Linkage map is a genetics map indicating the positions of known genes or genetic markers, which provides the basic framework for genetic and genomics studies, such as quantitative trait locus (QTL) analysis, marker assisted selection (MAS), comparative genomics, a nd genome assembly. The first genetic map of peanut was established for A genome by using

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67 RFLP markers (Halward et al., 1993). The map was derived from an interspecies hybridization and 117 RFLP markers were distributed among 11 linkage groups, covering 1,063 cM. The first SSR based Arachis linkage map was also for A genome, which included 204 polymorphic SSR markers and had 11 linkage groups, covering 1,230.89 cM of t otal map distance (Moretzsohn et al. 2005). Qin et al. (2012) screened 4,576 SSR markers to construct a linkage map with 324 polymorphic markers. Another linkage map was comprised of 895 SSR markers by using recombinant inbred line (RIL) and backcross popu lation (Gautami et al., 2012). A consensus map was constructed by using the common markers from other linkage maps which contained 3,694 markers covered 2,651 cM, and included a total of 20 linkage groups (Shirasawa et al. 2013) QTL analysis based on t he linkage map is critical in identifying markers linked to agronomically important traits. Several QTLs for important traits have been identified in peanut, such as drought tolerance (Gautami et al., 2012; Ravi et al., 2011), disease resistance (TSWV, rus t, late leaf spot, nematode, aphid vector of rosette disease, Cylindrocladium black rot and early leaf spot) (Herselman et al., 2004; Khedikar et al., 2010; Nagy et al., 2010; Qin et al., 2012; Simpson, 2001; Stalker and Mozingo, 2001; Sujay et al., 2012; Wang et al., 2013), and nutritional quality (oleic/linoleic acid and aflatoxins ( Aspergillus flavus )) (Liang et al., 2009; Sarvamangala et al., 2011). Some of the QTLs with high phenotypic variation explained (PVE) were utilized for MAS. For example, the n ematode resistant QTL mentioned above with up to 82.62% PVE (Sujay et al., 2012) had been used in MAS through four SSR markers. Florida EP TM is a new runner type variety released by the University of Florida Peanut Breeding Program and it has superior spotted wilt resistance ( Tillman and Gorbet, 2012 ) Florida EP TM was derived from a cross between NC94022 and ANorden. NC94022 is a

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68 breeding line with excellent field resistance to spotted wilt. This resistance was traced back to PI 576638, a botanical type of peanut known as hirsuta ( A. hypogaea subsp. hypogaea var. hirsuta ) (Barrientos Priego et al ., 2002; Culbreath et al., 2005 ) Florida EP TM has been tested under favorable conditions for spotted wilt epidemics i.e. earlier planting date (April) and reduced seed density (13.1 seed per meter). It showed excellent resistance to spotted wilt and had a significantly lower infection frequency (less than 10%) on both foliar symptomology and systematic infection than t he other cultivars Florida 07 (44%) and Georgia Green (67%) revealed by Immunostrip detection ( Mckinney, 2013 ) Therefore, Florida EP TM is considered to be a promising resource for resistance to spotted wilt. I t is also a good genotype to develop us eful molecular marker s linked to spotted wilt resistance. Spotted wilt resistance is a good candidate tra it for MAS, because the phenotyp e in the field is strongly affected by seasonal variability, different locations, years and measurement methods. If the major QTLs of s potted wilt resistance in Flori da EP uncertain environment impacts. Though two major QTLs for TSWV resistance were identified in peanut cultivar, NC94022 previously (Qin et al., 2012) whether the genetic basis of the resistance in Florida EP TM i s the same as that in NC94022 i s uncertain. The objectives of this study were : 1) to phenotypically evaluate the spotted wilt in a segregating population at two different environments in three years 2) to map the QTL controlling spotted wilt resistance in Florida EP TM Materials and Methods Plant Material and Experimental Design A F 2 population was originally derived from the cross between Florida EP TM Georgia Valencia made in 2009. Florida EP TM type

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69 peanut ( A. hypogaea L. subsp. hypogaea var. hypogaea botanical type Virginia ) developed by the University of Florida Peanut Breeding Program in 2012 with excellent resistance to spotted wilt ( Tillman and Gorbet, 2 012 ) Georgia Valencia was developed at the Coastal Plain Experiment Station of University of Georgia in 2000. It is a large podded Valencia market type peanut ( A. hypogaea subsp. fastigiata var. fastigiata ) used for boil ing in fresh markets in the sout heastern United States; however, it is susceptible to spotted wilt (Branch, 2001) The F 2 segregat ing population of 200 lines was planted in the NFREC in 2011. All F 2 plants were self pollinated to generate F 2:3 (F 2 derived in F 3 ) families in 2012 and allowed to self pollinating to generate F 2:4 (F 2 derived in F 4 ) families in 2013 and F 2:5 (F 2 derived in F 5 ) families in 2014 All the F 2 F 2:3 F 2:4 and F 2:5 w ere included in the research. Phenotypic evaluations were conducted in two locations, the North Florida Research and the Plant Science soil type of the NFREC is Chipola loamy sand and Orangeburg loamy sand. The test sites of NFREC were previously planted with maize ( Zea mays L.) and cotton ( Gossypium hirsutum L.) for crop rotation. As at PSREU the soil type is Arrendondo sand and Orangeburg loamy sand. Bahiagrass ( Paspalum notatum ) was planted for three year crop rotation prior to planting peanut at the test sites of PSREU. Plant s at the two locations were maintained using commercial peanut cultural practices and the standard UF/IFAS Extension recommendations. Irrigation was operated by overhead center pivots. In order to maximize the potential for severe spotted wilt epidemics, t himet insecticide was not applied, the planting date was earli er than recommended for minimizing spotted wilt and the seeding density was one plant per foot of row. These practices encouraged

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70 s potted wilt development and are opposite to the e xtension reco mmendations for farmers to minimize risks of spotted wilt (Culbreath et al., 2010) For each F 2:3 family, one plot was planted in both locations since i n s ufficient seeds were available for two replications in two locations Planting occurred in the PSREU in early April and at NFREC in the middle of April 2012. An augmented experimental design with two parental lines as controls was used in each location. Each plot had two rows, 0.9 m wide and 4. 5 m long, and a single family was planted in each plot. Seed planting density was one seed per 0.3 m. A total of 189 F 2 :3 lines were available for planting in 2012 along with 21 check plots, for a total of 210 plots. The plants from each F 2:3 family were bulk harvested and 128 seeds were randomly picked to plant the F 2:4 families. During harvest of the F 2:3 seed s from different lines were mix ed by accident and thus were removed for F 2:4 families development. As a result, only 163 F 2:4 families remained The experimental design in F 2:4 families was a randomized completed block design (RCBD) with two blocks and planted in two location s NFREC and PSREU. Each replication was an augmented design with the two parental lines spaced throughout the replication as controls. E ach replication had 163 plots plus 29 check plots, for a total of 384 plots. E ach plot had two rows spaced 0.9 m apart with a length of 4.5 m. The planting density was one seed per 0.3 m. Harvest of the F 2:4 families and subsequent planting of the F 2:5 were similar to that described for the F 2:3 The experimental design for the F 2:5 was the same as the F 2:4 but was planted only in the NFREC location. There were 384 plots with check plots and the plot size which was the same as generations F 2:3 and F 2:4. (Table 3 1).

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71 A single plant was harvested from e ach F 2:5 families and 32 seeds were randomly selected to plant the F 6 generation as RILs with a high level of homozygosity. Rating for Disease Resistance Two different disease evaluation methods to assess the severity of spotted wilt were conducted. One was a visual rating on a scale ranging from 1 to 10, and t he other one was a form of immunoassay (immunostrip testing), which was used to test for the presence of TSWV in the root crown A visual rating was conducted on a whole plot basis prior to digging. Each plot was assessed for typical symptoms of spotted w ilt such as stunting and foliar symptoms of ringspot, leaf necrosis, and chlorosis (yellowing) (Culbreath et al., 2003) The 1 to 10 scale represented a percentage of disease severity. In this experiment, 1 = 0%, 1.5 = symptoms observed with less than 10% infection 2 = 11 20% infection 3 = 21 30% infection 4 = 31 40% infection 5 = 41 50% infection 6 = 51 60% infection 7 = 61 70% infect ion 8 = 71 80% infection 9 = 81 90% infection and 10 = 91 100% infection The immunostrip testing was conducted by using the ImmunoStrip Kit (Agdia Inc., Elkhart, IN, United States ). The kit uses a TSWV specific monoclonal antibody as the capture reag ent and has been used as an on site tool to quickly identify presence of virus in plants. Ten individual plants were randomly selected from each plot and root crowns of each plant were collected and air dried after digging. Approximately 0.4 grams root cr own sample was trimmed and placed into the sampling bag that contained SEB1 (sample extraction buffer 1). The root crown samples were crushed within the sampling bags using a hammer. Then, the test strips were inserted into the bags to ensure that the stri ps were immersed in the SEB1/root crown fluid. Results were evident within 5 to 30 minutes. Each s trip had two indication lines: t he upper line was the control line and the lower line was the test line. If only the upper line (control line) displayed, no T SW V was detected (Figure 3 1); however, if both lines (control line and test line)

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72 displayed, TSWV was detected in the sample (Figure 3 2). If neither line displayed, the test was invalid and was repeated. Based on the presence or absence of TSWV, scoring was given as 1 for presence and 0 for absence. Ten plants were randomly selected from each plot and subjected to the immunostrip test to represent the average TSWV infection frequency of each plot, thus e ach plot had the possibility of TSWV infection perce ntage ranging from 0 to 100%. The visual disease evaluation method was conducted in F 2:3 F 2:4 and F 2:5 families at both NFREC and PSREU however, immunostrip testing was only utilized in F 2:3 and F 2:4 generations at PSREU (Table 3 2). Phenotypic correlations were analyzed using SPSS 22.0 to calculate Spearman's rank correlation coefficient (Spearman's rho). SSR G enotyping The genomic DNA of each sample was extracted from approximately 500 mg young leaf tissue using the modified method described by Dellaporta et al. ( 1983) Polyvinylpyrrolidone (PVP) was ad ded to remove phenolic compound s DNA quality and quantity were evaluated by 1% agarose gel electrophoresis and NanoDrop (Thermo Scientific, United States ). The isolated DNA was diluted to 5 to 30 ng /ul for further polymerase chain reaction (PCR) process. Public ly available SSR markers were selected based on related literature (He et al., 2003; Ferguson et al., 2004; Tang Wang et al., 2007; Proite et al., 2007; Cuc et al. 2008; Bertioli et al., 2009; Liang et al., 2009; Nagy et al., 2010; Qin et al., 2012; Gautami et al., 2012; Koilkonda et al., 2012; Macedo et al., 2012; Shirasawa et al., 2013; Wang et al., 2013) (Table 3 10). Primers were synthesized by Invitrogen, L ife Technologies. The linkage group information was collected from literature (Gautami et al. 2012; Guo et al. 2012; Khedikar et al. 2010; Nagy et al. 2010; Qin et al. 2012; Ravi et al. 2011; Sujay et al. 2012; Shrisawa et al. 2013) and the physical positions were obtained by aligning the primer sequences to two Arachis reference genomes, A

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73 ( A. duranensis ) and B genomes ( A. ipaensis ) using Bowtie (Langmead and Salzberg, 2012) with paired end alignment. of forward and reverse primer s deionized water. The PCR program was operated using the touchdown program with an initial denaturation at 94 C for 3 min; 10 cycles of amplification at 94 C for 30 s, 6 5 to 55 C for 20 s (every cycle dro ps one degree until 55 C), 72 C for 40 s; 30 cycles of amplification at 94 C for 30 s, 55 C for 20 s, 72 C for 40 s; and a final extension at 72 C for 7 min. The PCR products were separated on 6% non denatured polyacrylamide gel electrophoresis (PAGE) unde r 150 voltage for 2 hours in 1X TBE buffer with DYCZ 30B gel rigs system (Beijing, China) (Fountain et al., 2011) The gels were stained by ethidium bromide and visualized under UV light. SNP Markers Development and V alidation A total of 27,380,147 Illumina reads (150 bp) were generated for Florida EP TM in Dr. Jianp ing A total of 78,890 454 sequences from Georgia Valencia were obtained from the NCBI public database (http://www.ncbi.nlm.nih.gov/sra/SRX02211). For SNP discovery, the data sets were trimmed with Trimmomatic (Bolger et al., 2014) and aligned to two peanut ancestor reference genomes (A and B genomes) using default setting s of BWA MEM (Li, 2013) The SNPs were called using Samtools ( Li et al. 2009 ). For SNP validation, primers were designed by Primer3 (http://pgrc.ipk gatersleben.de/misa/primer3.html) with the setti ngs (optimal amplicon size = 600bp, Tm = 60C, optimal primer size =20 bp) to amplify the sequence region containing targeted SNP. The primer

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74 location was controlled to be 200 bp away from the SNP location to make the target SNP located in the middle of th e PCR product Primers were synthesized by Invitrogen, Life Technologies. PCR was conducted by utilizing Phusion High Fidelity DNA polymerase (New England Biolabs, Inc.) follow ing s : initial denatu ration at 95C for 1 min; 5 cycles of amplification at 95C for 45 s, 68C for 45 s, 72C for 40 s; 5 cycles of amplification at 95C for 45 s, 65C for 45 s, 72C for 40 s; 5 cycles of amplification at 95C for 45 s, 60C for 45 s, 72C for 40 s; 5 cycles of amplification at 95C for 45 s, 55C for 45 s, 72C for 40 s; and final extension at 72C for 7 min. The resulting PCR products were purified using Qiagen minElute PCR purification kit (Qiagen, Velancia, CA). After purification, the PCR products were s equenced using the Sanger technology at the Interdisciplinary Center for Biotechnology Research (ICBR), University of Florida. Linkage and QTL Analysis Linkage analysis was performed using software QTL IciMapping V4.0 (Wang et al., 2012) in combination with JoinMap 3.0 (Van Ooijen, 2006) To construct linkage groups, a minimum log of odds (LOD) threshold of 3.0 was applied and map distances were converted to centi morgans (cM) using Kosambi mapping function (Kosambi, 1943) The three year phenotyping data (Table 3 2) were incorporated with linkage map information for QTL mapping using software QTL IciMapping V4.0 (Wang et al., 2012) The Inclusive Composite Interval Mapping (ICIM ADD) method (Von Bargen et al., 2001; Li et al., 2007) was applied. The mapping parameters were a minimum 3.0 LOD threshold and the probability in stepwise regression was 0.001. The scanning interval was every 1.0 cM as a step.

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75 Results Disease Rating Distribution in the Segregation P opulation s Out of 163 F 2:3 plots, more than 40% (68) plots at PSREU, 2012 had a disease rating of symptoms. Only 25 plots showed more than 20% disease symptoms (disease rating more than 2) (Figure 3 3). The t wo checks (two parental lines) showed a distinct difference at PSREU 2012. Eight out of ten Florida EP TM plots did not show symptoms, but all Georgia Valencia showed various disease symptoms with ratings ranging from 2 to 8 (Figure 3 4). Historically, the disease pressure at NFREC is higher than at PSREU. C ompared to the rating data collected from PSREU, NF REC had more plots with rating s of 1.5 and less plots with rating s of 1 (Figure 3 5). Florida EP TM checks were mostly in rating 1, while Georgia Valencia had a wide rating range (Figure 3 6). In general, the spotted wilt epidemics detected by visual rating was higher at NFREC than at PSREU The distribution of Immunostrip testing results was quite different from that of visual rating by showing a flat shape in contrast to the skewed distribution in visual rating (Figure 3 7). The disease rating distr ibution of Florida EP TM 3 8). The distribution of visual rating s of the F 2:4 population at PSREU, 2013 also skewed to ward low disease rating. Out of 132 plots, 60 plots were scored 1.5; 34 plots were scored 1, and 38 plots were scored 2 (Figure 3 9). Florida EP TM received all ratings below 2; however, Georgia Valencia also received many rating scores below 2 (Figure 3 10). At NFREC in 2013 the visual rating r esult ed in most plots being s cored 2 (45 plots), followed by rating score of 3 (38 plots) (Figure 3 11). Only one plot was free of visual symptom s whereas more plots had a high rating score (>3) (Figure 3 11). Georgia Valencia plots had a wide range of disease rating rang ing from 1 t o 9 indicating the uneven infection or disease development in the field (Figure 3

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76 12). In general, the disease pressure was higher in 2013 than in 2012, and the disease rating was higher at NFREC than at PSREU. The distribution of immunostrip test results of the population was nearly a normal with a majority of plots falling between 0.4 and 0.7 (Figure 3 13). As for the disease severity distribution of the two checks, it was bi modal, which represented their resistant and susceptible feature respectively (Fi gure 3 14). The F 2:5 was only rated visually at NFREC, 2014. More plots (38, 35, and 33 plots, respectively) receiving rating scores of 2, 3 and 4 than othe r lower or higher rating scores (Figure 3 15). Most plots of Florida EP TM received rating sco res of 2 and lower, and the rating scores of Georgia Valencia plots were all higher than 2 (Figure 3 16). Phenotypic Correlation A t otal of seven phenotypic datasets representing different years, locations, generation s of the population and measurement methods were recorded. The seven datasets were entitled as 2012NF VR (2012: year 2012; NF: NFREC; VR: visual rating), 2013NF VR (2013: year 2013; NF: NFREC; VR: visual rating), 2014NF VR (2014: year 2014; NF: NFREC; VR: visual rating), 201 2PS VR (2012: year 2012; PS: PSREU VR: visual rating), 2013PS VR (2013: year 2013; PS: PSREU; VR: visual rating), 2012NF IS (2012: year 2012; NF: NFREC; IS: immunostrip), and 2013NF IS (2013: year 2013; NF: NFREC; IS: immunostrip). There were a total of 21 possible combinations among seven datasets. The correlations of all the combinations were significant, except the combination between 2012NF VR and 2013PS VR (Table 3 3). The highest correlation was found between 2013 NF IS and 2013NF VR with a coefficien t of 0.78 (p<0.01) The second highest correlation coefficient was 0 .59 (p<0.01) between 2012NF IS and 2012NF VR. In general, t he dataset s from the same location tended to have higher correlations. Even though 2012NF IS and 2013NF IS were datasets of different

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77 years and generations they were still correlated with a coefficient of 0.498 (p<0.01) However, 2012NF IS and 2013NF VR were also correla ted with a coefficient of 0.503 (p<0.01) but they were datasets from different years and using different measurement methods. Overall, the majority correlations had coefficients in a range between 0.2 and 0.4. While comparing the correlations within the y ear of 2013 (0.781, 0.401, and 0.316 p<0.01 ) and within the year of 2012 (0.591, 0.270, and 0.266 p<0.01 ), the correlations were higher in 2013 than in 2012. Within the correlations between 2012 and 2013 at the same measurement method (visual rating), t he dataset generated from NFREC had greater correlation coefficient (0.355 p<0.01 ) than that from PSREU (0.236 p<0.01 ). While comparing the correlations between 2012 and 2013 at the same location (NFREC), the correlation coefficient of the IS (0.498 p<0 .01 ) was higher than the VR (0.355 p<0.01 ). SSR Marker Screening A total of 2,431 markers across the whole peanut genomes (Table 3 4) have been screened between the two parental lines, Florida EP TM and Georgia Valencia. E ach linkage group (LG) had an average of 88.4 markers screened, ranging from 62 to 128 markers/LG. The average number of markers screened was 93.9 on A chromosome and 82.9 on B chromosome, with additional 663 markers having no linkage group information. Th e number of amplifiable markers was 2,221 with an amplification ratio of 91.36%. The makers on B07 (B genome, 7 th chromosome) had the lowest amplification ratio of 78%, while B10 (B genome, 10 th chromosome) had the highest amplification ratio of 97.18%. Polymorphism was detected between the two parent lines at 329 SSR marker loci with a polymorphic ratio of 13.51%. The lowest polymorphic ratio was found on A04 (2.94% A genome, 7 th chromosome ), while the highest was found on B02 (26.39% B gen ome, 2 nd chromosome ) (Table 3 4).

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78 The sequences from 2,431 markers were aligned to two reference genomes (A and B genome) (Table 3 5), and 1,637 (67.34%) could be mapped to the genome and 794 SSR primers (32.66%) could not be mapped, which may due to the g enome rearrangement of cultivated peanut or incompleteness of the reference genomes. Polymorphic Marker Screening The 329 polymorphic SSR markers were further screened using selected 12 individuals, consisting of two parental lines, five susceptible plant s, and five resistant plants. Only 19 markers on A01 chromosomes displayed co segregation with phenotypes. Specifically, five resistant plants and Florida EP TM had the same band patterns, which were different from the patterns showed by five suscepti ble plants and Georgia Valencia (Figure 3 17). Markers on the other 19 chromosomes showed random band patterns and did not correspond to their phenotypi c data (Figure 3 18) indicating there are potential genes or regions on A01 controlling the spotted wilt disease resistance. To increase the mapping resolution on A01 chromosome, an additional 1 54 latest literatures (Koilkonda et al., 2012; Shrisawa et al. 2013 ) on A01 chromosome were recruited for polymorphism screening using the parental lines. Overall, 2,583 markers have been screened (Table 3 10). Most makers were derived from the published consensus map (Shirasawa et al., 2013) and table 3 11 showed In total, 29 p olymorphic SSR markers on A01 were used to genotype the whole F 2 population of 163 individuals. T o be thorough, five polymorphic markers on A09 were also used to genotype the 163 F 2 plants since a recent literature (Khera et al., 2014) reported another possible TSWV resistance QTL located on A09 chromosome.

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79 Local SNP Marker Development To enrich the marker s on chromosome A01, SNPs were discovered by aligning sequence reads from Florida EP TM and Georgia Valencia to the peanut reference gen omes. The Florida EP TM sequence alignment ratio was 99.97%; however, only 26.7% sequences had a single hit in the genomes. On the other hand, the alignment ratio of Georgia Valencia 454 sequences was 97.8%, and only 17.8% sequence had single hit (Tab le 3 6). In total, 754 SNPs were called and only 7 were located on A01 chromosome (Table 3 7). One SNP with a decent read depth ( Florida EP TM depth=49; Georgia Valencia depth=14) was selected for further validation (Table 3 8). The Sanger sequence re sults indicated that the SNP was not the true SNP between the two parental line s thus SNPs were not used for further genetic mapping Linkage Map Construction In total 29 polymorphic SSR markers on A01 were used to genotype the F 2 population with 163 in dividuals and 23 markers were mapped on A01 linkage group The genetic distance was 157.80 cM on A01 (Figure 3 19b). Out of 23 linked markers, 19 markers can be aligned to the reference genome on A01 chromosome (Figure 3 19a). On the physical map, the top marker aligned to A01 was AhTE0369 and the positi on was at 5.7 megabase. The bottom marker was AHGS1351 and the position was at 105.1 megabase. A good marker collinearity between the genetic and physical maps was observed (Figure 3 19, Table 3 9). Five polymorphic markers on A09 were genotyped, but the results indicated that the five makers were not linked so a linkage group could not be constructed QTL Analysis The s even phenotypic datasets and the genotypic results were used for QTL analysis. Two QT Ls representing two locations, NFREC and PSREU, were detected on A01 chromosome (Figure 3 20 and 3 21) The s ame QTL was identified using 2012NF IS, 2013NF VS, 2013NF

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80 IS, and 2014NF VS datasets with flanking markers, AHGS4584 (80.73 cM) and GM672 (83.28 cM ). A similar QTL was identified using 2012NF VS dataset with flanking markers, AHGS1646 (78.83cM) and AHGS4584 (80.73cM ). The QTL using 2013NF IS data set showed the highe st LOD score (9.00) and PVE (22.7%). 2012PS VS and 2013PS VS had same flaking markers, AHGS1713 (90.00 cM) and AHGS1760 (92.57 cM). However, the QTL on 2013PS VS showed the lowest LOD sc ore (3.76) and PVE (10.02%). Discussion One Gene Controlling R esistance Visual rating was a comprehensive plot based method including the assessment of spotted wilt severity and incidence H owever, compared to immunostrip test visual rating had a narrow disease scoring scale (1 to 10). When conducting QTL analysis, the discrete scales might not be able to represent the diverse variability of the populat ions In addition, visual rating requires experience in order to obtain stable and reliable phenotypic results. It is highly subjective and can p roduce rating biases by a single person. Different p ersons, various locations and years can cause inconsistent scoring issue Compar ing the to two measurement methods, immunostrip testing can capture TSWV reaction more accurately than visual scoring since it can detect the virus in non symptom atic plants Visu al rating had more ratings in the low range (rating 1 1.5 and 2) and much fewer in the high range (rating 7, 8 and 9) mostly due to the non symptomatic issue and non synchronized infection Even the homozygous parental lines e xhibited inconsistent disease level s across the field. Georgia Valencia should have had higher disease ratings than Florida EP TM ; however, if disease pressure was not sufficient, the susceptible and resistant checks were not easily d istinguished. However, t he distribution of immunostrip testing results of the two parents showe d a distinct difference which provided a clear cutoff point for the phenotyp ic data.

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81 Every single plot in F 2:4 and F 2:3 families can be traced back to a single F 2 plant. The family mean of phenotypic value from multiple individuals can represent the phen otype of single F 2 plant (Singh and Singh, 2015) Immunostrip results were derived from 10 random plants in a plot and can be considered as the average of a plot. According to immun ostrip results, more plots had severe infection compared to those with no or minor infection. The plots with severe infection showed bo t h homozygote and heterozygo te band pattern through ou t molecular maker testing. Therefore, susceptibility might be dominant over resistance. In 2013 (F 2:4 population) most immunostrip results of Florida EP TM (26 plots) were under 0.3 with only one plot above this value Therefore, a value of 0.3 was utilized as a cutoff. There were 38 plots under 0.3 and the re maining 125 plots were between 0.31 and 1.0. The 38 plots can be considered as resistant phenotypes and 125 plots were susceptible. A chi square goodness of fit test failed to reject a value=3.84, p <0.05). In 2012 (F 2:3 population), the disease pressure was lower than 2013 and according to the distribution of checks, all the immunostrip results of Florida EP TM were below 0.2. The cutoff was adjusted to 0.2 and there were 47 plots treated as resistant and 116 plots as susceptible. The chi square result still fit 3 (susceptible) to 1 (r =1. 28, critical value=3.84, p <0.05). The results indicated that a single major gene could possibly control the spotted wilt resistance in Florida EP TM Varying disease pressure over seasons and locations can affect the spott ed wilt epidemic making visual ratings less reliable. However, immunostrip testing can more accurately represent virus infection and therefore disease potential than visual rating. In this study, immunostrip results from Florida EP TM provide d information that allowed a reliable separat ion of resistant and susceptible phenotypes.

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82 One Putative M ajor QTL The QTL analysis of this study (Table 3 12, Figure 3 20 and 3 21) revealed two putative QTLs. One was associated to NFREC phenotypical data and another was associated to PSREU phenotypical data, which most likely are the same one QTL with a slight shift to different positions due to the environment effects (Weinig and Schmitt, 2004) In addition, g enotype by environment interaction can also influence the QTL position. All the seven data sets of phenotype data (table 3 12) had spotted wilt resistant QTLs on A01 chromosome with significant major effects (PVE>10%). Population size and marker density are critical for linkage map construction and the following QTL analysis (Collard et al. 2005) Th e population size of the F 2 in this study was relatively small with only 163 individuals and the marker density on linkage map was also limited (only 23 markers on A01 chromosome), which can cause the overestimation of QTL magnitude. In addition, genotypin g errors can affect the marker order and the accuracy of marker distance as well (Feltus et al. 2006; Gustafson et al. 2009; Varshney et al. 2007). Al though whole genome marker screening w as conducted with 308 markers and A01 chromosome was the most li kely location to possess spotted wilt resistant QTLs, the possibility to detect QTLs on other chromosomes with minor effects still exists In order to cover all the peanut genome section, it is necessarily to increase the number of markers in the future. Spotted wilt resistance QTL s Since QTL mapping has become relatively routine work in peanut, various QTLs were identified, including drought tolerance (Gautami et al., 2012; Ravi et al., 2011), disease resistance (TSWV, rust, late leaf spot, nematode, aphid vector of rosette disease, Cylindrocladium black rot and early leaf spot) (Herselman et al., 2004; Khedikar et al., 2010; Nagy et al., 2010; Qin et al., 2012 ; Simpson, 2001; Stalker and Mozingo, 2001; Sujay et al.,

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83 2012; Wang et al., 2013), and nutritional quality (oleic/linoleic acid and aflatoxins ( Aspergillus flavus )) (Liang et al., 2009; Sarvamangala et al., 2011). Several TSWV related genes/QTLs were iden tified. TSWV has a broad host range, covering more than 1 000 plant species (Peters, 1998) D ominant resistance genes have been found in tomato ( Solanum lycopersicum ) and pepper ( Capsicum annuum ), named Sw 5 and Tsw respectively (Stevens et al., 1991; Boiteux and De Avila, 1994) Often no disease symptoms were observed on the plants carrying Sw 5 and Tsw ; however, the local necrotic lesions may appear on infected leaves. These symptoms are typical of a resistance response in plants called hypersensitive response (HR). As discussed in C hapter T wo, the spotted wilt resistan ce in Florida EP TM does not appear to be a HR mechanism and in this study, though one major QTL was identified, the pheno t yping results indicated the inheritance of the trait was one resistant recessive gene model. Resistance from a rece ssive gene is relatively rare compared to dominant gene resistance One Sw 5 ortholog gene, Ahsw, has been cloned in peanut and it had 37% amino acid identity to Sw 5 (Chen et al., 2008) Further two putative TSWV resistance genes, Ahsw 1 and Ahsw 2 were cloned from peanut genomes and characterized as peanut oxalate oxidase genes (Chen et al., 2011) Two gene specific SSR markers (Seq2F10 and TC7G10) were screened with four parental lines from two mapping populations (SunOleic 97R/NC94022 and Tifunner/GT C20) and the association of markers with TSWV resistance is yet to be validated These SSR makers did not display polymorphism between Florida EP TM and Georgia Valencia thus were not used to validate the linkag e with in our segregating population. Two mapping populations have been utilized for spotted wilt resistan ce research. They were derived from the cross between Tifrunner and GT C20 (referred as T population) and the cross between SunOleic 97R and NC94022 ( referred as S population) (Qin et al., 2012) Two

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84 resistant QTLs ( qTSW V1 and qTSWV2 ) were first reported on S populations (Qin et al., 2012) qTSWV1 was located on linkage group 15 (LGJ15) on T population and qTSWV2 was on A01 chromosome on S population. Further QTL analyses were conducted using different generations (F 2 F 5 and RILs) on T population (Qin et al., 2012; Wang et al., 2013; Khera et al., 2014) There were 15, 9 and 11 spotted wilt related QTLs detected on F 2 F 5 and RILs populations, respectively. However, most of QTLs were found to be minor QTL (PVE<10%) ( Wang et al., 2013) On S population, RILs were utilized for QTL analysis and 13 spotted wilt related QTLs were found. Four QTLs were major QTLs (PVE>10%) on three linkage groups (A01, A01 and A09) and QTLs on A01 were the most significant ( Khera et al., 2014) The S population was derived from NC94022 an ancestor of Florida EP TM S ignificant major spotted wilt resistance QTLs on A01 at the similar locations were identified in both the S population and the p opulation studied here T he resistance segregating in both population s may be derived from same genetic source, PI 576638, known as hirsuta botanical type line introduced from the highlands of Mexico. Interestingly, on the similar QTL region, not only spotted wilt, another disease, early leaf spot resistant Q TL was detected ( Khera et al., 2014) Wild Arachis species displayed a high level resistance to several diseases (early leaf spot, late leaf spot and stem rot) (Holbrook and Sta lker, 2003; Singh et al., 1984). Although it has been difficult to transfer the disease resistan ce traits due to compatibility barriers and linkage drag, breeders made the efforts to transfer the resistance from wi ld species to cultivated peanut by using inter specific hybridization (Wynne et al., 1991; Singh et al., 1997). Peanut is not a native crop to the United States so plant introduction (PI) played an important role for peanut cultivar development. Many peanut cultivars in the United States can be trace d back to their ancestry of PI accessions in their pedigree (Isleib et al., 2001) In some

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85 areas, a s ingle cultivar can dominate the production market for a long time and become genetically vulnerable to biotic stress es (Knauft and Gorbet, 1989). In the United States the ancestors of runner type cultivars were derived from only 13 PIs (Isleib et al., 2001) Florunner, a runner type peanu t (Norden et al., 1969) was a dominat e cultivar in the southeas tern United States accounting for over 8 0 % of the United States acreage from 1972 to 1993. PI 203396 provided a source of resistance t o spotted wilt, late leaf spot [ Cercosporidium personatum (Berk. & Curt.) Deighton] and southern stem rot ( Sclerotium r olfsii ). Because of the multiple disease resistances, PI 203396 was used extensively as a parent for crossing and appeared in many runner (Isleib et al., 2001) PI 576638 was another PI accessions to provide spotted wilt resistance. It was reported that several bre eding lines containing PI 576638 ha d better TSWV resistance than the lines derived from PI 203396. The two PI accessions may contain different resistant genes and have different resistan ce mechanisms (Culbreath et al., 2005) The S mapping populations ( Qin et al., 2012 ) and the population in this study all contained PI 576638 within the pedigree and the same major QTL s located on A01 chromosome were identified, which should be donated by PI 576638. Peanut Germplasm D iversity China and India are the first and second largest peanut producer in the world (FAO database, 2014). They have their own germplasm sources which a re different to the United States To better understand the genetic diversity and population structure, 79 peanut breeding lines and cultivars in the United States India and China were evaluated by using 111 SSR markers. The mean values of gene diversity in the United States India and China was 0.363, 0.47 and 0.489, respectively. The average polymorphic information content (PIC) of the United States India and China was 0.323, 0.412 and 0.43 0 respectively (Wang et al., 2015) Both results indicated the lower genetic diversity in the United States germplasm than in India and China. The d endrogram

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86 based on the genetic distance matrix from three countries also showed the breeding lines in the United States had a lower level of genetic variation than other countries (Wang et al., 2015) Hybridization and selection are the fundamental techniques for genetic im provement in a breeding program; however, the limited sources used in the United States (Wang et al., 2015) hampered the increase of genetic gain. To broaden the genetics basis, different cross combinations from different countries can be designed to develop effective peanut breeding programs. Marker Assisted S election (MAS ) Several researche r s reported that marker assisted selection (MAS) and/or marker assisted backcrossing (MABC) were successfully applied for peanut cultivar development (Ashikari and Matsuoka, 2006; Miklas et al., 2006; Collard and Mackill, 2008; Xu and Crouch, 2008) For (Simp son and Starr, 2001) (Simpson et al., 2003) were the first two nematode resistant cultivars developed by using restriction fragment length polymorphism (RFLP) markers to assist selection resistant cultivars, however it doe s not have a high oleic : linoleic acid (high O:L) ratio in seeds (Chu et al., 2011) Thus MABC was conducted with Tifgua rd as the recurrent parent to maintain nematode resistance. Georgia 02C and Florida 07, two high oleic acid cultivars were used as donor parents. After three cycles of MABC, Tifguard (Chu et and a major QTL with up to 82.62% PVE was validated (Sujay et al., 2012) MABC was conducted through four SSR markers and the QTL was introgressed into three rust susceptible varieties (JL 24, TAG 24 and IC GV 91114). Several introgression lines were developed with rust resistance and higher yields (Varshney et al., 2014).

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87 I ntrogress ing multiple disease resistant genes/QTLs into a cultivar has been a challenge in conventional breeding program. However, with MAS/MABC multiple resistant genes can be introduced into pyramid lines, show ing a wider spectrum of disease resistance (Kelly and Miklas, 1998; Wang Zhen et al., 2005; Ashikari and Matsuoka, 2006; Stuthman et al., 2007; Zong et al., 2012) In peanut, the pyramiding breeding progress has bee n slow due to limited closely linked markers available. Ho wever, with more marker linked genes/ QTLs identified it will be easier to develop new cultivar s with multiple desired traits and with a short er breeding cycle (Gajjar et al., 2014). Qualitative traits such as disease resistance are more effective for conduct ing gene/QTL pyramiding than quantitative traits such as yield due to their p olygenic and complex inheritances In this study, the spotted wilt resistant QTL with 22.7% PVE was identified and two flanking makers, AHGS4584 and GM672 can be applied to conduct MAS. The map distance between two markers was 2.55 cM on linkage map with 14.4 megabase ( Mb ) distance on physical map. Although the physical distance was estimat ed by A genome ( A. duranensis ) sequencing, not directly from the genome s of cultivated peanut, the distance was still too far. Crossovers could happen between traits (the resistant QTL) and markers, causing recombination which may lead false positive resu lts and decreased selection accuracy. More c lose ly linked markers should be obtained to increase the efficiency of MAS. Advanced T echnology SNP is the most abundant polymorphism in genomes with a frequency of one SNP every 100 to 300 base pair s ( Gupta et al. 2001) and can be found in both genic and non genic regions ( Bundock et al. 2009; Kwok et al., 2001) In this study, to enrich the marker in the target region, because the high density markers can improve the precision of QTL mapping, we discovered th e SNPs between the two parental lines. However, no SNP were validated mostly due to the shallow reads specifically from Georgia Valencia ( only 78 890 reads)

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88 Genotyping by sequencing (GBS) is a next generation sequencing (NGS) enabled genotyping method w hich is high throughput and can generate a large number of SNP s with genome wide coverage. GBS is feasible for large genome species with a low cost (Elshire et al., 2011) It can be applied to the mapping population in this study and new SNPs will be available for construct ing a high density linkage map, QTL analysis and MAS. NGS technology can generate much more genomic data than before and new mapping populations (NAM and MAGIC) with multiple parents can be c onsidered for d evelop ment Nested association mapping (NAM) is a famous technique for identifying the genetic architecture in corn (Buckler et al., 2009) Twenty five diverse corn lines were chosen as parents and used to develop a lager inter related RIL mapping population. The purpose was to find the associations between traits and SNPs within the NAM populations. NAM populations were very successful in corn research because they had higher power to detect QTLs than bi parental mapping populations (McMullen et al., 2009; Kump et al., 2011; Tian et al., 2011) The parents involved in multi parent advanced generation inter cross (MAGIC) populations contained multiple parents with different desirable traits, like disease resistance drought tolerance and yield (Huang et al., 2012; Bandillo et al., 2013) This method can directly map the QTLs of multiple traits and MAGIC populations can be further used in breeding programs as valuable germplasm sources. Conclusion E xpression of spot ted wilt disease in peanut is highly variable from season to season depending on the disease pressure. By using immunostrip testing, Florida EP TM proved to be a good variety to efficiently separate resistant and susceptible genotypes Since visual ra ting cannot detect asymptomatic resistan t were actually susceptible genotypes. According to phenotypic distributions by chi square goodness of fit test s the spotted wilt resistan ce trait could possibly be controlled by one recessive gene.

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89 By screening polymorphic makers between Flor i da EP TM and Georgia Valencia, only makers located on A01 chromosome co segregat ed with spotted wilt resistan ce The A01 linkage map constructed b y Florida EP TM derived population had good marker collinearity with the physical linkage map. Two QTLs have been identified on A01 chromosome. One QTL was PSREU specific and another was NFREC specific. The latter one had higher LOD and PVE values tha n the former. Historically, the spotted wilt pressure was lower at PSREU so QTL regions identified on NFREC are more reliable with t wo flanking markers AHGS4584 and GM672. The QTL is most likely contributed by PI 576638, a hirsuta botanical type line, in troducing from Mexico with spotted wilt resistance. Marker enrichment on the region needs to be conducted to perform fine mapping to refine the QTL region. Next generation technology, like GBS or other new developed populations, like NAM and MAGIC could also be utilized in the future.

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90 Table 3 1. The number of plot, replication and check at different sites in F 3 F 4 and F 5 populations. Year Number Site PSREU NFREC Plot number 210 210 2012 (F 2:3 ) Replication number 1 1 Check number 21 21 Plot number 384 384 2013 (F 2:4 ) Replication number 2 2 Check number 55 55 Plot number 384 2014 (F 2:5 ) Replication number 2 Check number 56 Table 3 2. The information about sites and phenotyping methods on F 3 F 4 and F 5 populations. Method Site PSREU NFREC Visual rating 2012 (F 2:3 ) 2012 (F 2:3 ) 2013 (F 2:4 ) 2013 (F 2:4 ) 2014 (F 2:5 ) Immunostrip testing 2012 (F 2:3 ) 2013 (F 2:4 ) Table 3 3. The phenotyping correlation table among different datasets and the number indicated the Spearman's rank correlation coefficient. means correlation is significant at the 0.05 level, ** means correlation is significant at the 0.01 level. 2012NF VR 2012PS VR 2013NF IS 2013NF VR 2013PS VR 2014NF VR 2012NF IS 0 .591** 0 .270** 0 .498** 0 .503** 0 .209** 0 .290** 2012NF VR 0 .266** 0 .285** 0 .355** 0.094 0 .162* 2012PS VR 0 .247** 0 .266** 0 .236** 0 .223** 2013NF IS 0 .781** 0 .401** 0 .433** 2013NF VR 0 .316** 0 .343** 2013PS VR 0 .338**

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91 Table 3 4. The number of SSR primers screened, amplification and polymorphic ratio at different linkage groups. Linkage group Total SSRs screened Number of primer amplifible Number of ploymorphic primer Amplification Ratio (%) Polymporphic Ratio (%) A01 110 98 19 89.09 17.27 A02 75 66 14 88.00 18.67 A03 128 121 23 94.53 17.97 A04 100 88 7 88.00 7.00 A05 89 81 9 91.01 10.11 A06 98 92 14 93.88 14.29 A07 62 54 15 87.10 24.19 A08 102 93 3 91.18 2.94 A09 91 82 20 90.11 21.98 A10 84 77 6 91.67 7.14 B01 99 89 20 89.90 20.20 B02 72 64 19 88.89 26.39 B03 95 89 8 93.68 8.42 B04 97 90 7 92.78 7.22 B05 76 72 8 94.74 10.53 B06 82 78 10 95.12 12.20 B07 80 72 13 90.00 16.25 B08 82 75 6 91.46 7.32 B09 75 66 15 88.00 20.00 B10 71 69 11 97.18 15.49 No li n kage group info rm ation 663 606 82 91.40 12.37 Total 2431 2221 329 91.36 13.53 Table 3 5 The alignment and no alignment ratio of SSR primers aligned to two Arachis reference genomes. Number of primer Ratio (%) No Alignment 794 32.67 Alignment 1637 67.33 Total 2431 100

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92 Table 3 6. The alignment information of sequence reads from Florida EP TM and Georgia Valencia Total reads Aligned reads Alignment ratio (%) Single hit ratio (%) Florida EP TM 27380147 27372233 99.97 26.7 Georgia Valencia 78890 77156 97.8 17.8 Table 3 7. The positions and depths of putative SNPs on A01 chromosome between Florida EP and Georgia Valencia Chromosome Position Ref allele Alt allele Depth (Georgia Valencia) Depth ( Florida EP TM ) A01 96139352 G A 2 186 A01 96139354 A C 2 146 A01 22845982 T C 14 49 A01 40659019 T C 3 30 A01 93702114 T C 5 6 A01 5688530 T C 3 5 A01 40659146 C T 3 2 Table 3 8. The SNP primer sequence, amplicon size and Tm for validation. Primer name Forward sequence Reverse sequence Amplicon size Tm ( C ) A01_SNP GGGAGAACAAACATGCATCA TGTTAGCTTTTCATTGCGTCA 597 59.5

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93 Table 3 9. The positions of A01 markers on physical (Mb) and linkage map (cM). Marker name Physaical map (Mb) Linkage map (cM) AhTE0369 5.692568 0 AhTE0188 none 4.11 AHGS1910 19.035441 27.49 ARS729 31.451985 32.03 ARS721 22.130732 34.56 AHGS1465 20.917612 56.67 AHGS1389 37.76887 78.21 AHGS3363 42.634304 78.52 AHGS1646 43.349687 78.83 AHGS4584 57.79209 80.73 GM672 72.196023 83.28 Ah21 89.295856 84.22 GM1661 89.295751 82.22 Ah126 89.295748 84.22 GM1694 90.064266 85.46 AHGS1713 90 AHGS1760 92.841619 92.57 GM2350 103.02 TC3H02 130.17 AhTE0571 103.985083 139.01 seq8E12 104.139715 145.06 AhTE0499 103.336438 149.76 AHGS1351 105.122604 157.85

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94 Table 3 10. The literature sources of SSR primers screened. Marker reference Number of marker used Hopkins et al., 1999 2 Gautami et al., 2012 3 Liu et al., 2013 7 Gimenes et al., 2007 8 Leal Bertioli et al., 2009 8 Moretzsohn et al., 2009 9 He et al., 2005 17 Moretzsohn et al., 2004 17 He et.al, 2006 18 Liang et al., 2009 20 Proite et al., 2007 27 He et al. 2003 30 Bertioli et al., 2009 48 Qin et al., 2011 54 Cuc et al, 2008 55 Dr. Jianping Wang's lab 57 Wang et al., 2007 57 Macedo et al., 2012 65 Ferguson et al., 2004 127 Moretzsohn et al., 2005 128 Wang et al., 2012 156 Nagy et al., 2010 284 Koilkonda et al., 2012 344 Shirasawa et al., 2013 1044 Total 2585

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95 Table 3 11. Marker name Forward sequence Reverse sequence AHW0831 TACAGTTTGTGCAGCTTCGG TCAAACAAGAAGCTCGTTCA AHW0842 TGAAATCGCTGCTTGAGAGA TGGCCATCCCATTACTTCAT AHW099 GGGTTCGTGTTCTGGGTAGA TCCATAGCTGAGTCTGGCCT AHW1021 CTCTTGCCCTGTCTCACTCC AGACCTTGCAACTGTGAGGG AHW1062 TTCGACATCGCTGTTCTGTC CGAGCTCTCTCTCTCCTTCG AHW1063 TTCGACATCGCTGTTCTGTC GCTGATCTGGAGGCAACG AHW1332 CATTTACCTGAGTCTCTCCTTCAA TCCGAAGCCAAAATCAAATC AHW1337 TTACATTGGCTGGGGAGAAG AGCCTCCCGAGAGTAACACA AHW1358 ATCCCATTCACTCGTCTTCG CATGGCAAGTTGCTTCTTCA AHW1871 AAGCAAAGCAACCCTTCCTC TTTAGGGCATGGGAATGAAC AHW1997 GGGACCGAAGCAAGAAATTA TGCATAGCTTGCTTCTCCAA AHW1999 CCAAACCAAACAGAAGAGGAA CAATCTCCGAGAACTGGCTC AHW2009 GGGCTTCATCAGGTCAGAGT CATCCTCGATCAAAGCCAAT AHW2181 CATGTCGTTGTCCATGAAGG AGTTGTCTGACGTCTCGGCT AHW2279 CTACCACACGCATTGTCACC GACAGAACAGCGATGTCGAA AHW2670 ACCGAAACCCCCAAGTTATC CTTCCCGAACACTTGTCCAT AHW2770 CATTTACCTGAGTCTCTCCTTCA TCCTTCTGCCATCGTCTTCT AHW2781 CCCTTCCTTCTCTTCTTCCTC TGTGAGGTTGTGGCAGTGAT AHW2797 GCCATGTTCTTTTTGCTTGA CCCCATCACCTATTTCTTATTCTT AHW3006 AAGCAGCGAAAGTGAAGAGG GGCAAGAAGAGCATGTGTCA AHW3198 ATCATGTCGTCAATCGTCCA TCTCCAAATTGACCAAAAGG AHW3220 TACTGGGTTCCCTTTCATGC TCAATTTAGCAACAAATTCCACA AHW3303 TAAGCTGCTGTTGCTGCTGT CCACATCACATGGGGACA AHW3304 TAAGCTGCTGTTGCTGCTGT AATAAGCATGAATGGGAGCG AHW351 GGTTCATCCTCCTCCTCCTC ATGCCAATGGCTTCTCAACT AHW3621 TGGGTTTCGGGATATGTTGT GTGGTATAATCCGCCACCAC AHW369 CACTATGGTGACTGAACTGCAA CGCATTTCATGTGGTTTTTG AHW3838 TCCCTACTTCTCCCTCCCTC GGAGGGTCCAGGTACTCCAT AHW3887 GACCCAAAAACAAACATGGC AGAAAACAATGGCGAGGATG AHW3982 AGTCTCATGGAAGCAGGCAC GCCCATGATGATGAAAAGGT AHW4148 ACACCACAACCCTCTCGTTC TCAGAATTTGGGTGTGTGAAA AHW4292 TTGAATCCCTTCTCGATTATCC GAAGAGGGACTCCATGGTCA AHW4300 TTTACCTGAGTCTCTCCTTCCA TTAACACGTTAACGCCACCA AHW4320 TCGTTTTCAACATGGTTTGACT ACCAATCAACCCTCCAATGA AHW4460 ACATTGAAGCTGGAGGCAGT TAAAAAGCGTAACCCGTGCT AHW4473 TTTTTGAATCCCCTCTCGATT GAAGAGGGACTCCATGGTCA AHW4526 CTTCACCTTTTTGGTTCCCA GAGAAGAAAGAGGAGCCCGT AHW4542 CCCAATGGTCGCTGAATAGT CGTTGTGTCTTTGGTGATGG AHW4564 GGATTCAGTGCCACCAAAGT GGCAAGAAGAGCATGTGTCA AHW4605 CGCGAAGAAAAACAAGAAGG TCACCGAGAGTTTTTCACCC AHW4857 GGGGGACAAATAACAATACACG AATGATGGTTTGGAGAAGCG AHW4961 CCTTGACGCTGCTTCTTCTT TTACCTGCTGCATTTCCCTC

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96 AHW4972 TTTCTCGCTTGTGATCCCTT TGGGTATTCGTTGCGTTGTA AHW5330 CATCCTTGAATATCCTAATTCCTAA CATTGGCTGGTTCTCTCCAT AHW5374 CCTTGACGCTGCTTCTTCTT GGTGGTGCTGTTTTGGTTCT AHW5635 TGTTCCCCTTTTTCACCTTG GCAAATATTGACAACGGCCT AHW6219 GATTGAAATCCACGGCTTGT CCTTTTTGATCTGGTTCCCA AHW6286 CATACCGTTCTCGCCAATTT AGGAAGAGACGACCCAGTGA AHW630 AAGAAACGAGATGAGGCAGAA CGGATACCATCATCTTGCG AHW6343 CACGATGCTCTCTGCCAATA GAGGGGTAGCAGAGGTGATG AHW780 CCTCCACCATAAGTACCTGC CAATTGAGCTTTCACCGATTC ESTLAB01 ATCGCGAGAAGCAGTGGTAT GAAGCAGTGGTATCAACGCA ESTLAB02 GCAGTGGTATCAACGCAGAG CCATTCATATGTCGAGACACCA ESTLAB03 CGACACAGTGGTATCAACGC AAAGTCGGAGTTGAACGGTG ESTLAB04 AAGTGTGGTGTCCAAGGGAG GTCCTCGAAAGGACATGGTG ESTLAB05 AGACGCTCGACACCAAAGTC GAAGCAGTGGTATCAACGCA ESTLAB06 CTGAAATGGGGAAAGGAGGT AAGGGCCATGTCTCCTTGTT

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97 Table 3 1 2 The positions, flanking markers, LOD values, PVE (%) and additive effects of putative QTLs on A01 chromosome. Dataset Chromosome Position (cM) Left marker Right marker LOD PVE(%) add 2012PS VR A01 92 AHGS1713 AHGS1760 6.65 16.93 0.56 2013PS VR A01 90 AHGS1713 AHGS1760 3.76 10.02 0.24 2012NF VR A01 80 AHGS1646 AHGS4584 4.52 12.17 0.59 2013NF VR A01 81 AHGS4584 GM672 6.91 17.69 0.91 2014NF VR A01 81 AHGS4584 GM672 4.33 11.55 0.71 2012NF IS A01 81 AHGS4584 GM672 6.52 17.06 0.19 2013NF IS A01 82 AHGS4584 GM672 9 22.7 0.17

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98 Figure 3 1. Three prefilled Agdia sample bags with three negative (single red line) results for TSWV. Photo taken by Yu Chien Tseng. Figure 3 2. Three prefilled Agdia sample bags with three positive (two red line) results for TSWV. Photo taken by Yu Chien Tseng.

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99 Figure 3 3. TSWV infec tion results on F 2:3 population by visual rating at PSREU in 2012. Figure 3 4. TSWV infection results on the checks of F 2:3 population by visual rating at PSREU in 2012. 0 10 20 30 40 50 60 70 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visaul rating 0 1 2 3 4 5 6 7 8 9 10 1 1.5 2 3 4 5 6 7 8 Numer of lines Visual rating Florida-EP-113 Georgia Valencia

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100 Figure 3 5. TSWV infection results on F 2:3 population by visual rating at NFREC in 2012. Figure 3 6. TSWV infection results on the checks of F 2:3 population by visual rating at NFREC in 2012. 0 10 20 30 40 50 60 70 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visaul rating 0 1 2 3 4 5 6 7 8 9 10 1 1.5 2 3 4 5 Number of lines Visual rating Florida-EP-113 Georgia Valencia

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101 Figure 3 7. TSWV infection results on F 2:3 population by immunostrip testing at NFREC in 2012. Figure 3 8. TSWV infection results on the checks of F 2:3 population by immunostrip testing at NFREC in 2012. 0 5 10 15 20 25 30 0-.1 .11-.2 .21-.3 .31-.4 .41-.5 .51-.6 .61-.7 .71-.8 .81-.9 .91-1 Number of lines Precentage of immunostrip positive 0 1 2 3 4 5 6 7 8 9 10 0-.1 .11-.2 .21-.3 .31-.4 .41-.5 .51-.6 .61-.7 .71-.8 .81-.9 .91-1 Number of lines Number of immuostrip positive Florida-EP-113 Georgia Valencia

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102 Figure 3 9. TSWV infection results on F 2:4 population by visual rating at PSREU in 2013. Figure 3 10. TSWV infection results on the checks of F 2:4 population by visual rating at PSREU in 2013. 0 10 20 30 40 50 60 70 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visual rating 0 5 10 15 20 25 1 1.5 2 3 4 5 6 7 Number of lines Visual rating Florida-EP-113 Georgia Valencia

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103 Figure 3 11. TSWV infection results on F 2:4 population by visual rating at NFREC in 2013. Figure 3 12. TSWV infection results on the checks of F 2:4 population by visual rating at NFREC in 2013. 0 10 20 30 40 50 60 70 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visaul rating 0 5 10 15 20 25 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visual rating Florida-EP-113 Georgia Valencia

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104 Figure 3 13. TSWV infection results on F 2:4 po pulation by immunostrip testing at NFREC in 2013. Figure 3 14. TSWV infection results on the checks of F 2:4 population by immunostrip testing at NFREC in 2013. 0 5 10 15 20 25 30 0-.1 .11-.2 .21-.3 .31-.4 .41-.5 .51-.6 .61-.7 .71-.8 .81-.9 .91-1 Numver of lines Percentage of immunostrip positive 0 5 10 15 20 25 0-.1 .11-.2 .21-.3 .31-.4 .41-.5 .51-.6 .61-.7 .71-.8 .81-.9 .91-1 Number of lines Number of immunostrip positive Florida-EP-113 Georgia Valencia

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105 Figure 3 15. TSWV infection results on F 2:5 population by visual rating at NFREC in 2014. Figure 3 16. TSWV infection results on the checks of F 2:5 population by visual rating at NFREC in 2014. 0 10 20 30 40 50 60 70 1 1.5 2 3 4 5 6 7 8 9 Number of lines Visual rating 0 5 10 15 20 25 1 1.5 2 3 4 5 6 7 8 Number of lines Visual rating Florida-EP-113 Georgia Valencia

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106 Figure 3 17. PAGE gel image s for 12 plants using polymorphic SSR markers located on A01 chromosome. A) GA110 markers located on A01 chromosome, B) AH GS1910 marker located on A01 chromosome. 1: Georgia Valencia, 2: Florida EP TM 3 to 7: Susceptible plants, 8 12: resistant plants. Figure 3 18. PAGE gel image s for 12 plants using polymorphic SSR markers located on A09 & A10 chromosomes. A) AHGS1647 markers located on A09 chromosome, B) AHGS1390 marker located on A10 chromosome. 1: Georgia Valencia, 2: Florida EP TM 3 to 7: Susceptible plants, 8 12: resistant plants.

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107 Figure 3 19. Physical and linkage map showing the position of SSR markers on A01 chromosome. Lines indicated the same markers on both maps. A) physical map and the number indicated the Megabase (Mb), B) linkage map and the number indicated Centimorgan (cM).

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108 Figure 3 20. Linkage group with S SR marker positions and the detected QTLs showing by different icons indicated different phenotyping datasets.

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109 Figure 3 21. Linkage group with SSR marker positions and the detected QTLs showing by different color peaks indicated different phenotyping da tasets.

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110 CHAPTER 4 HERITABILITY OF SPOTTED WILT RESISTANCE IN Florida EP TM DERIVED POPULATIONS Introduction Cultivated p eanut ( Arachis hypogaea L.) is a widely cultivated oil seed crop The seeds can be utilized directly for food or be produced for o il purpose. Peanut seed has high nutrient, high protein content good flavor and essential vitamins. More than 42 million tons of peanuts were produced in 2013 China, India, Nigeria and the United States are the four major peanut production countries ( FAO Statistical Databases 2015, http://faostat.fao.org/faostat/ ) Peanut is grown mainly in semi arid tropic and sub tropic areas in the world (Naidu et al., 1999) Peanut is an allotetraploid crop (genome AABB, 2n=4x=40). Genomes A and B in cultivated peanut came from two wild diploid Arachis species, A. duranensi s (A genome) and A. ipaensis (B genome). The hybridization of two wild diploid species and spontaneous chromosome duplication resulted in the isola tion of cultivated peanut from wild species (Kochert et al., 1991) Tomato spotted wilt virus (TSWV) is a virus in the family Bunyaviridae genus Tospovirus (Culbreath et al., 2003; Whitfield et al., 2005; Culbreath and Srinivasan, 2011) It causes spotted wilt in peanuts and can seriously affect peanut production in the southeastern United States. Annual peanut yield lo sses due to TSWV were estimated to be $ 40 100 million in Georgia alone in 1997 (Culbreath et al., 2003) The t ypical foliar symptoms of spotted wilt include concentric ringspots, chlorosis and necrosis on leaflets as well as mosaic patterns and stunting (Culbreath et al., 2003; Whitfield et al., 2005) TSWV is only transmitted by thrips but has a broad host range, including both dicots and monocots in 35 families and many economically important field crops such as tobacco and peanut; vegetables such as tomato, pepper, potato and eggplant (Parrella et al., 2003; Pappu et al., 2009) Western flower thrips (Frankliniella occidentalis) and tobacco

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111 thrips (Frankliniella fusca) are the two major TSWV transmitting vectors in peanut (Tod d et al., 1995; Groves et al., 2002) No single method has proven effective to control spotted wilt. Many factors have been shown to affect the severity of spotted wilt including : peanut variety, planting date, plant population, row pattern, crop rotation, tillage, and so on. The methods involving in these factors are all combined into an integrated disease management tool for spotted wilt suppression called Peanut Rx (Culbreath et al., 2003; Sundaraj et al., 2014) However, t he single most important factor in management of spotted wilt in peanut is cultivar resistance. The University of Florida peanut breeding program released a runner market type peanut, Florida EP TM ( Tillman and Gorbet, 2012 ) which was derived from a cross between NC940 2 2 and ANorden (Gorbet, 2007b) NC94022 is a breeding line with good field resistance to spotted wilt. This resistance was theorized to have come from PI 576638, a botanical type of peanut known as hisuta ( A. hypogaea subsp. hypogaea var. hirsuta ). The hirsuta types might provide a spe cial spotted wilt resistance resource (Culbreath et al., 2005 ) Currently, no varieties are completely immune to spotted wilt (Culbreath et al., 2010) except that the new released Florida EP TM displaying a promising spotted wilt resistance in the field and being superior over many other most resistance varieties currently available (Mckinney, 2013) Florida EP TM has been tested under earlier planting date (April) and reduced seed density (13.1 seed per meter). Both conditions are favor for spotted wilt epidemics, under which Florida EP TM performed excellent resistance to obviate the high risk situations (Mckinney, 2013) Immunosstrip testi ng was conducted to detect virus inside the plants, since foliar symptomology cannot always represent disease incidence, which may underestimate the actual

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112 viral amount (Murakami et al., 2006; Row land et al., 2005). In chapter 2 it showed that Florida EP T M had significant lower infection frequency (less than 5%) than other three cultivars ( Florida 07, Georgia Green and Georgia Valencia ) by immunostrip testing. Other studies have reported several breeding lines also contain lower TSWV infection frequency but never as low as Florida EP TM (Mckinney, 2013) Heritability described as the resemblance between offspring and their parents (Falconer, 1960; Lynch and Walsh, 1998) Heritability can determine the potential of a population responding to selection, thus is critical for breeders, because it reflects the selection efficiency of interested traits. High heritability means high selection response and the traits are highly inheritable. Heritability is a proportion of g enetic variance (addictive variance) in the overall phenotypic variance. If the proportion of e nvironmental variance is much larger than the genotypic variance, it is less reliable to select the traits based on the phenotypical performance. On the other hand, if the proportion of genetic variance is high in relation to environment variation, selection will be efficient (Briggs and Knowles, 1977) The expression of spotted wilt disease is highly variable from season to season and makes it difficult to effectively assess the disease resistance in the field A few studies have been reported discussing the heritability of spotted wilt resistance. Baldessari (2008) used a type of visual rating method: disease intensity rating (DIR), which represented a combination of incidence and severity to condu ct disease phenotyping. The results showed a wide range of heritability (0.01 0.71) with most estimates in the low to medium range. The reason for such a wide range heritability could be the high level of disease variability mostly due to non synchronized and non uniformed natural infection and asymptomatic issue in the field (Culbreath et al., 2005; Rowland et al., 2006). Immunostrip testing can detect virus coat protein thus eliminated the

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113 asymptomatic issue and is a promising alternative method to precis ely estimate heritability of TSWV resistance. In addition, could avoid the escape condition by the natural inoculation and improves the estimation of heritability. Florida EP TM is a promising spotted wilt resistant variety and can used as a breeding line to provide the disease resistance trait H owever, the knowledge of inheritance in Florida EP TM i s unknown. It is important to conduct experiments to estimate heritability of spotted wilt resistance. T he objectives of this study were: 1) to estimate heritability of two measurement methods, visual rating and immunostrip testing at different locations in different years, and to evaluate if the immunostrip testing is a better m ethod of choice 2) to calculate the genetic correlation between measurement methods, 3) to calculate the genetic correlation among locations and years and to understand if G*E interaction is present and 4) to provide breeding value prediction among diff erent years and locations by visual rating and immunostrip testing and identify breeding lines with resistance to spotted wilt disease similar to, or superior to Florida EP TM Materials and Methods Plant M aterial Field experiments were conducted in two locations, the North Florida Research and he soil type of the NFREC is a Chipola loamy sand and Orangeburg loamy sand. The test sites were previously planted with maize ( Zea mays L.) and cotton ( Gossypium hirsutum L.) for crop rotation. At PSREU the soil type is Arrendondo sand and Orangeburg loamy sand. Bahiagrass

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114 ( Paspalum notatum ) was chosen for crop rotation with three years in bahiagrass followed by peanut. A cross between Florida EP TM and Georgia Valencia was made in 2009 and their F 1 hybrid seeds were plante d at PSREU in 2010. The F 2 segregating population was planted at NFREC in 2011. All F 2 plants were self pollinated to generate F 2:3 (F 2 derived in F 3 ) families in 2012 and allowed to self pollinating to generate F 2:4 (F 2 derived in F 4 ) families in 2013 a nd F 2:5 (F 2 derived in F 5 ) families in 2014 The experiments were maintained with commercial peanut cultural practices and the standar d IFAS Extension recommendation Irrigation was accomplished using overhead center pivots. In order to maximize the pote ntial for severe spotted wilt epidemics, thimet insecticide was not applied, the planting date was earlier than regular suggestion, and the seeding density was one plant per foot of row. Hence, t hese practices encourage spotted wilt development and are th e opposite of university extension recommendations for farmers to minimize risk of spotted wilt (Culbreath et al., 2010) The F 2 population was planted in the NFREC, where spotted wilt epidemics tend to be greater compared to the PSREU. A total of 200 individual F 2 peanut plants were planted in April, 2011. Each F 2:3 family was derived from a single F 2 plant. Since there were not sufficient seeds for two replications in two locations, only one replication was planted in each location. Planting occurred at PSREU in early April and at NFREC in the middle of April. An augmented experimental design with two parental lines as controls was used in each location. Each plot contained a single family and had two rows, 0.9 m wide and 4.5 m long. Seed planting density was one seed per 0.3 m. Since some F 2 seeds were mixed together after harvest, only the pure

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115 seeds were kept and planted to become F 2:3 family. The experiment field was comprised of 189 plots plus 21 check plots, with a total of 210 plots. The check plots were two parental lines. The plants from each F 2:3 family were bulk harvested and 32 seeds were ran domly picked to plant the F 2:4 families. The experimental design in F 2:4 families was a randomized completed block design (RCBD) with two blocks and planted in two location s NFREC and PSREU. Each replication was arranged as an augmented design with two pa rental lines spaced throughout the replication as controls. Every replication had 163 plots plus 29 check plots, with a total for the two replications of 384 plots. Every plot had two rows, in a width of 0.9 m and a length of 4.5 m. The seed planting den sity was one seed per 0.3 m. Harvest of the F 2:4 families and subsequent planting of the F 2:5 was the same as described for the F 2:3 The experimental design for the F 2:5 was the same as the F 2:4 but was planted only in the NFREC location. There were 384 plots with check plots. The plot size is as same as generation F 2:3 and F 2:4. (Table 4 1). Disease Evaluation Methods and D at a C ollection Two different disease evaluation methods to assess the severity of spotted wilt were conducted for each plot. First, a visual rating on a scale of 1 to 10 scale and second a form of ELISA (the enzyme linked immunosorbent assay ) testing. The visual assessment (1 to 10 scale) was estimated through the whole plot prior to digging and each plot was assessed for typical symptoms of spotted wilt such as stunting and foliar symptoms of ringspot, leaf necrosis and chlorosis (yellowing) (Culbreath et al., 2003b) The 1 to 10 represents a percentage of disease severity. In this study 1 = 0%, 1.5 = symptoms observed with less than 10% infection, 2 = 11 20% infection, 3 = 21 30% infection, 4 = 31 40% infection, 5 = 41 50% in fection, 6 = 51 60% infection, 7 = 61 70% infection, 8 = 71 80% infection, 9 = 81 90% infection, and 10 = 91 100% infection

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116 The immunostrip testing was conducted by using the ImmunoStrip Kit (Agdia Inc., Elkhart, IN, United States ). It used TSWV specific m onoclonal antibody as the capture reagent and is an on site tool to quickly identify virus in plants. Immunostrip Kits were stored at four to six C until the testing began. Ten individual plants were randomly selected from each plot and root crowns for each plant were collected after digging. Each root crown was air dred in the laboratory after which each root crown sample was trimmed to 0.4 grams and placed into the sampling bag that contained SEB1 (sample extraction buffer1). The root crown sample was crushed within the sampling bag by utilizing a hammer. Then, the test strips were inserted into the bags ensuring that the strips were immersed in the SEB1/root crown fluid. Results were evident within 5 to 30 minutes. The strip has two indication lines: t he upper line was control line and the lower line was test line. If only the upper line (control line) displayed, no T SW V was detected (Figure 4 1), however, if two lines (control line and test line) displayed, TSWV was detected in the sample (Figure 4 2) If neither line displayed, the test was invalid. Based on the presence or absence of TSWV, scoring was 1 if the virus was detected or 0 if no virus was detected. Each plot had the possibility of TSWV infection percentage ranging from 0 to 100%. The visu al disease evaluation method was conducted in F 2:3 F 2:4 and F 2:5 f amilies in both PSREU and NFREC; however, immunostrip testing was only utilized in F 2:3 and F 2:4 generations at NFREC (Table 4 2). Single Site Analysis (Univariate M odel) A single site ana lysis was conducted by using linear mixed models with statistical software ASReml version 3.0 (Gilmour et al., 2009) T he t wo evaluation methods, visual rating and immunostrip testing were t reated as different response variables. Visual ratings were logarithmically (base 10) transformed to achieve a normal distribution of residuals. Data from

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117 each year (2012, 2013 and 2014) and each location (NFREC and PSREU) were fitted individually to the following linear mixed model equation: a vector of the fixed effects; g is a vector of random effects with g~MVN (0, A ); and e is the vector of random errors, with e~MVN (0, I ). The le tters X and Z are the incidence matrices for the fixed and random effects respectively The matrix A is the numerator relationship matrix obtained from pedigree, and matrix I is an identity matrix. The pedigree files of the population were incorporated int o the analysis. It is critical to incorporate the pedigree information because different generations are considered t o be dependent to each other If the genetic relationship among different generations can be described more specifically and accurately, th e estimation of genetic parameters can be largely improved. Spatial analysis was conducted with the row and column information according to the field map layout. Variance components for genotype and residual s were calculated by utilizing the first order autoregressive model, AR(1) that allows estimation of spatial correlation. Seven different single site analyses were completed and were indicated as 2012M VR (2012: year 2012; M: NFREC; VR: visual rating), 2013M VR (2013: year 2013; M: NFREC; VR: visual r ating), 2014M VR (2014: year 2014; M: NFREC; VR: visual rating), 2012C VR (2012: year 2012; C: PSREU; VR: visual rating), 2013C VR (2013: year 2013; C: PSREU; VR: visual rating), 2012M IS (2012: year 2012; M: NFREC; IS: immunostrip) and 2013M IS (2013: yea r 2013; M: NFREC; IS: immunostrip).

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118 Multi Site Analysis (Univariate M odel) Multi site analysis was done by using ASReml 3.0. Two variables (visual rating and immunostrip testing) were fitted to the following model separately: It is an implicit model, w vector of site effects; bs is the fixed vector of blocks within site effects; gs is the random vector of genotype effects within site, with gs ~ MVN(0, A G ); and e is the random vector of errors, with e~MVN(0, R ). The letters X 1 X 2 and Z represent incidence matrices The matrix A is the numerator relationship matrix obtained from pedigree and G is a matrix of variance covariance relationships between genotypes across sites and was calc ulated using the US parameterization. is the Kronecker or direct product. R represent the residual structures and row/column information was inc orporated with AR(1) structures for each sites. Three multi site analyses were conducted. First, the variable is visual rating and data from five different environments were included, 2012NF VR (2012: year 2012; NF: NFREC; VR: visual rating), 2013NF VR (2013: year 2013; NF: NFREC; VR: visual rating), 2014NF V R (2014: year 2014; NF: NFREC; VR: visual rating), 2012PS VR (2012: year 2012; PS: PSREU; VR: visual rating) and 2013PS VR (2013: year 2013; PS: PSREU; VR: visual rating). This analysis was indicated as VR 5. The following analyses used immunostrip testing as the variable: 2012NF IS (2012: year 2012; NF: NFREC; IS: immunostrip) and 2013NF IS (2013: year 2013; NF: NFREC; IS: immunostrip) and were labeled the IS 2 dataset. The last analysis was 2012NF VR (2012: year 2012; NF: NFREC; VR: visual rating) and 201 3NF VR (2013: year 2013; NF: NFREC; VR: visual rating) and were labeled as VR 2 analysis.

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119 Bi variate Analysis (Multi variate M odel) A bivariate model was conducted in order to estimate the type A genetic correlation between two different measuring methods (visual rating and immunostrip testing). Linear mixed models were performed by ASReml 3.0 by fitting the two measuring methods simultaneously. Only the plant samples at NFREC in 2012 and 2013 were analyzed by both visual rating and immunostip, so only thes e data were fitted to the bivariate model below: where y is the data vector of visual ratings and immunostrip test results simultaneously; m is the vector of fixed effects, e.g. different measuring methods; bm is the fixed vector of block effects within measuring method; gm is the random vector of genotype effects within measuring method, with gu~MVN(0, A G ) and e is the random vector of errors, with e~MVN(0, R ). The letters X 1 X 2 and Z represent incidence mat rices. The detail s of G matrix and R matrix are as same as the matrix described in univariate model. There were two bivariate analyses performed. One utilized 2012 visual rating data at NFREC and was labeled 2012NF. Another was 2013 data at NFREC and was labeled 2013NF. Heritability Heritability was estimated by different variance components and Restricted Maximum Likelihood (REML) techniques produced by ASReml through linear mixed model approach. For single site analysis, heritability was calcu lated using the formula: Where was the variance component for genotype and was the residual variance component.

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120 On multiple site analysis, the formula for heritability estimation was: Where was the variance component for genotype; was the residual variance component and was the variance component of genot ype B y environment interaction. The variance components were directly obtained from the fitted model. Genetic C orrelation There were two types of genetic correlation calculated in this study: typ e A and type B correlation. Type A genetic correlation is traditional genetic correlation. It calculated the correlation between two traits measured on the same experimental unit. By using bivariate analysis through REML estimation, the genetic correlation s between visual rating and immunostrip testing were obtained. Type A correlation was calculated as: Where is the estimated genotypic covariance between visual rating, x and immunostrip testing, y. is the estimated genotypic variance of visual rating and is the estimated genotypic variance of immunostrip testing. Type B correlation used the measures on the same trait but in different environmental units. By performing multi site analysis through R EML estimation, the variance component were obtained and Type B correlation was estimated by the corresponding variance component as:

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121 Where is the estimated genotypic variance for visual rating and immunostrip testing. is the interaction between geno types and various environments. Best Linear Unbiased Prediction (BLUP) Breeding value (BV) of the disease rating of individual genotypes were obtained from the linear mixed model as b est linear unbiased prediction (BLUPs). Full pedigree information of two parental lines, F 2 F 3 F 4 and F 5 populations were incorporated for accuracy. BLUPs for breeding value were estimated by single site analysis through linear mixed model. Visual rating and immunostrip testing were analyzed separately. The average breeding value from BLUP of F 2:3 (year 2012), F 2:4 (year 2013) and F 2:5 (year 2014) were obtained by calculating all the Results Heritability Single site analysis Heritability of visual rating was estimated through single site analysis at the five different environments. According to the Chapter 3 phenotyping results, the intensity of epidemics as evaluated visually varied considerably among five tests and the dise ase pressure at NFREC was usually higher than at PSREU. Heritabilities of the five tests were different and ranged from 0.13 to 0.64. If comparing the same year between two locations, in 2012, at PSREU the heritability was 0.50; however, it was 0.64 at N FREC In 2013, heritability was 0.13 at PSREU and it was 0.49 at NFREC (Table 4 3). The location effect played an important role in the spotted wilt epidemics. Environments with higher disease ratings and immunostrip infection frequency as expected, te nded to have

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122 higher heritability. Heritability was determined by variance components through the linear mixed model analysis and according to equations listed above residual variance and genetic variance contributed to heritability estimation. Low spotte d wilt incidence at PSREU resulted in a small proportion of genetic variance. For example, the genetic variances at PSREU were smaller than NFREC in 2012 and 2013 (Table 4 3) The genetic variance was only 0.02 in 2013 PSREU, seven times smaller than the v ariance at NFREC in the same year. The disease pressure is a critical factor affecting heritability because environmental variation was proportionally greater than genetic variation when the disease pressure is low. The epid emics varied from year to years; however, at NFREC the disease pressure was more consistent than at PSREU Heritability of spotted wilt based on immunostrip data was estimated through single site analysis in two environments (Table 4 4). The experiment was conducted at NFREC only beca use of the historically high disease severity. Heritability was higher in 2012 (0. 80 ) than in 2013 (0.54), but standard errors in both years were similar (0.06 and 0.05). In 2012, the genetic variance was two times higher (0.08) than in 2013 (0.04) and res ulted in higher heritability in 2012 than in 2013 Comparing the two methods of disease assessment in single site, heritability was generally higher with immunostrip testing than visual rating, because the residual variances were smaller (0.02 and 0.03). Immunostrip testing appears to be a more accurate method than visual rating in assessing spotted wilt and it could effectively diminish environment error. Multi site analysis The heritability estimated through multi site analysis ( Table 4 5 ) ranged f rom 0.39 to 0.69. The visual rating (five environments, VR5) included the data from 2012 PSREU, 2013 PSREU, 2012 NFREC, 2013 NFREC and 2014 NFREC. The heritability was 0.39, the lowest among three analyses (VR5, VR2 and IS2) Another visual rating analysis (VR2) included only

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12 3 2012 NFREC and 2013 NFREC datasets and was conducted to compare with immunostrip testing, because only two environments (2012 NFREC and 2013 NFREC) were used for immunostrip evaluation. The heritability was 0.50, slightly higher than VR5. The error variances in VR5 (0.14) and VR2 (0.13) were similar, but the genetic variance was higher in VR2 (0.10) than in VR5 (0.09). Immunostrip testing occurred in the 2012 NFREC and 2013 NFREC environments. Heritabilit y is highest (0.69) in IS2, because of relatively low error variance (0.03) compared to generic variance (0.06). This implies that the variance of the data based on immunostrip method had an reduced environmental error. Genetic C orrelation T ype A correl ation ( r A ) T ype A genetic correlation was calculated between the two measurement methods in the same environment by using bivariate analysis. Because immunostrip testing was conducted only in 2012 and 2013 NFREC, t ype A correlations were estimated in 2012 and 2013 only The correlation coefficients were high more than 0.9 in both years (Table 4 6) and a slightly higher in 2012 (0.9989) than in 2013 (0.9204). High correlations indicated that two measurement methods were highly in agreement T ype B correlation ( r B ) Multi site analysis was conducted to obtain t ype B genetic correlation and three analyses (VR5, VR2 and IS2) were performed. T ype B correlation was measured at different environments on the same individuals and the same measurement method s. The correlation efficiencies among VR5, VR2 and IS2 ( Table 4 7 ) ranged from r B =0.75 to r B =0.96 and all the values were high. The visual rating in five environments had the lowest correlation (0.75). In contrast, the correlation was highest ( r B =0.96) b etween two environments, VR2. Immunostrip testing had an intermediate to high correlation value (0.84). High correlation means low G*E

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124 interaction and indicates that the ranking of genotypes in a certain environment were similar in different environment. Best Linear Unbiased Prediction (BLUP) Visual rating The breeding value was predicted by BLUP Breeding values of visual rating at five different environments showed that i n 2012, the average breeding value was 0.18 at PSREU, and 0.06 at NFREC (Table 4 8). The smaller breeding value indicated the lower visual rating score Thus the resul t s meant that the average rating score at PSREU was smaller than that at NFREC. Historically, the disease pressure at PSREU is lower than NFREC and BLUPs resul ts supported the statement. The maximum and minimum breeding values at PSREU were both smaller than the corresponding values at NFREC In 2013, the average breeding value was 0.04 at PSREU and 0.06 at NFREC (Table 4 8) However, the breeding value range at PSREU (0.50) was much smaller than at NFREC (1.4). The maximum breeding value a t PSREU was 0.28 and at NFREC was 0.78; the minimum value at PSREU was 0.22 and at NFREC was 0.62 The data indicated the disease pressure at PSREU was very low, so the rating range was narrow. Over three years at NFREC (2012, 2013 and 2014) breeding values were similar among maximum, minimum, range and average breeding values indicating that the sported wilt epidemics at NFREC were consistent between different year s At PSREU 2013 disease pressure was quite low and even in 2012, the pressure was lower than that at NFREC. Most of the breeding values predicted from various populations in different years were between the breeding values of two parental lines ( Florida EP TM ). Only few lines showed transgressive segregation (data not shown).

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125 Immunostrip testing Breeding values from multi site analysis using the data from immunostrip testing in 2012 and 20 13 showed on Table 4 9. The values were the relative value s to the average of the population. In 2012, the average breeding value was 0.000594 and in 2013, it was 0.000216. The maximum and minimum breeding values in 2012 were 0.51 and 0.47, respectively. In 2013, the maximum and minimum values were 0.37 and 0.36, respectively. The range of breeding values in 2012 was 0.97 and in 2013 was 0.73. The data in both ye ars were quite similar and indicated the consistent spotted wilt epidemic at NFREC measured by both visual rating and immunostrip testing methods. Similar to the visual rating results, most of the breeding values predicted in 2012 and 2013 by immunostrip testing were between the values of two parental lines ( Florida EP TM and Georgia Valencia ). Only few lines showed transgressive segregation (data not shown). Discussion He ritability I nflation Variation of heritability estimates in this study could ha ve been due to several reasons. First, the F 3 generation had enough seed for only one replication in 2012 at both locations, however, there were two replications in 2013 and 2014. The number of replication can explain why the standard errors in 2012 in bot h locations were two times higher than other years. Since there was one replication in 2012, part of the environmental error was not estimated which can lead to inflation of heritability ( Ho lland et al., 2003; Visscher et al., 2008) T he proportion of variance components where residual variance should account for shift to genetic variance thus heritability was overestimated The heritabilities estimated in 2012 were 0.50, 0.63 and 0.80 relatively higher than the heritabilities in 2013 which were 0.13, 0.49 and 0.54.

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126 A second reason for variation in heritability estimates is variation in environmental and pathogenic factors that affect spotted wilt. In this study, t he spotted wilt epid emic varied among testing locations and seasons. Higher disease pressure usually results in more accurate resistant breeding lines from a population, and are typically more uniform over the test site (Venuprasad et al., 2007) According to the disease triangle, the three essential factors for disease development are host, pathogen, and environment. For a given crop, the disease situation is based on the interaction of these three fa ctors (Scholthof, 2007) At a lower disease pressure environ Thus experiments need to be conducted in a manner favoring spotted wilt development or to create a favorable environment to ensure disease development. Based on the Peanut Rx (Culbreath et al., 2003; 2010) the field management situations which favor spotted wilt development, are low plant stand (less than four plants per foot of row), early planting (prior to May 1), single row pattern (twin rows reduce risk compared to single rows), no phorate insecticide According to these factors, our experiments were at high risk of developing spotted wilt. The pathogen for peanut spotted wilt disease is nearly impossible to manipulate in the field. The tests relied on natural inoculation which is dependent on thrips (the vector) and TSWV population (the pathogen) (Mandal et al., 2001) Historically, PSREU has had lower spotted wilt disease pressure than NFREC. Spotted wilt is transmitted only by thrips and there are two major th rip s vectors: western flower thrips and tobacco thrips. The population of western flower thrips is abundant in north Florida (NFREC), however, less in central Florida (PSREU) (Childers and Nakahara, 2006) On the other hand, TSWV need hosts to continue their life cycle. In north Florida, more row crops are planted in the region, like peanut, cotton, tomato an d tobacco and

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127 they could become the host of TSWV and maintain the virus population (Reitz, 2002) In central Florida, the major crop is warm season grass pasture which provides less host options for TSWV in the region (Kucharek et al., 1990) Under low risk environment, even susc eptible are lacking in the disease triangle. In such a situation, true resistant and fake resistant plants are indistinguishable even though the genetic fact ors (resistant and susceptible genotypes) are presented in the population. A third reason in accurate heritability estimation is the ability to measure disease level accurately. Immunostrip is a sensitive tool to track precisely the viral accumulation in root crown and can alleviate the misjudgment based only on the visual symptom. Susceptible lines which appear resistant in visual evaluation could be infected, but remain asymtomatic (Chapter 3; Kresta et al., 1995; Mckinney, 2013) The reason that some plants become infected with TSWV, but fail to express disease is unknown, most likely could be due to low virus titer. Immunostrip testing had relatively higher heritability as compared to visual rating, because it can identify infected genotypes which were a symptomatic. This allows capturing more genetic va riance among different genotypes and diminishes the environmental variance errors. T ype A Correlation/ T ype B C orrelation Both phenotypic and genotypic correlations explain s the relationship between two traits. Phenotypic correlation may be caused by gene tic and/ or environmental factors and include covariance of residuals and non addictive genetic effects. Genotypic correlation usually refers to addictive effect correlation (correlation of breeding values) (Falconer, 1960) and is cal culated using only the genotypic variances/covariances (Weber and Moorthy, 1952; Falconer, 1960) Genetic correlation can be estimated by a linear mixed model approach, similar to the mating designs conducted for the estimation of genetic variance components (Falconer, 1960; Lynch and

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128 Walsh, 1998) When two traits have a certain degree of genetic correlation, the selection for one trait will cause a response to the other trait. It is helpful when the correlated trait is easier to measure or has a higher heritability than the trait of interest. Under these conditions, indirect selection could be more effective than direct selection (Cooper et al., 1997; Zhao et al., 2006) Although visual rating and immunostrip testing are two different measurement me thods, they estimated the same trait: spotted wilt resistance. The high genetic correlation between two measurement methods (0.99 and 0.92 in 2012 and 2013, respectively) indicated a high consistency of the rankings of genotypes across the measurement meth ods. Comparing the phenotypic correlation results in chapter three (0.58 and 0.79 in 2012 and 2013, respectively), the differences between phenotypic and genotypic correlation were caused by environmental variation It is difficult to accurately measure th e spotted wilt due to a high level of environmental variation Differences in heritability estimates from the two measurement methods strengthen this viewpoint. The heritability was 0.69 in immunostrip testing and 0.44 in visual rating by mulit site analys is. In immunostrip testing, the genetic variance component was three times higher than error variance, however, in visual rating, the genetic variance was lower than error variance. Visual rating can only measure symptom development but cannot evaluate asy mptomatic infection that is common in spotted wilt of peanut. In contrast, Immunostrip can identify susceptible genotypes with whether symptomatic or not. This capability helps to reduce environmental error. The traditional genetic correlation mentioned above is called t ype A correlation. T ype B genetic correlation reveals the genot ype B y environment (G*E) interaction among different environments. G*E interactions are often observed when conducting multiple environment experiments (Culbreath et al., 1997; Tillman et al., 2007) High t ype B correlatio n in this study

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129 (0.75, 0.84 and 0.96) indicated very small G*E interaction which means that the performance of genotypes was relatively consistent in different environments. The environment (soil, weather and cropping systems) are very distinct between Cen tral Florida (PSREU) and northern Florida (NFREC), however, there was good consistency of genotype ranking for spotted wilt. This suggests suggested that breeders can make a selection based on experiments carried in a single site without much loss of genetic gain loss on the other site (Silva et al., 2014) However, the additional sites testing can afford more reliable G*E interaction estimations and would be advisable for breeders before making the selection decision (Whitaker et al., 2012) Breeding V alue (BLUPs) Breeding values were predicted by BLUP to help select superior breeding lines in breeding programs (Piepho et al., 2008) In self pollinated crops, the additive variances increase with selfing at every ge neration (Holland et al., 2003) Most breeding values among the segregating population fel t between the breeding value s of the two parental lines. Only few of transgressi ve segregants were observed. This can be explained by the additive model (Falconer, 1960) The resistant parent, Florida EP TM resistance and is therefore a good candidate to be utilized as one of the crossing parents for improving cultivars with resistance to spotted wilt. In this study, the breeding values for spotted resistance a t NFREC had lower minimum values than at PSREU. It meant at NFREC, if breeders can precisely conduct selections, the selection will be more efficient than PSREU and can obtain the resistant individuals with better breeding values in next generations. A few lines consistently showed excellent breeding value results and these lines were tested by two flanking markers (GM672 and AHGS4584) linked to the spotted wilt resistance QTL (Chapter 3). These lines with the best breeding value in the populations displaye d resistant band pattern on both flanking markers (data not shown). The

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130 breeding value prediction had a corresponding result to molecular marker, the genomic technology. S everal studies reported successful utilization of BLUPs to predict and select super ior progenies (Panter and Allen, 1995a; b ; Bernardo, 1996; Pattee et al., 2001; Bauer et al., 2006; Crossa et al., 2006; Chiorato et al., 2008; Reif et al., 2013; Paynter et al., 2014) in other disease and agronomic traits. Accurate selection from various candidate genotypes is a critical step in identifying superior cultivars. Breeding values determine the response from selection and BLUP methods can give breeders more i n formation for the selection process (Milla Lewis and Isleib, 2005) The Applicat ion in H eritability The three stages in plant breeding include 1) creating or assembling variable germplasm pools, 2) selecting superior individuals from the pool and 3) utilizing selected individuals to develop a superior cultivar. An understanding of genetic variance and heritability is useful in all the stages of the breeding process (Dudley and Moll, 1969) In breeding programs, heritability is critical because it d etermine s the response to selection that can be expected when plant breeders make selections. Heritability can predict the genetic values, especially breeding values, precisely and give breeders information to design appropriate breeding schemes (Visscher et a l., 2008) Heritability information for spotted wilt resistance is important, especially for the resistance found in Florida EP TM Heritability in Genomic E ra Results from this study demonstrated that : 1) immunostrip testing is a better measureme nt method than visual rating for spotted wilt in peanut, because it can capture more genetic variance and decrease the environmental errors, and 2) spotted wilt resistance found in Florida EP TM is a heritable trait and has a moderatly high heritability (0.69). This indicates an excellent

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131 prospect for genetic gain H owever, immunostrip testing is a very expensive technique and is not practical to apply in a large scale breeding program. Genetic markers should be a cheaper alternative an d are not subject to environmental variation common with spotted wilt. Many genomic tools and resources have been developed in peanut recently allowing for rapid development of molecular markers that are the bridge to connect genotype and phenotype (Visscher et al., 2008) In the genomics era, heritability has applied from different perspectives, but still it is a means to help breeders measure the importance of genetic effects across population (Visscher et al., 2008) .. In peanut, spotted wilt epidemics ar e variable from season to season When disease pressure is low, it is difficult for breeders to make selections accurately when symptoms are scarce. The results of this study show that the heritability of spotted wilt resistance is moderate to high when us ing Florida EP TM as the source of resistance so that considerable progress can be made if the resistance can be transferred to commercially acceptable cultivars. One major QTL has been identified on peanut A01 chromosome and the flanking markers of the QTL can be utilized for breeding purposes (see chapter3). The traditional selection in spotted wilt resistanc is based on visual symptoms. Since the disease severity increases with time until the epidemic reach the final intensity ( Culbreath et al., 1997; Murakami et al., 2006) breeders always make selection late during the season in order to obtain a certain degree of disease intensity. The molecular makers linked to spotted resistance will allow breeders to make selections at e arly stages and eliminate the susceptible individuals in advance, which can save a lot of labor, money, time, and space. Another advantage of molecular markers is that they can be utilized under low or no disease pressure conditions. Since spotted wilt evaluation relies on natural inoculation, disease

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132 epidemics fluctuate temporally and spatially making it difficult to accura tely assess resistance in breeding material and subsequent cultivar releases. With marker assisted selection, susceptible lines can be eliminated earlier in the breeding program. However, the accuracy of maker selection depends on the genetic proximity of flanking markers and target genes. If crossover occurs between markers and resistance genes, then false resistant plants will be chosen that do not give the expected outcome. Before breeders release a cultivar, disease resistance is a critical evaluation f actor (Sleper and Poehlman 2006) so some level of phenotypic verification is needed to accurately characterize the resistance of peanut cultivars to spotted wilt. Dynamic H eritability In this study, heritability of resistance to spotted wilt was moderate to high (0.69) by im munostrip testing and moderate (0.43) by visual rating. Heritability estimates could be higher if experiment errors can be reduced by some modifications like increased replication, better experimental design, and decrease sampling errors during data colle ction (Whitaker et al., 2012) In this study, the experimental design was randomized complete block design (RCBD). It was better than what occurred in the normal breeding process, so heritability within the breeding program will likely be lower t han this study. Heritability is dynamic and represents a combination of experimental, environmental and management conditions in a given population (Silva et al., 2014) It is common to have various heritability values reported even when populations have a high degree of similarity (Lynch and Walsh, 1998; Holland et al., 2003) In theory, heritability changes over time because genetic variance changes due to inbreeding and environmental variance changes due to field management and uncontrollable environmental factors. Interestingly, in practice, similar traits often sho w similar heritability across populations in the

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133 same species or even across species (Visscher et al., 2008) The morphological traits (non fitness traits) tend to have higher heritability than fitness related traits (Visscher et al., 2008) When using REML under most conditions, the individual values from plots produce higher heritability estimates than using the plot means (Huber et al., 1994) In present study, plot means instead of individual plants were utilized to conduct the analysis. Si nce spotted wilt epidemics are variable from season to season, if the experiment unit is every single plant by of plot mean) is the alternative way to estimate heritability. The cost of immunostrip testing on every individual plants is extremely high and the mean from ten randomly selected plants per plot is a better method. In self pollinated crops, selected F 2 i ndividuals form F 3 families and selection is based on whole family performance. Then, selected F 3 families become F 4 families. Every family traces back to a single F 2 plant. In a practical peanut breeding program, every plot is a family and the plot mean c an represent the whole family performance for spotted wilt resistance. Conclusion Two different measurement methods were utilized to estimate the heritability over different years and locations. Heritability estimated by immunostrip was 0.69 and visual ra ting wa s 0.43. Given these relatively high heritability estimates, genetic gain for resistance to spotted wilt is expected to be high Additionally, introgression of the resistance in Florida EP TM to other cultivars would have a major impact on peanut production in areas where spotted wilt is problematic. Very high t ype A genetic correlation indicated the heritability difference between two measurements is due to environment errors. Immunostrip testing is a promising method to accurately measure spotted wilt since it can detect asymptomatic infection by targeting virus coat

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134 protein. High t ype B genetic correlation indicated low genotype by environment interaction. Breeders can make selections in a single si te without much loss in genetic gain. Few transgressive segregants were observed in the population according to breeding value information predicted by BLUPs The p arental line, Florida EP TM showed excellent breeding value prediction across differen t years and locations. Florida EP TM should be widely utilized to create excellent spotted wilt resistance in different cross combinations for selection. The breeding value for other agronomic traits could be predicted in the future I t might become a nother determinate factor for breeders in making selection. In the genomic era, molecular genetic tools can have significant impact on plant breeding. Flanking markers linked to spotted wilt resistance could assist breeders in making selections regardles s of the disease epidemic and at early stages to save money, labor, time, and space.

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135 Table 4 1. The number of plot, replication and check at different sites in F 3 F 4 and F 5 populations. Year Number Site PSREU NFREC Plot number 210 210 2012 (F 2 :3 ) Replication number 1 1 Check number 21 21 Plot number 384 384 2013 (F 2:4 ) Replication number 2 2 Check number 55 55 Plot number 384 2014 (F 2:5 ) Replication number 2 Check number 56 Table 4 2. The information about sites and phenotyping methods on F 3 F 4 and F 5 populations. Method Site PSREU NFREC Visual rating 2012 (F 2:3 ) 2012 (F 2:3 ) 2013 (F 2:4 ) 2013 (F 2:4 ) 2014 (F 2:5 ) Immunostrip testing 2012 (F 2:3 ) 2013 (F 2:4 ) Table 4 3. Genetic variance, error variance and heritability estimated by single site analysis of visual rating from PSREU and NFREC in 2012, 2013 and 2014. Site Year Genetic Variance Error Variance Heritability Standard Error Replication PSREU 2012 0.12 0.12 0.50 0.13 1 2013 0.02 0.12 0.13 0.05 2 NFREC 2012 0.15 0.08 0.64 0.11 1 2013 0.12 0.12 0.49 0.05 2 2014 0.13 0.16 0.46 0.05 2

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136 Table 4 4. Genetic variance, error variance and heritability estimated by single site analysis of immunostrip testing from NFREC in 2012 and 2013. Table 4 5. Genetic variance, error variance and heritability estimated by multiple site analysis of visual rating and immunostrip testing at different environments. Measurement method Genetic Variance Error Variance Heritability Visual rating (five environments, VR5) 0.09 0.14 0.39 Visual rating (two environments, VR2) 0.10 0.13 0.44 Immunostrip testing (two environments, IS2) 0.06 0.03 0.69 Table 4 6. Type A correlation estimated by bivariate analysis between visual rating and immunostrip testing from NFREC in 2012 and 2013. Year T ype A correlation 2012 0.9989 2013 0.9204 Table 4 7. Type B correlation estimated by multiple site analysis of visual rating and immunostrip testing at different environments. Measurement method T ype B correlation Visual rating (five environments, VR5) 0.75 Visual rating (two environments, VR2) 0.96 Im munostrip testing (two environments, IS2) 0.84 Site Year Genetic Variance Error Variance Heritability Standard Error Replication NFREC 2012 0.08 0.02 0.80 0.06 1 2013 0.04 0.03 0.54 0.05 2

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137 Table 4 8. Maximum, minimum, range and average breeding value (BLUP) predicted by visual rating at PSREU and NFREC in 2012, 2013 and 2014. Site Year Breeding value (BLUP) Maximum Minimum Range Average PSREU 2012 0.47 0.50 0.98 0.18 2013 0.28 0.22 0.50 0.04 NFREC 2012 0.60 0.44 1.04 0.06 2013 0.78 0.62 1.40 0.06 2014 0.75 0.52 1.27 0.03 Table 4 9. Maximum, minimum, range and average breeding value (BLUP) predicted by immunostrip testing at NFREC in 2012, and 2013. Site Year Breeding value (BLUP) Maximum Minimum Range Average NFREC 2012 0.51 0.47 0.97 0.00059 2013 0.37 0.36 0.73 0.00022

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138 Figure 4 1. Three prefilled Agdia sample bags with three negative (single red line) results for TSWV. Photo taken by Yu Chien Tseng. Figure 4 2. Three prefilled Agdia sample bags with three positive (two red line) results for TSWV. Photo taken by Yu Chien Tseng.

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139 CHAPTER 5 SUMMARY The purpose of this study was to investigate and understand the genetic basis of spotted wilt resistance in the peanut cultivar, Florida EP TM Three specific objectives included: first, to evaluate the spatial and temporal viral abundance in Florida EP TM in comparison with other peanut cultivars through immunoassays; second, to map the genetic components controlling spotted wilt resistance in Florida EP TM and to find the molecular makers linked to QTLs controlling TSWV resistance for furth er application in breeding programs; and the last, to estimate the heritability of disease resistance in Florida EP TM for breeders to efficiently conduct selection. Florida EP TM Ti llman and Gorbet, 2012 ) and showed a better resistance than other resistance varieties (Mckinney, 2013) Immunosstrip testing was conducted to detect virus within the plant s, since foliar symptomology may not always represent disease incidence Foliar symptoms may underestimate the actual viral infection. In this study, immunostrip testing was used as an important tool for detecting virus presence compared to the traditiona l visual rating method. The spatial and temporal viral abundance in Florida cultivars were tested by immunostrip in various plant tissues and different time points. Infection was severe in later growth stages. R oot crown had the highest infection frequency among four tissue types Florida EP TM has the lowest infection frequency among four cultivars These results have been reported previously ( Tillman and Gorbet, 2012 ; Mandal et al. 2002; Mckinney, 2013; Murak ami et al., 2006; Rowland et al., 2005) and were further confirmed using immunoassays in this study

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140 The mechanism of resistance in Florida EP TM does not appear to relate to hypersensitive response ( Sw 5 in tomato and Tsw in pepper) or non preference by thrips (avoidance). Comparing Florida EP TM to the other cultivars, the detection of TSWV was delayed (60 days delayed) and the frequency of infected plants was reduc ed (80% reduction). The reasons for delay might be due to the interference of virus transmission from virus vectors and the reduction in frequency could be a result of a restriction in movement within the plants. To map the genetic components controlling sp otted wilt resistance in Florida EP TM (Chapter 3), a segregating population derived from cross between Florida EP TM and a susceptible cultivar Georgia Valencia, were thoroughly phenotyped and genotyped. Visual rating and immunostrip testing we re conducted for phenotyping. Visual rating has a narrow disease score scale (1 to 10) and the discrete scales might not be able to represent the diverse variability of single F 2 individuals. Immunostrip testing can capture virus more accurately, so can se parate the resistant and susceptible plants more precisely. According to immunostrip phenotypic data, the spotted wilt resistance might be control led by one single gene. SSR markers were used for genotyping the individuals in the segregating population. S creening of 2431 SSR markers located across the whole peanut genome, 329 were polymorphic markers between the two parental lines. Those polymorphic markers were used to further genotype a representative set of individuals in the segregating population. O nl y markers on A01 chromosome showed the co segregation between genotype and phenotype, so marker density was enriched at A01 chromosome. A linkage map with 23 makers on A01 chromosome was constructed and it showed a good collinearity between linkage map and physical map. Combined with phenotype data, a major QTL was identified on A01 chromosome. The QTL had up to 22.7% PVE and 9.0 LOD value. Two flanking markers, AHGS4584 and

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141 GM672, were linked to this spotted wilt resistant QTL which can be used for furthe r application in practical breeding program. Heritability can determine the potential of a population to respond to selection and it is important information for breeders when mak ing selections. Previously, a wide range of spotted wilt resistance heritab ility (0.01 to 0.71) was reported and the majority was at low to medium range (Baldessari, 2008 Chapter 4 ). The previous experiment was conducted on single plant individuals by visual rating. In order to reduce the environmental errors, immunostrip testin g was included on plot basis evaluation. The heritability estimated based on immunostrip results was 0.69 and was much higher than the heritability estimated based on visual rating, 0.40. High er heritability one could obtain large r genetic gain at every selection cycle. Immunostrip testing minimized the environment al errors causing by seasonal impacts and variation in spotted wilt symptomology due to low infection frequency and therefore produced a more reliable and accurate herita bility. Both methods displayed a high type B ( r B ) correlation (0.82 and 0.75) indicating lower genotype by environment interaction and consistency of the parental rankings across all sites evaluated. Breeding selection can be made by evaluating in fewer lo cations. In summary, the resistance in Florida EP TM delay ed the detection of TSWV and reduce d the frequency of infected plants. According to phenotyp ic data, the genetic component might be controlled by one gene. Supporting this hypothesis is the fa ct that o ne major QTL has been identified on A01chormosome. The resistance is heritable and this trait has potential for large genetic gain in spotted wilt resistance Under high disease pressure, breeders can easily make selections by visual rating, beca use the phenotypic differences between resistant and susceptible plants are obvious. The

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142 disease pressure, breeders cannot rely on visual rating and immunostrip tes ting becomes a good alternative method to conduct selection. If breeders can make selections at early stages, no matter the dissesase preesure, they can save a lot of labor, money, time, and space. The molecular markers developed from chapter 3 can help to solve these problems. In chapter 4, high heritability indicated the possibility of high genetic gain for spotted wilt resistance from each breeding cycle. Florida EP TM is a promising cultivar with a high level of spotted wilt resistance and can be used in crosses for further cultivar development. The resistance appears to be relatively simply inherited with high heritability estimated by immunostrip but with significant seasonal variability. Therefore, molecular markers can help to avoid uncertain environment al impacts and assist in selection

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143 LIST OF REFERENCES Ashikari, M., and M. Matsuoka. 2006. Identification, isolation and pyramiding of quantitative trait loci for rice breeding. Trends Plant Sci. 11(7): 344 350. Atabekov, J.G., and Y.L. Dorokhov. 1984. Plant virus specific transport function and resistance of plants to viruses. Academic Press. Baldessari, J.J. 2008. Genetics of tomato spotted wilt virus resistance in peanut. Bandillo, N., C. Raghavan, P.A. Muyco, M.A.L. Sevilla, I.T. Lobina, C.J. Dilla Ermita, C. W. Tung, S. McCouch, M. Thomson, and R. Mauleon. 2013. Multi parent advanced generation inter cross (MAGIC) populations in rice: progress and potential for genetics r esearch and breeding. Rice 6(1): 11. Barrientos Priego, L., T.G. Isleib, and H.E. Pattee. 2002. Variation in Oil Content Among Mexican and Peruvian hirsuta Peanut Landraces and Virginia Type hypogaea Lines 1. Peanut Sci. 29(1): 72 77. Bauer, A.M., T.C. Reetz, and J. Lon. 2006. Estimation of breeding values of inbred lines using best linear unbiased prediction (BLUP) and genetic similarities. Crop Sci. 46(6): 2685 2691. Bernardo, R. 1996. Best Linear Unbiased Prediction of Maize Single Cross Performance Bertioli, D.J., M.C. Moretzsohn, L.H. Madsen, N. Sandal, S.C.M. Leal Bertioli, P.M. Guimares, B.K. Hougaard, J. Fredslund, L. Schauser, and A.M. Nielsen. 2009. An analysis of synteny of Arachis with Lotus and Medicago sheds new light on the structure, stability and evolution of legume genomes. BMC Genomics 10(1): 45. Bertrand, P.F. 1998. 1997 Georgia plant disease loss estimates. Univ. Ga. Coop. Ext. Pub. Pathol. 81: 98 107. Black, L.L., H.A. Hobbs, and J.M. Gatti Jr. 1991. Tomato spotted wilt virus r esistance in Capsicum chinense PI 152225 and 159236. Plant Dis. 75(8). Black, M.C., and D.H. Smith. 1987. Spotted wilt and rust reactions in south Texas among s elected peanut genotypes. Proc. Am. Peanut Res. Ed. Soc : 19 Boiteux, L.S., and A.C. De Avila. 1994. Inheritance of a resistance specific to tomato spotted 2): 139 142. Bolger, A.M., M. Lohse, and B. Usadel. 2014. Trimmomatic: a flexible trimmer for Illumina seque nce data. Bioinformat ics 1126.

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144 : 806. a 01R'peanut. Crop Sci. 42(5): 1750. 178. Briggs, F. N., and P.F. Knowles. 1977. Introduction to plant breeding. Briggs and Knowles. Brown, S. 1999. Tomato spotted wilt of peanut: Identifying and avoiding high risk situations. Cooperative Extension Service, the University of Georgia College of Agriculture. Brown, S.L., J.W. Todd, and A.K. Culbreath. 1995. Effect of selected cultural practices on incidence of tomato spotted wilt virus and populations of thrips vectors in peanuts. Tospoviruses Thrips Flor. Veg. Crop. 431: 491 498. Buckler, E.S., J.B. Holland P.J. Bradbury, C.B. Acharya, P.J. Brown, C. Browne, E. Ersoz, S. Flint Garcia, A. Garcia, and J.C. Glaubitz. 2009. The genetic architecture of maize flowering time. Science 325(5941): 714 718. Burdon, R.D. 1977. Genetic correlation as a concept for stu dying genotype environment interaction in forest tree breeding. Silvae Genet. 26(5 6): 168 175. Chen, X., A. Culbreath, T. Brenneman, C. Holbrook Jr, and B. Guo. 2008. Identification and cloning of TSWV resistance gene (s) in cultivated peanuts and develo pment of markers for breeding selection. American Phytopathological Society Abstracts. Chen, X., M.L. Wang, C. Holbrook, A. Culbreath, X. Liang, T. Brenneman, and B. Guo. 2011. Identification and characterization of a multigene family encoding germin like proteins in cultivated peanut (Arachis hypogaea L.). Plant Mol. Biol. Report. 29(2): 389 403. Childers, C.C., and S. Nakahara. 2006. Thysanoptera (thrips) within citrus orchards in Florida: species distribution, relative and seasonal abundance within tre es, and species on vines and ground cover plants. J. Insect Sci. 6: 1 19. Chiorato, A.F., S.A.M. Carbonell, L.A.D.S. Dias, and M.D.V. De Resende. 2008. Prediction of genotypic values and estimation of genetic parameters in common bean. Brazilian Arch. Bio l. Technol. 51(3): 465 472. Chu, Y., C.L. Wu, C.C. Holbrook, B.L. Tillman, G. Person, and P. Ozias Akins. 2011. Marker Assisted Selection to Pyramid Nematode Resistance and the High Oleic Trait in Peanut. Plant Genome J. 4(2): 110

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145 Collard, B.C.Y., and D. J. Mackill. 2008. Marker assisted selection: an approach for precision plant breeding in the twenty first century. Philos. Trans. R. Soc. B Biol. Sci. 363(1491): 557 572. Cooper, M., R.E. Stucker, I.H. DeLacy, and B.D. Harch. 1997. Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci. 37(4): 1168 1176. Costa, A.S. 1941. Una molestia de virus de amendoim (Arachis hypogaea L.) A mancha anular. Biologico 7: 249 251. Crossa, J., J. Burgueo, P.L. Cornelius, G. McLaren, R. Trethowan, and A. Krishnamachari. 2006. Modeling genotype environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Sci. 46(4): 1722 1733. Cruz, S., A. Roberts, D. Prio r, S. Chapman, and K. Oparka. 1998. Cell to cell and phloem mediated transport of potato virus X. The role of virions. Plant Cell 10(4): 495 510. Cuc, L.M., E.S. Mace, J.H. Crouch, V.D. Quang, T.D. Long, and R.K. Varshney. 2008. Isolation and characteriza tion of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea). BMC Plant Biol. 8(1): 55. Culbreath, A., J. Beasley, B. Kemerait, E. Prostko, T. Brenneman, N. Smith, S. Tubbs, R. Olatinwo, R. Srinivasan, and B. Tillman. 2010. Peanut Rx: minimizing diseases of peanut in the southeastern United States. Beasley, JP: 81 96. Culbreath, A.K., A.S. Csinos, T.B. Brenneman, J.W. Demski, and J.W. Todd. 1991. Association of tomato spotted wilt virus wit h foliar chlorosis of peanut in Georgia. Plant Dis. 75(8): 863. Culbreath, A K., D.W. Gorbet, N. Martinez Ochoa, C.C. Holbrook, J.W. Todd, T.G. Isleib, and B. Tillman. 2005. High Levels of Field Resistance to Tomato spotted wilt virus in Peanut Breeding Lines Derived from hypogaea and hirsuta Botanical Varieties. Peanut Sci. 32: 20 24. Culbreath, A K., and R. Srinivasan. 2011. Epidemiology of spotted wilt disease of peanut caused by Tomato spotted wilt virus in the southeastern U.S. Virus Res. 159(2): 1 01 109 Culbreath, A K., B.L. Tillman, D.W. Gorbet, C.C. Holbrook, and C. Nischwitz. 2008. Response of New Field Resistant Peanut Cultivars to Twin Row Pattern or In Furrow Applications of Phorate for Management of Spotted Wilt. Plant Dis. 92: 1307 1312. Culbreath, A K., J.W. Todd, and S.L. Brown. 2003 Epidemiology and management of tomato spotted wilt in peanut. Annu. Rev. Phytopathol. 41(134): 53 75. Culbreath, A.K., J.W. Todd, J.W. Demski, and J.R. Chamberlin. 1992. Disease progress of spotted wilt i n peanut cultivars Florunner and Southern Runner. Phytopathology 82(7): 766 771.

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146 Culbreath, A K., J.W. Todd, D.W. Gorbet, F.M. Shokes, and H.R. Pappu. 1997. Field Response of New Peanut Cultivar UF 91108 to Tomato Spotted Wilt Virus. Plant Dis. 81(12): 14 10 1415. Dang, P.M., D.L. Rowland, and W.H. Faircloth. 2009. Comparison of ELISA and RT PCR assays for the detection of Tomato spotted wilt virus in peanut. Peanut Sci. 36: 133 137. Dellaporta, S.L., J. Wood, and J.B. Hicks. 1983. A plant DNA minipreparation: version II. Plant Mol. Biol. Report. 1(4): 19 21. Dudley, J.W., and R.H. Moll. 1969. Interpretation and Use of Estimates of Heritability and Genetic Variances in Plant Breeding1. Crop Sci. 9: 257. Elshire, R.J., J.C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E.S. Buckler, and S.E. Mitchell. 2011. A robust, simple genotyping by sequencing (GBS) approach for high diversit y species. PLoS One 6(5): 19379. Falconer, D.S. 1960. Introduction to quantitative genetics. DS Falconer. Ferguson, M .E., M.D. Burow, S.R. Schulze, P.J. Bramel, A.H. Paterson, S. Kresovich, and S. Mitchell. 2004. Microsatellite identification and characterization in peanut (A. hypogaea L.). Theor. Appl. Genet. 108(6): 1064 1070. Flor, H.H. 1942. Inheritance of pathogeni city in Melampsora lini. Phytopathology 32(653): 69. Fountain, J., H. Qin, C. Chen, P. Dang, M.L. Wang, and B. Guo. 2011. A Note on Development of a Low cost and High throughput SSR based Genotyping Method in Peanut (Arachis hypogaea L.). Peanut Sci. 38(2): 122 127. Gautami, B., D. Foncka, M.K. Pandey, M.C. Moretzsohn, V. Sujay, H. Qin, Y. Hong, I. Faye, X. Chen, and A. BhanuPrakash. 2012. An international reference consensus genetic map with 897 marker loci based on 11 mapping populations for tetrap loid groundnut (Arachi s hypogaea L.). PLoS One 7(7): 41213. German, T.L., D.E. Ullman, and J.W. Moyer. 1992. Tospoviruses: diagnosis, molecular biology, phylogeny, and vector relationships. Annu. Rev. Phytopathol. 30(1): 315 348. Gilmour, A R., B.J. Gog el, B.R. Cullis, and R. Thompson. 2009. ASReml user guide release 3.0. VSN Int. Ltd. Gilmour, A.R., R. Thompson, and B.R. Cullis. 1995. Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics: 1440 1450. 127.

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147 124. Gorbet, D.W., A.J. Norden, F.M. Shokes, and D.A. Knauft. 1987. Registration Gorbet, D.W., and F.M. Shokes. 1994. Plant spacing and tomato spotted wilt virus. p. 50. In Proc. Am. Peanut Res. Educ. Soc. p Sci. 42(6): 2207 a. 366 204. Gorbet, D.W., and B.L. Tillman. 2009. R 14. Groves, R.L., J.F. Walgenbach, J.W. Moyer, and G.G. Kennedy. 2002. The role of weed hosts and tobacco thrips, Frankliniella fusca, in the epidemiology of Tomato spotted wilt virus. Plant Dis. 86(6): 573 582. Gunning, B.E.S., and R.L. Overall. 1983. Plasmodesmata and cell to cell transport in plants. Bioscience 33(4): 260 265. Hagan, A.K., J.R. Weeks, R.T. Gudauskas, and J.C. French. 1991. Development of control recommendations for TSWV in pea nut. p. 52. Proc. Am. Peanut Res. Ed. Soc. Halliwell, R., and G. Philley. 1974. Spotted wilt of peanut in Texas. Plant Dis. Report. 58(1): 23 25. Harries, P., and B. Ding. 2011. Cellular factors in plant virus movement: at the leading edge of macromolecu lar trafficking in plants. Virology 411(2): 237 43 He, G., R. Meng, M. Newman, G. Gao, R.N. Pittman, and C.S. Prakash. 2003. Microsatellites as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 3(1): 3. Henderson, C.R. 1976. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics: 69 83. Hoffmann, K., S.M. Geske, and J.W. Moyer. 1998. Pathogenesis of Tomato Spotted Wilt Virus in Peanut Plants Dually Infected with Peanut Mottle Virus. Plant Dis. 82(6): 610 614. Peanut. J. Plant Regist. 2(2): 92.

PAGE 148

148 Holland, J.B., W.E. Nyquist, and C.T. Cervantes Martinez. 2003. Estimating and Interpreting Heritability for Plant Breeding.Pdf. Plant Breed. Rev. 22: 9 112 Huang, B.E., A.W. George, K.L. Forrest, A. Kilian, M.J. Hayden, M.K. Morell, and C.R. Cavanagh. 2012. A multiparent advanced generation inter cross population for genetic analysis in wheat. Plant Biotechnol. J. 10(7): 826 839. Huber, D.A., T.L. White, and G.R. Hodge. 1994. Variance component estimation techniques compared for two mating designs with forest genetic architecture through computer simulation. Theor. Appl. Gene t. 88(2): 236 242. Hull, R. 1989. The movement of viruses in plants. Annu. Rev. Phytopathol. 27(1): 213 240. Isleib, T.G., C.C. Holbrook, and D.W. Gorbet. 2001. Use of plant introductions in peanut cultivar development. Peanut Sci. 28(2): 96 113. Jones, R.K., and J.R. Baker. 1991. TSMV: symptoms, host range and spread. ARS US Dep. Agric. Agric. Res. Serv. Kelly, J.D., and P.N. Miklas. 1998. The role of RAPD markers in breeding for disease resistance in common bean. Mol. Breed. 4(1): 1 11. Khera, P., H. Wang, A.K. Culbreath, M.K. Pandey, R.K. Varshney, X. Wang, B. Liao, X. Zhang, J. Wang, and C.C. Holbrook. 2014. QTL mapping and quantitative disease resistance to TSWV and leaf spots in a recombinant inbred line population SunOleic 97R and NC94022 of pean ut (Arachis hypogaea L.). Adv. Arachis through Genomics Biotechnol. Kochert, G., T. Halward, W.D. Branch, and C.E. Simpson. 1991. RFLP variability in peanut (Arachis hypogaea L.) cultivars and wild species. Theor. Appl. Genet. 81(5): 565 570. Koilkonda, P., S. Sato, S. Tabata, K. Shirasawa, H. Hirakawa, H. Sakai, S. Sasamoto, A. Watanabe, T. Wada, and Y. Kishida. 2012. Large scale development of expressed sequence tag derived simple sequence repeat markers and diversity analysis in Arachis spp. Mol. Breed 30(1): 125 138. Kosambi, D.D. 1943. The estimation of map distances from recombination values. Ann. Eugen. 12(1): 172 175. Kresta, K.K., F.L. Mitchell, and J.W. Smith Jr. 1995. Survey by ELISA of Thrips (Thysanoptera: Thripidae) Vectored Tomato Spotted Wilt Virus Distribution in Foliage and Flowers of Field Infected Peanut 1. Peanut Sci. 22(2): 141 149. Kucharek, T., L. Brown, F. Johnson, and J. Funderburk. 1990. Tomato spotted wilt virus of agronomic vegetable, and ornamental crops. Circ. Coop. Ext. S erv.

PAGE 149

149 Kump, K.L., P.J. Bradbury, R.J. Wisser, E.S. Buckler, A.R. Belcher, M.A. Oropeza Rosas, J.C. Zwonitzer, S. Kresovich, M.D. McMullen, and D. Ware. 2011. Genome wide association study of quantitative resistance to southern leaf blight in the maize nes ted association mapping population. Nat. Genet. 43(2): 163 168. Langmead, B., and S.L. Salzberg. 2012. Fast gapped read alignment with Bowtie 2. Nat. Methods 9(4): 357 359. Li, H. 2013. Aligning sequence reads, clone sequences and assembly contigs with B WA MEM. arXiv Prepr. arXiv1303.3997. Li, H., G. Ye, and J. Wang. 2007. A modified algorithm for the improvement of composite interval mapping. Genetics 175(1): 361 374. Liang, X., X. Chen, Y. Hong, H. Liu, G. Zhou, S. Li, and B. Guo. 2009. Utility of EST derived SSR in cultivated peanut (Arachis hypogaea L.) and Arachis wild species. BMC Plant Biol. 9(1): 35. Llamas Llamas, M.E., E. Zavaleta Mejia, V.A. Gonzalez Hernandez, L. Cervantes Diaz, J.A. Santizo Rincon, and D.L. Ochoa Martinez. 1998. Effect of t emperature on symptom expression and accumulation of tomato spotted wilt virus in different host species. Plant Pathol. 47(3): 341 347 Lu, P., D.A. Huber, and T.L. White. 2001. Comparison of multivariate and univariate methods for the estimation of type B genetic correlations. Silvae Genet. 50(1): 13 22. Lyerly, J.H., H.T. Stalker, J.W. Moyer, and K. Hoffman. 2002. Evaluation of Arachis species for resistance to Tomato spotted wilt virus. Peanut Sci. 29(2): 79 84. Lynch, M., and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Sunderland, MA. Macedo, S.E., M.C. Moretzsohn, S.C.M. Leal Bertioli, D.M.T. Alves, E.G. Gouvea, V.C.R. Azevedo, and D.J. Bertioli. 2012. Development and characterization of highly polymorphic long TC repeat mic rosatellite markers for genetic analysis of peanut. BMC Res. Notes 5(1): 86. Mandal, B., H.R. Pappu, A.K. Culbreath, C.C. Holbrook, D.W. Gorbet, and J.W. Todd. 2002. Differential response of selected peanut (Arachis hypogaea) genotypes to mechanical inocu lation by Tomato spotted wilt virus. Plant Dis. 86(9): 939 944. Mandal, B., H.R. Pappu, A K. Culbreath, and P. Pathology. 2001. Factors Affecting Mechanical Transmission of Tomato spotted wilt virus to Peanut ( Arachis hypogaea ). Plant Dis. 85(12 ): 1259 1263.

PAGE 150

150 Maule, A.J., C. Caranta, and M.I. Boulton. 2007. Sources of natural resistance to plant viruses: status and prospects. Mol. Plant Pathol. 8(2): 223 231. Mckinney, J.L. 2013. Influence Of Planting Date, Plant Population, And Cultivar On Manage ment Of Spotted Wilt In Peanut (Arachis Hypogaea L.). McMullen, M.D., S. Kresovich, H.S. Villeda, P. Bradbury, H. Li, Q. Sun, S. Flint Garcia, J. Thornsberry, C. Acharya, and C. Bottoms. 2009. Genetic properties of the maize nested association mapping pop ulation. Science (80 ). 325(5941): 737 740. Mekuria, G., S.A. Ramesh, E. Alberts, T. Bertozzi, M. Wirthensohn, G. Collins, and M. Sedgley. 2003. Comparison of ELISA and RT PCR for the detection of Prunus necrotic ring spot virus and prune dwarf virus in almond (Prunus dulcis). J. Virol. Methods 114(1): 65 69. Miklas, P.N., J.D. Kelly, S.E. Beebe, and M.W. Blair. 2006. Common bean breeding for resistance against biotic and abiotic stresses: from classical to MAS breeding. Euphytica 147(1 2): 105 131. Mi lla Lewis, S.R., and T.G. Isleib. 2005. Best Linear Unbiased Prediction of Breeding Values for Tomato Spotted Wilt Virus (TSWV) Incidence in Virginia type Peanuts. Peanut Sci. 32(1): 57 67. Mitchell, F.L., and J.W. Smith. 1993. Suivey by ELISA of Thrips ( ) Vectored Tomato Spotted Wilt Virus Distribution in Foliage and Flowers of Field Infected 149. Moury, B., A. Palloix, K.G. Selassie, and G. Marchoux. 1997. Hypersensitive resistance to tomato spotted wilt virus in three Capsicum chinense accessions is controlled by a single gene and is overcome by virulent strains. Euphytica 94(1): 45 52. Murakami, M., M. Gallo Meagher, D.W. Gorbet, and R.L. Meagher. 2006. Utilizing immunoassays to determine systemic tomato spotted wilt virus infection for elucidating field resistance in peanut. Crop Prot. 25(3): 235 243. Nagy, E.D., Y. Chu, Y. Guo, S. Khanal, S. Tang, Y. Li, W.B. Dong, P. Timper, C. Taylor, and P. Ozias Akins. 2010. Recombination is suppressed in an alien introgression in peanut harboring Rma, a dominant root knot nematode resistance gene. Mol. Breed. 26(2): 357 370. Nagy, E.D., Y. Guo, S. Tang, J.E. Bowers, R. a Okashah, C. a Taylor, D. Zhang, S. Khanal, A.F. Heesacker, N. Khalilian, A.D. Farmer, N. Carra squilla Garcia, R.V. Penmetsa, D. Cook, H.T. Stalker, N. Nielsen, P. Ozias Akins, and S.J. Knapp. 2012. A high density genetic map of Arachis duranensis, a diploid ancestor of cultiv ated peanut. BMC Genomics 13(1)

PAGE 151

151 Naidu, R.A., F.M. Kimmins, C.M. Deom, P. Subrahmanyam, A.J. Chiyembekeza, and P.J.A. Van der Merwe. 1999. Groundnut rossette: a virus disease affecting groundnut production in sub saharan Africa. Plant Dis. 83(8): 700 709. Norden, A.J., R.W. Lipscomb, and W.A. Carver. 1969. Registration of Fl orunner Peanuts1 (Reg. No. 2). Crop Sci. 9(6): 850. Pandey, M.K., E. Monyo, P. Ozias Akins, X. Liang, P. Guimares, S.N. Nigam, H.D. Upadhyaya, P. Janila, X. Zhang, and B. Guo. 2012. Advances in Arachis genomics for peanut improvement. Biotechnol. Adv. 30 (3): 639 651. Pan ter, D.M., and F.L. Allen. 1995a Using best linear unbiased predictions to enhance breeding for yield in soybean: I. Choosing parents. Crop Sci. 35(2): 397 405. Pan ter, D.M., and F.L. Allen. 1995b Using best linear unbiased predictions to enhance breeding for yield in soybean: II. Selection of superior crosses from a limited number of yield trials. Crop Sci. 35(2): 405 410. Pappu, H.R., R.A.C. Jones, and R.K. Jain. 2009. Global status of tospovirus epidemics in diverse cropping systems : successes achieved and challenges ahead. Virus Res. 141(2): 219 236. Parrella, G., P. Gognalons, K. Gebre Selassie, C. Vovlas, and G. Marchoux. 2003. An update of the host range of Tomato spotted wilt virus. J. Plant Pathol.: 227 264. Pattee, H.E., T.G Isleib, D.W. Corbet, F.G. Ciesbrecht, and Z. Cuf. 2001. Parent Selection in Breeding for Roasted Peanut Flavor Quality. : 51 58. Paynter, M.L., J. De Faveri, and M.E. Herrington. 2014. Resistance to Fusarium oxysporum f. sp. fragariae and Predicted Bree ding Values in Strawberry. J. Am. Soc. Hortic. Sci. 139(2): 178 184. Peters, D. 1998. An updated list of plant species susceptible to tospoviruses. p. 107 110. 4th International Symposium on Tospoviruses and Thrips in Floral and Vegetable Crops. Piepho, H.P., J. Mhring, a. E. Melchinger, and a. Bchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1 2): 209 228. Prins, M., M. Kikkert, C. Ismayadi, W. de Graauw, P. de Haan, and R. Goldbach. 1997. Characterizat ion of RNA mediated resistance to tomato spotted wilt virus in transgenic tobacco plants expressing NS(M) gene sequences. Plant Mol. Biol. 33: 235 243 Proite, K., S.C.M. Leal Bertioli, D.J. Bertioli, M.C. Moretzsohn, F.R. da Silva, N.F. Martins, and P.M. Guimares. 2007. ESTs from a wild Arachis species for gene discovery and marker development. BMC Plant Biol. 7(1): 7.

PAGE 152

152 Qin, H., S. Feng, C. Chen, Y. Guo, S. Knapp, A. Culbreath, G. He, M.L. Wang, X. Zhang, C.C. Holbrook, P. Ozias Akins, and B. Guo. 2012. An integrated genetic linkage map of cultivated peanut (Arachis hypogaea L.) constructed from two RIL populations. Theor. Appl. Genet. 124: 653 664. Reif, J.C., Y. Zhao, T. Wrschum, M. Gowda, and V. Hahn. 2013. Genomic prediction of sunflower hybrid perf ormance. Plant Breed. 132(1): 107 114. Reitz, S.R. 2002. Seasonal and Within Plant Distribution of Frankliniella Thrips (Thysanoptera: Thripidae) in North Florida Tomatoes. Florida Entomol. 85(3): 431 439. Resende, M.D.V. de, and M.H.P. Barbosa. 2006. Se lection via simulated individual BLUP based on family genotypic effects in sugarcane. Pesqui. Agropecuria Bras. 41(3): 421 429. Riley, D.G., S. V Joseph, R. Srinivasan, and S. Diffie. 2011. Thrips vectors of tospoviruses. J. Integr. Pest Manag. 1(2): 1 1 0. Robertson, A. 1959. The sampling variance of the genetic correlation coefficient. Biometrics 15(3): 469 485. Robinson, H.F., R.E. Comstock, and P.H. Harvey. 1949. Estimates of heritability and the degree of dominance in corn. Agron. J 41(8): 353 359. Rosell, S., M.J. Dez, and F. Nuez. 1998. Genetics of tomato spotted wilt virus resistance coming from Lycopersicon peruvianum. Eur. J. Plant Pathol. 104(5): 499 509. Schaeffer, L.R., J.W. Wilton, and R. Thompson. 1978. Simultaneous estimation of varian ce and covariance components from multitrait mixed model equations. Biometrics: 199 208. Scholthof, K. B.G. 2007. The disease triangle: pathogens, the environment and society. Nat. Rev. Microbiol. 5(2): 152 156. Shirasawa, K., D.J. Bertioli, R.K. Varshney, M.C. Moretzsohn, S.C.M. Leal Bertioli, M. Thudi, M.K. Pandey, J. F. Rami, D. Foncka, and M.V.C. Gowda. 2013a. Integrated consensus map of cultivated peanut and wild relatives reveals structures of the A and B g enomes of Arachis and divergence of the legume genomes. DNA Res 42. Shrestha, A. 2011. Interactions between frankliniella fusca (thysanoptera: thripidae) and tomato spotted wilt virus in the peanut pathosystem. Silva, G.A.P., S.A. Gezan, M.P. de Carvalh o, L.R.L. Gouva, C.K. Verardi, A.L.B. de Oliveira, and P.D.S. Gonalves. 2014. Genetic parameters in a rubber tree population: heritabilities, genotype by environment interactions and multi trait correlat ions. Tree Genet. Genomes 10(6)

PAGE 153

153 Simpson, C.E., a Simpson, C.E., J.L. Starr, G.T. Church, M.D. Burow, and A.H. Paterson. 2003. Registration of Singh, B.D., and A.K. Singh. 2015. Marker Assist ed Plant Breeding: Principles and Practices. Sleper, D.A., and J.M. Poehlman. 2006. Breeding field crops. Blackwell publishing. Smith Jr, J.W., and R.L. Sam sgj. 1977. Economics Of Thrips Control On Peanuts In Texas Snedecor, G.W., and W.G. Cochran. 19 67. Statistical Methods. Ames. Stevens, M.R., S.J. Scott, and R.C. Gergerich. 1991. Inheritance of a gene for resistance to tomato spotted wilt virus (TSWV) from Lycopersicon peruvianum Mill. Euphytica 59(1): 9 17. Stuthman, D.D., K.J. Leonard, and J. Miller Garvin. 2007. Breeding crops for durable resistance to disease. Adv. Agron. 95: 319 367. Sundaraj, S., R. Srinivasan, A.K. Culbreath, D.G. Riley, and H.R. Pappu. 2014. Host plant resistance against Tomato spotted wilt virus in peanut (Arachis hypog aea) and its impact on susceptibility to the virus, virus population genetics, and vector feeding behavior and Survival. Phytopathology 104(2): 202 210. Tang Wang, C., X. Dao Yang, D. Xu Chen, S. Lin Yu, G. Zhen Liu, Y. Yi Tang, and J. Zhi Xu. 2007. Isolation of simple sequence repeats from groundnut. Electron. J. Biotechnol. 10(3): 473 480. Tian, F., P.J. Bradbury, P.J. Brown, H. Hung, Q. Sun, S. Flint Garcia, T.R. Rocheford, M.D. McMullen, J.B. Holland, and E.S. Buckler. 2011. Genome wide associati on study of leaf architecture in the maize nested association mapping population. Nat. Genet. 43(2): 159 162. Tillman, B.L., and D.W. Gorbet. 2012 Peanut cultivar UFT113 U.S. Patent No. 8,178,752. Tillman, B.L., D.W. Gorbet, and P.C. Andersen. 2007. I nfluence of planting date on yield and spotted wilt of runner market type peanut. Peanut Sci. 34(2): 79 84. Todd, J.W., A.K. Culbreath, and S.L. Brown. 1995. Dynamics of vector populations and progress of spotted wilt disease relative to insecticide use i n peanuts. Tospoviruses Thrips Flor. Veg. Crop. 431: 483 490. Todd, J.W., A.K. Culbreath, J.W. Demski, and R. Beshear. 1990. Thrips as vectors of TSWV. p. 81. In Proc. Am. Peanut Res. Ed. Soc.

PAGE 154

154 Ullman, D.E., J.L. Sherwood, T.L. German, and T. Lewis. 1997 Thrips as vectors of plant pathogens. Thrips as Crop pests.: 539 565. Valls, J.F.M., and C.E. Simpson. 1994. Taxonomy, natural distribution, and attributes of Arachis. Biol. Agron. forage Arachis: 1 18. V an Ooijen, J.W. 2006. JoinMap 4. Softw. Calc. Ge net. Link. maps Exp. Popul. Kyazma BV, Wageningen, Netherlands. Venuprasad, R., H.R. Lafitte, and G.N. Atlin. 2007. Response to direct selection for grain yield under drought stress in rice. Crop Sci. 47(1): 285 293. Visscher, P.M., W.G. Hill, and N.R. W ray. 2008. Heritability in the genomics era -concepts and misconceptions. Nat. Rev. Genet. 9(4): 255 266. V on Bargen, S., K. Salchert, M. Paape, B. Piechulla, and J.W. Kellmann. 2001. Interactions between the tomato spotted wilt virus movement protein and plant proteins showing homologies to myosin, kinesin and DnaJ like chaperones. Plant Physiol. Biochem. 39: 1083 1093. Vos, P., G. Simons, T. Jesse, J. Wijbrandi, L. Heinen, R. Hogers, A. Frijters, J. Groenendijk, P. Diergaarde, and M. Reijans. 1998. The tomato Mi 1 gene confers resistance to both root knot nematodes and potato aphids. Nat. Biotechnol. 16(13): 1365 1369. Wang, H., P. Khera, B. Huang, M. Yuan, R. Katam, W. Zhuang, K. Harris Shultz, K.M. Moore, A.K. Culbreath, X. Zhang, R.K. Varshney, L. Xi e, and B. Guo. 2015. Analysis of genetic diversity and population structure of peanut cultivars and breeding lines from China, India and the US using simple sequence repeat markers. J. Integr. Plant Biol. Wang, J., H. Li, L. Zhang, and L. Meng. 2012. Quant. Genet. Group, Inst. Crop Sci. Chinese Acad. Agric. Sci. (CAAS), Beijing 100081. Wang, H., M. Pandey, L. Qiao, H. Qin, A. Culbreath, G. He, R. Varshney, and B. Scully. 2013. Genetic mapping of quantitative trait loci analysis for disease resistance Using F2 and F5 Generation Plant Genome 6(3): 1771 1774. Wan g Zhen, G.U.O., Z. Tian Zhen, Z. Xie Fei, and P.A.N. Jia Ju. 2005. Mo Pyramiding Breeding with Molecular Marker Assisted Selection and Its Applications in Cotton. Warner, J.N. 1952. A Method for Estimating Heritability1. Agron. J. 44(8): 427. Weber, C.R., and B.R. Moorthy. 1952. Heritable and non heritable relationships and variability of oil content and agronomic characters in the F2 generation of soybean crosses. Agron. J 44(4): 202 209.

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155 Weinig, C., and J. Schmitt. 2004. Environmental effects on the expression of quantitative trait loci and implications fo r phenotypic evolution. Bioscience 54(7): 627 635. Whitaker, V.M., L.F. Osorio, and T. Hasing. 2012. Estimation of Genetic Parameters for 12 Fruit and Vegetative Traits in the University of Florida Strawberry Breeding Population. 137(5): 316 324. White, T., and G. Hodge. 1988. Best linear prediction of breeding values in a forest tree improvement program. Theor. Appl. Genet.: 719 727 Whitfield, A.E., D.E. Ullman, and T.L. German. 2005. Tospovirus thrips interactions. Annu. Rev. Phytopathol. 43: 459 489. Xu, Y., and J.H. Crouch. 2008. Marker assisted selection in plant breeding: from publications to practice. Crop Sci. 48(2): 391 407. Yamada, Y. 1962. Genotype by environment interaction and genetic correlation of the same trait under different environmen ts. Japanese J. Genet. 37(6): 498 509.

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156 BIOGRAPHICAL SKETCH Yu Chien Tseng was born in Taichung City, Taiwan and moved to Chiayi City at the age of four. She expressed her interests in history, society and biology when she was young. Later, she received her B.S. degree from the Agronomy Department at the National Taiwan University, Taiwan in 2011. During college, she took a plant breeding course and first ly became interested in plant breeding. She decide d to continue her graduate studies in the United States i n order to learn the ART of plant breeding. S he sta r ted her Ph.D career in Plant Molecular Breeding Initiative (PMBI), in the Agronomy D epartment at the Universi ty of Florida, in August 2011. She wants to become a plant breeder or works in the related agricultural fields in the future.