Mapping quantitative trait loci (QTLs) and estimating the epistasis controlling stem rot resistance in cultivated peanut (Arachis hypogaea)

Abstract

Key message

A total of 33 additive stem rot QTLs were identified in peanut genome with nine of them consistently detected in multiple years or locations. And 12 pairs of epistatic QTLs were firstly reported for peanut stem rot disease.

Abstract

Stem rot in peanut (Arachis hypogaea) is caused by the Sclerotium rolfsii and can result in great economic loss during production. In this study, a recombinant inbred line population from the cross between NC 3033 (stem rot resistant) and Tifrunner (stem rot susceptible) that consists of 156 lines was genotyped by using 58 K peanut single nucleotide polymorphism (SNP) array and phenotyped for stem rot resistance at multiple locations and in multiple years. A linkage map consisting of 1451 SNPs and 73 simple sequence repeat (SSR) markers was constructed. A total of 33 additive quantitative trait loci (QTLs) for stem rot resistance were detected, and six of them with phenotypic variance explained of over 10% (qSR.A01-2, qSR.A01-5, qSR.A05/B05-1, qSR.A05/B05-2, qSR.A07/B07-1 and qSR.B05-1) can be consistently detected in multiple years or locations. Besides, 12 pairs of QTLs with epistatic (additive × additive) interaction were identified. An additive QTL qSR.A01-2 also with an epistatic effect interacted with a novel locus qSR.B07_1-1 to affect the percentage of asymptomatic plants in a row. A total of 193 candidate genes within 38 stem rot QTLs intervals were annotated with functions of biotic stress resistance such as chitinase, ethylene-responsive transcription factors and pathogenesis-related proteins. The identified stem rot resistance QTLs, candidate genes, along with the associated SNP markers in this study, will benefit peanut molecular breeding programs for improving stem rot resistance.

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References

  1. Adrian M, Jeandet P (2012) Effects of resveratrol on the ultrastructure of Botrytis cinerea conidia and biological significance in plant/pathogen interactions. Fitoterapia 83:1345–1350

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    CAS  PubMed  Article  Google Scholar 

  3. Anco D (2017) Peanut disease management. South carolina pest management handbook for field crops. South Carolina State Library, Columbia, pp 190–201

    Google Scholar 

  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300

    Google Scholar 

  5. Bera SK, Kamdar JH, Kasundra SV, Ajay BC (2016) A novel QTL governing resistance to stem rot disease caused by Sclerotium rolfsii in peanut. Aust Plant Pathol 45:637–644

    Article  Google Scholar 

  6. Bertioli DJ, Cannon SB, Froenicke L, Huang G, Farmer AD, Cannon EK, Liu X, Gao D, Clevenger J, Dash S (2016) The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet 47:438

    Article  CAS  Google Scholar 

  7. Bertioli DJ, Jenkins J, Clevenger J, Dudchenko O, Gao D, Seijo G, Leal-Bertioli SCM, Ren L, Farmer AD, Pandey MK, Samoluk SS, Abernathy B, Agarwal G, Ballén-Taborda C, Cameron C, Campbell J, Chavarro C, Chitikineni A, Chu Y, Dash S, El Baidouri M, Guo B, Huang W, Kim KD, Korani W, Lanciano S, Lui CG, Mirouze M, Moretzsohn MC, Pham M, Shin JH, Shirasawa K, Sinharoy S, Sreedasyam A, Weeks NT, Zhang X, Zheng Z, Sun Z, Froenicke L, Aiden EL, Michelmore R, Varshney RK, Holbrook CC, Cannon EKS, Scheffler BE, Grimwood J, Ozias-Akins P, Cannon SB, Jackson SA, Schmutz J (2019) The genome sequence of segmental allotetraploid peanut Arachis hypogaea. Nat Genet 51:877–884

    CAS  Article  Google Scholar 

  8. Beute M, Wynne J, Emery DJCS (1976) Registration of NC 3033 Peanut Germplasm1 (Reg. No. GP 9) 16:887-887

  9. Bocianowski J (2013) Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect. Genet Mol Biol 36:93–100

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Chen W, Jiao Y, Cheng L, Huang L, Liao B, Tang M, Ren X, Zhou X, Chen Y, Jiang H (2016) Quantitative trait locus analysis for pod- and kernel-related traits in the cultivated peanut (Arachis hypogaea L.). BMC Genet 17:25

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. Chu Y, Chee P, Culbreath A, Isleib TG, Holbrook CC, Ozias-Akins P (2019) Major QTLs for resistance to early and late leaf spot diseases are identified on chromosomes 3 and 5 in peanut (Arachis hypogaea). Front Plant Sci 10:883

    PubMed  PubMed Central  Article  Google Scholar 

  12. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Clevenger J, Chu Y, Chavarro C, Agarwal G, Bertioli DJ, Leal-Bertioli SCM, Pandey MK, Vaughn J, Abernathy B, Barkley NA, Hovav R, Burow M, Nayak SN, Chitikineni A, Isleib TG, Holbrook CC, Jackson SA, Varshney RK, Ozias-Akins P (2017) Genome-wide SNP genotyping resolves signatures of selection and tetrasomic recombination in peanut. Mol Plant 10:309–322

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Core Team R (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  15. Culbreath AK, Brenneman TB, Bondari K, Reynolds KL, McLean HS (1995) Late leaf spot, southern stem rot, and peanut yield responses to rates of cyproconazole and chlorothalonil applied alone and in combination. Plant Dis 79:1121–1125

    CAS  Article  Google Scholar 

  16. De Oliveira Y, Sosnowski O, Charcosset A, Joets J (2014) BioMercator 4: a complete framework to integrate QTL, meta-QTL, and genome annotation. In: European conference on computational biology, Strasbourg

  17. Dodia S, Rathnakumar A, Mishra G, Radhakrishnan T, Binal J, Thirumalaisamy P, Narendra K, Sumitra C, Dobaria J, Abhay K (2016) Phenotyping and molecular marker analysis for stem-rot disease resistance using F2 mapping population in groundnut. Int J Trop Agric 34:1135–1139

    Google Scholar 

  18. Dodia SM, Joshi B, Gangurde SS, Thirumalaisamy PP, Mishra GP, Narandrakumar D, Soni P, Rathnakumar AL, Dobaria JR, Sangh C, Chitikineni A (2019) Genotyping-by-sequencing based genetic mapping reveals large number of epistatic interactions for stem rot resistance in groundnut. Theor Appl Genet 132(4):1001–1016

    PubMed  PubMed Central  Article  Google Scholar 

  19. Dunn OJ (1964) Multiple comparisons using rank sums. Technometrics 6:241–252

    Article  Google Scholar 

  20. Fujita M, Fujita Y, Noutoshi Y, Takahashi F, Narusaka Y, Yamaguchi-Shinozaki K, Shinozaki K (2006) Crosstalk between abiotic and biotic stress responses: a current view from the points of convergence in the stress signaling networks. Curr Opin Plant Biol 9:436–442

    PubMed  Article  PubMed Central  Google Scholar 

  21. Goffinet B, Gerber S (2000) Quantitative trait loci: a meta-analysis. Genetics 155:463–473

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gu Z, Gu L, Eils R, Schlesner M, Brors B (2014) circlize implements and enhances circular visualization in R. Bioinformatics 30(19):2811–2812

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. Guo Y, Khanal S, Tang S, Bowers JE, Heesacker AF, Khalilian N, Nagy ED, Zhang D, Taylor CA, Stalker HT, Ozias-Akins P, Knapp SJ (2012) Comparative mapping in intraspecific populations uncovers a high degree of macrosynteny between A- and B-genome diploid species of peanut. BMC Genom 13:608

    CAS  Article  Google Scholar 

  24. Herselman L, Thwaites R, Kimmins F, Courtois B, Van Der Merwe P, Seal SE (2004) Identification and mapping of AFLP markers linked to peanut (Arachis hypogaea L.) resistance to the aphid vector of groundnut rosette disease. Theor Appl Genet 109:1426–1433

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. Holbrook CC, Culbreath AK (2007) Registration of ‘Tifrunner’peanut. Plant Regist 1(124):10–3198

    Google Scholar 

  26. Holbrook C, Isleib T, Ozias-Akins P, Chu Y, Knapp S, Tillman B, Guo B, Gill R, Burow MJPS (2013) Development and phenotyping of recombinant inbred line (RIL) populations for peanut (Arachis hypogaea). Peanut Sci 40:89–94

    Article  Google Scholar 

  27. Hu X, Zhang S, Miao H, Cui F, Shen Y, Yang W, Xu T, Chen N, Chi X, Zhang Z (2018) High-density genetic map construction and identification of QTLs controlling oleic and linoleic acid in peanut using SLAF-seq and SSRs. Sci Rep 8:5479

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Huang L, He H, Chen W, Ren X, Chen Y, Zhou X, Xia Y, Wang X, Jiang X, Liao B (2015) Quantitative trait locus analysis of agronomic and quality-related traits in cultivated peanut (Arachis hypogaea L.). Theor Appl Genet 128:1103–1115

    PubMed  PubMed Central  Article  Google Scholar 

  29. Jogi A, Kerry JW, Brenneman TB, Leebens-Mack JH, Gold SE (2016) Identification of genes differentially expressed during early interactions between the stem rot fungus (Sclerotium rolfsii) and peanut (Arachis hypogaea) cultivars with increasing disease resistance levels. Microbiol Res 184:1–12

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. Johnson NL, Kemp AW, Kotz S (2005) Univariate discrete distributions. Wiley, New York

    Google Scholar 

  31. Khatri K, Kunwar S, Barocco R, Dufault NJPS (2017) Monitoring fungicide sensitivity levels and mycelial compatibility groupings of Sclerotium rolfsii Isolates from Florida peanut fields. Peanut Sci 44:83–92

    Article  Google Scholar 

  32. Kokalis-Burelle N, Porter D, Rodriguez-Kabana R, Smith D, Subrahmanyam P (1997) Compendium of peanut diseases. American Phytopathological Society, Saint Paul

    Google Scholar 

  33. Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175(1):361–374

    PubMed  PubMed Central  Article  Google Scholar 

  34. Li H, Ribaut JM, Li Z, Wang J (2008) Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet 116(2):243–260

    PubMed  Article  PubMed Central  Google Scholar 

  35. Li S, Wang J, Zhang L (2015) Inclusive composite interval mapping of QTL by environment interactions in biparental populations. PLoS ONE 10(7):e0132414

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. Little E (2015) Georgia plant disease loss estimates. The University of Georgia Cooperative Extension Bulletin, University of Georgia, Athens

    Google Scholar 

  37. Mamidi S, Miklas PN, Trapp J, Felicetti E, Grimwood J, Schmutz J, Lee R, McClean PE (2016) Sequence-based introgression mapping identifies candidate white mold tolerance genes in common bean. Plant Genome 9:2

    Article  CAS  Google Scholar 

  38. Marrs KA (1996) The functions and regulation of glutathione S-transferases in plants. Annu Rev Plant Biol 47:127–158

    CAS  Article  Google Scholar 

  39. McHale L, Tan X, Koehl P, Michelmore RW (2006) Plant NBS-LRR proteins: adaptable guards. Genome Biol 7:212

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3(3):269–283

    Article  Google Scholar 

  41. Monnahan PJ, Kelly JK (2015) Epistasis is a major determinant of the additive genetic variance in Mimulus guttatus. PLoS Genet 11:e1005201

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genet 13:85

    CAS  Article  Google Scholar 

  43. Pandey MK, Agarwal G, Kale SM, Clevenger J, Nayak SN, Sriswathi M, Chitikineni A, Chavarro C, Chen X, Upadhyaya HD (2017) Development and evaluation of a high density genotyping ‘Axiom_Arachis’ array with 58K SNPs for accelerating genetics and breeding in groundnut. Sci Rep 7:40577

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Punja ZK (1988) Sclerotium (Athelia) Rolfsii, a pathogen of many plant species. In: Sidhu GS (ed) Genetics of plant pathogenic fungi. Advances in plant pathology. Academic Press, Cambridge, pp 523–534

    Google Scholar 

  45. Rajyaguru RH, Thirumalaisamy P, Patel KG, Thumar JT (2017) Biochemical basis of genotypic and bio-agent induced stem rot resistance in groundnut. Legume Res Int J 40:2

    Google Scholar 

  46. Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol Plant 10:1047–1064

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. Schlötterer C (2004) The evolution of molecular markers—just a matter of fashion? Nat Rev Genet 5:63

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  48. Shapiro SS, Wilk MB (1965) An analysis of variance test for normality. Biometrika 52:591–611

    Article  Google Scholar 

  49. Shinozaki K, Yamaguchi-Shinozaki K, Seki M (2003) Regulatory network of gene expression in the drought and cold stress responses. Curr Opin Plant Biol 6:410–417

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. Sosnowski O, Charcosset A, Joets J (2012) BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics 28:2082–2083

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Sujay V, Gowda M, Pandey M, Bhat R, Khedikar Y, Nadaf H, Gautami B, Sarvamangala C, Lingaraju S, Radhakrishan T (2012) Quantitative trait locus analysis and construction of consensus genetic map for foliar disease resistance based on two recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breed 30:773–788

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  52. Van Loon LC, Rep M, Pieterse CM (2006) Significance of inducible defense-related proteins in infected plants. Annu Rev Phytopathol 44:135–162

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  53. Van Ooijen JW (2006) JoinMap 4®, software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen

    Google Scholar 

  54. Vasconcellos RC, Oraguzie OB, Soler A, Arkwazee H, Myers JR, Ferreira JJ, Song Q, McClean P, Miklas PN (2017) Meta-QTL for resistance to white mold in common bean. PLoS ONE 12:e0171685

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  55. Vuong TD, Diers BW, Hartman GL (2008) Identification of QTL for resistance to Sclerotinia stem rot in soybean plant introduction 194639. Crop Sci 48:2209–2214

    Article  Google Scholar 

  56. Wang C, Rutledge J, Gianola D (1994) Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genet Sel Evol 26:91

    PubMed Central  Article  Google Scholar 

  57. Wang S, Basten C, Zeng Z (2012) Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh

    Google Scholar 

  58. Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer, Berlin

    Google Scholar 

  59. Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, Zhu J (2008) QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24:721–723

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  60. Zar JH (2005) Spearman rank correlation. Encycl Biostat 7:121

    Google Scholar 

  61. Zhao X, Han Y, Li Y, Liu D, Sun M, Zhao Y, Lv C, Li D, Yang Z, Huang L (2015) Loci and candidate gene identification for resistance to Sclerotinia sclerotiorum in soybean (Glycine max L. Merr.) via association and linkage maps. Plant J 82:245–255

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. Zhao Y, Zhang C, Chen H, Yuan M, Nipper R, Prakash C, Zhuang W, He G (2016) QTL mapping for bacterial wilt resistance in peanut (Arachis hypogaea L.). Mol Breed 36:13

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  63. Zhou X, Xia Y, Ren X, Chen Y, Huang L, Huang S, Liao B, Lei Y, Yan L, Jiang H (2014) Construction of a SNP-based genetic linkage map in cultivated peanut based on large scale marker development using next-generation double-digest restriction-site-associated DNA sequencing (ddRADseq). BMC Genom 15:351

    Article  CAS  Google Scholar 

  64. Zhuang W, Chen H, Yang M, Wang J et al (2019) The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nat Genet 51(5):865

    CAS  Article  Google Scholar 

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Funding

This study was funded by the Florida Peanut Producers Association, the National Peanut Foundation and USDA National Institute of Food and Agriculture, Hatch Project 1011664.

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Contributions

JW coordinated the research. YC, TGI, POA and CH developed the RIL population. ND and TB provided the inoculum and advised on the inoculation procedure. BT and JW supervised the phenotyping in Florida. YCT, HZ, ZP, XY, YL and JW conducted the experiments and collected the phenotypic data in Florida. TB and POA supervised the research in Georgia. RC and TB conducted the experiments and collected the phenotypic data in Georgia. CC and POA conducted the linkage map analysis. ZL analyzed the whole data sets and prepared the manuscript draft. All authors revised the manuscript and read and approved the final manuscript.

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Correspondence to Jianping Wang.

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Data supporting the results are provided in the additional files and analyzed/organized in tables. The original data and the materials are available upon reasonable request to the corresponding author at wangjp@ufl.edu.

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Fig. S1

The distributions of phenotypic data from all datasets (TIFF 44237 kb)

Table S1

Description of experiments in this study (XLSX 10 kb)

Table S2

Kruskal-Wallis ANOVA test for different datasets (XLSX 11 kb)

Table S3

The correlation of phenotypic data from all experiments (XLSX 12 kb)

Table S4

Information of the linkage groups used for stem rot QTL mapping (XLSX 10 kb)

Table S5

The original additive QTLs detected for stem rot resistance by three types of software (XLSX 19 kb)

Table S6

Summary of original additive QTLs’ contribution to phenotype variations (XLSX 10 kb)

Table S7

The consensus additive QTLs detected for stem rot resistance after QTL meta-analysis (XLSX 15 kb)

Table S8

The epistatic QTLs for stem rot resistance detected by QTL IciMapping and QTLNetwork (XLSX 17 kb)

Table S9

The candidate genes identified within all stem rot QTL intervals (XLSX 27 kb)

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Luo, Z., Cui, R., Chavarro, C. et al. Mapping quantitative trait loci (QTLs) and estimating the epistasis controlling stem rot resistance in cultivated peanut (Arachis hypogaea). Theor Appl Genet 133, 1201–1212 (2020). https://doi.org/10.1007/s00122-020-03542-y

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