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Status and Prospectives of Genome-Wide Association Studies in Plants

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Bioinformatics in Rice Research

Abstract

The genome-wide association study (GWAS) is one of the potential approaches for identifying QTLs/genes and complex traits associated with target traits quickly using natural variations. With the advancement in genome sequencing technology, it is possible to examine genome-wide genetic variants of agro-morphological, physiological, biochemical, and molecular traits across diverse genetic materials. In nature, natural variants of crops are generated due to spontaneous mutations and manual breeding in the wild progenitors. Traditional landraces are adapted to various environmental conditions, rich sources of alleles, and genes linked with various traits valuable for variety improvement through molecular breeding because of the availability of high-throughput sequencing technologies and a reference genome sequence, accurate re-sequencing of a significant number of crop genomes as possible. It aided in understanding the genetic basis of phenotypic variance and allows for functional studies of evolutionary changes in crops. This rapid development will significantly improve crop design research using genomics-assisted breeding, allowing it to be used in gene recognition, cloning, QTL identification, and crop breeding using marker-assisted selection or genetic engineering. This book chapter presents an overview of the entire process of a typical GWAS, various software applications and, its limitations, and future perspectives.

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Abbreviations

BC:

Bonferroni correction

BILs:

Backcross inbred lines

CMLM:

Compress Mixed Linear Model

EMMA:

Effective Mixed-Model Association

EMMAX:

Efficient Mixed-Model Association eXpedited

FDR:

False discovery rate

GAPIT:

Genome Association and Prediction Integrated Tool

GLM:

General linear model

GWAS:

Genome-wide association study

LD:

Linkage disequilibrium

MAGIC:

Multiparent Advanced Generation Inter-Cross

MAS:

Marker assisted selection

MLM:

Mixed linear model

NAM:

Nested association mapping

NILs:

Near Isogenic lines

QTL:

Quantitative trait locus

RILs:

Recombinant Inbred Lines

SNP:

Single nucleotide polymorphism

TILLING:

Targeting induced local lesions in genome

References

  1. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: the challenge of feeding 9 billion people. Science. 2010;327:812–8.

    Article  CAS  PubMed  Google Scholar 

  2. Tester M, Langridge P. Breeding technologies to increase crop production in a changing world. Science. 2010;327:818–22.

    Article  CAS  PubMed  Google Scholar 

  3. Zhang Q. Strategies for developing Green Super Rice. Proc Natl Acad Sci U S A. 2007;104:16402–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009;19:1068–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6:e19379.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bevan MW, Uauy C. Genomics reveals new landscapes for crop improvement. Genome Biol. 2013;14:206.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Hamblin MT, Buckler ES, Jannink J-L. Population genetics of genomics-based crop improvement methods. Trends Genet. 2011;27:98–106.

    Article  CAS  PubMed  Google Scholar 

  8. Zhu C, Gore M, Buckler ES, Yu J. Status and prospects of association mapping in plants. Plant Genome. 2008;1. https://acsess.onlinelibrary.wiley.com/doi/abs/10.3835/plantgenome2008.02.0089.

  9. Michael TP, Jackson S. The first 50 plant genomes. Plant Genome. 2013;6. https://onlinelibrary.wiley.com/doi/10.3835/plantgenome2013.03.0001in.

  10. Sukumaran S, Yu J. Association mapping of genetic resources: achievements and future perspectives. In: Tuberosa R, Graner A, Frison E, editors. Genomics of plant genetic resources, vol.1: Managing, sequencing and mining genetic resources. Dordrecht: Springer; 2014. p. 207–35. https://doi.org/10.1007/978-94-007-7572-5_9.

    Chapter  Google Scholar 

  11. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xiao Y, Liu H, Wu L, Warburton M, Yan J. Genome-wide association studies in maize: praise and stargaze. Mol Plant. 2017;10:359–74.

    Article  CAS  PubMed  Google Scholar 

  13. Takeda S, Matsuoka M. Genetic approaches to crop improvement: responding to environmental and population changes. Nat Rev Genet. 2008;9:444–57.

    Article  CAS  PubMed  Google Scholar 

  14. Mackay TFC, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and prospects. Nat Rev Genet. 2009;10:565–77.

    Article  CAS  PubMed  Google Scholar 

  15. Ersoz ES, Yu J, Buckler ES. Applications of linkage disequilibrium and association mapping in crop plants. In: Varshney RK, Tuberosa R, editors. Genomics-assisted crop improvement, vol. 1: Genomics approaches and platforms. Dordrecht: Springer; 2007. p. 97–119. https://doi.org/10.1007/978-1-4020-6295-7_5.

    Chapter  Google Scholar 

  16. Liu H-J, Yan J. Crop genome-wide association study: a harvest of biological relevance. Plant J. 2019;97:8–18.

    Article  CAS  PubMed  Google Scholar 

  17. Varshney RK, Ribaut J-M, Buckler ES, Tuberosa R, Rafalski JA, Langridge P. Can genomics boost productivity of orphan crops? Nat Biotechnol. 2012;30:1172–6.

    Article  CAS  PubMed  Google Scholar 

  18. Gupta PK, Kulwal PL, Jaiswal V. Chapter two - Association mapping in plants in the post-GWAS genomics era. In: Kumar D, editor. Advances in genetics. Boston: Academic; 2019. p. 75–154. https://www.sciencedirect.com/science/article/pii/S0065266018300385.

    Google Scholar 

  19. Chen E, Huang X, Tian Z, Wing RA, Han B. The genomics of Oryza species provides insights into rice domestication and heterosis. Annu Rev Plant Biol. 2019;70:639–65.

    Article  CAS  PubMed  Google Scholar 

  20. Chen W, Wang W, Peng M, Gong L, Gao Y, Wan J, et al. Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat Commun. 2016;7:12767.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhou Y, Srinivasan S, Mirnezami SV, Kusmec A, Fu Q, Attigala L, et al. Semiautomated feature extraction from RGB images for sorghum panicle architecture GWAS. Plant Physiol. 2019;179:24–37.

    Article  CAS  PubMed  Google Scholar 

  22. Spindel JE, Dahlberg J, Colgan M, Hollingsworth J, Sievert J, Staggenborg SH, et al. Association mapping by aerial drone reveals 213 genetic associations for Sorghum bicolor biomass traits under drought. BMC Genomics. 2018;19:679.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Wang S-B, Feng J-Y, Ren W-L, Huang B, Zhou L, Wen Y-J, et al. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep. 2016;6:19444.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang Y, Massel K, Godwin ID, Gao C. Applications and potential of genome editing in crop improvement. Genome Biol. 2018;19:210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hansen TF. The evolution of genetic architecture. Annu Rev Ecol Evol Syst. 2006;37:123–57.

    Article  Google Scholar 

  26. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013;9:29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38:203–8.

    Article  CAS  PubMed  Google Scholar 

  28. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, et al. Efficient control of population structure in model organism association mapping. Genetics. 2008;178:1709–23.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42:355–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8:833–5.

    Article  CAS  PubMed  Google Scholar 

  32. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Listgarten J, Lippert C, Kadie CM, Davidson RI, Eskin E, Heckerman D. Improved linear mixed models for genome-wide association studies. Nat Methods. 2012;9:525–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang Q, Tian F, Pan Y, Buckler ES, Zhang Z. A SUPER powerful method for genome wide association study. PLoS One. 2014;9:e107684.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Abdurakhmonov IY, Abdukarimov A. Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genomics. 2008;2008:1–18.

    Article  CAS  Google Scholar 

  36. Zhu C, Yu J. Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types. Genetics. 2009;182:875–88.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Xu S. Theoretical basis of the Beavis effect. Genetics. 2003;165:2259–68.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sallam A, Martsch R. Association mapping for frost tolerance using multi-parent advanced generation inter-cross (MAGIC) population in faba bean (Vicia faba L.). Genetica. 2015;143:501–14.

    Article  PubMed  Google Scholar 

  39. Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT, Dilla-Ermita CJ, et al. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice. 2013;6:11.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD. Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature. 1988;335:721–6.

    Article  CAS  PubMed  Google Scholar 

  41. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32:650–4.

    Article  CAS  PubMed  Google Scholar 

  42. Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265:2037–48.

    Article  CAS  PubMed  Google Scholar 

  43. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516–7.

    Article  CAS  PubMed  Google Scholar 

  44. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES. Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet. 2001;28:286–9.

    Article  CAS  PubMed  Google Scholar 

  45. Alqudah AM, Koppolu R, Wolde GM, Graner A, Schnurbusch T. The genetic architecture of barley plant stature. Front Genet. 2016;7. https://www.frontiersin.org/articles/10.3389/fgene.2016.00117/full

  46. Alqudah AM, Youssef HM, Graner A, Schnurbusch T. Natural variation and genetic make-up of leaf blade area in spring barley. Theor Appl Genet. 2018;131:873–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.

    Article  CAS  PubMed  Google Scholar 

  48. Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–7.

    Article  CAS  PubMed  Google Scholar 

  49. Sherry ST, Ward M-H, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucl Acids Res. 2001;29:308–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Aranzana MJ, Kim S, Zhao K, Bakker E, Horton M, Jakob K, et al. Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet. 2005;1:e60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Bush WS, Moore JH. Chapter 11: genome-wide association studies. PLoS Comput Biol. 2012;8:e1002822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Digel B, Tavakol E, Verderio G, Tondelli A, Xu X, Cattivelli L, et al. Photoperiod-H1 (Ppd-H1) controls leaf size. Plant Physiol. 2016;172:405–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Milner SG, Jost M, Taketa S, Mazón ER, Himmelbach A, Oppermann M, et al. Genebank genomics highlights the diversity of a global barley collection. Nat Genet. 2019;51:319–26.

    Article  CAS  PubMed  Google Scholar 

  54. Alomari DZ, Eggert K, von Wirén N, Alqudah AM, Polley A, Plieske J, et al. Identifying candidate genes for enhancing grain Zn concentration in wheat. Front Plant Sci. 2018;9. https://www.frontiersin.org/articles/10.3389/fpls.2018.01313/full

  55. Camiolo S, Sablok G, Porceddu A. Altools: a user friendly NGS data analyser. Biol Direct. 2016;11:8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Kwak I-Y, Pan W. Adaptive gene- and pathway-trait association testing with GWAS summary statistics. Bioinformatics. 2016;32:1178–84.

    Article  CAS  PubMed  Google Scholar 

  57. Günther T, Coop G. Robust identification of local adaptation from allele frequencies. Genetics. 2013;195:205–20.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Westreich ST, Nattestad M, Meyer C. BigTop: a three-dimensional virtual reality tool for GWAS visualization. bioRxiv. 2019:650176.

    Google Scholar 

  59. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100:9440–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Moore CB, Wallace JR, Frase AT, Pendergrass SA, Ritchie MD. BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge. BMC Med Genomics. 2013;6:S6.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Huang M, Liu X, Zhou Y, Summers RM, Zhang Z. BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience. 2019;8:giy154. https://doi.org/10.1093/gigascience/giy154.

    Article  CAS  Google Scholar 

  62. Pasam RK, Sharma R, Malosetti M, van Eeuwijk FA, Haseneyer G, Kilian B, et al. Genome-wide association studies for agronomical traits in a world wide spring barley collection. BMC Plant Biol. 2012;12:16.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rentería ME, Cortes A, Medland SE. Using PLINK for genome-wide association studies (GWAS) and data analysis. In: Gondro C, van der Werf J, Hayes B, editors. Genome-wide association studies and genomic prediction. Totowa: Humana Press; 2013. p. 193–213. https://doi.org/10.1007/978-1-62703-447-0_8.

    Chapter  Google Scholar 

  64. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012;28:2397–9.

    Article  CAS  PubMed  Google Scholar 

  65. Hickey JM, Gorjanc G. Simulated data for genomic selection and Genome-Wide Association Studies Using a Combination of Coalescent and Gene Drop Methods. G3 Genes Genomes Genet. 2012;2:425–7.

    Google Scholar 

  66. Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36:1501–8.

    CAS  PubMed  Google Scholar 

  67. Yao Y, Liu Z, Wei Q, Ramsey SA. CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features. BMC Bioinformatics. 2019;20:63.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Millstein J, Chen GK, Breton CV. cit: hypothesis testing software for mediation analysis in genomic applications. Bioinformatics. 2016;32:2364–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Majumdar A, Haldar T, Bhattacharya S, Witte JS. An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations. PLoS Genet. 2018;14:e1007139.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Arkin Y, Rahmani E, Kleber ME, Laaksonen R, März W, Halperin E. EPIQ—efficient detection of SNP–SNP epistatic interactions for quantitative traits. Bioinformatics. 2014;30:i19–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Morris JA, Randall JC, Maller JB, Barrett JC. Evoker: a visualization tool for genotype intensity data. Bioinformatics. 2010;26:1786–7.

    Article  CAS  PubMed  Google Scholar 

  72. Svishcheva GR, Belonogova NM, Axenovich TI. Region-based association test for familial data under functional linear models. PLoS One. 2015;10:e0128999.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 2016;12:e1005767.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Dunham I, Kulesha E, Iotchkova V, Morganella S, Birney E. FORGE: a tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions. F1000Res. 2015;4:18.

    Article  CAS  Google Scholar 

  75. Coetzee SG, Rhie SK, Berman BP, Coetzee GA, Noushmehr H. FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucl Acids Res. 2012;40:e139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Tang Y, Liu X. G2P: a Genome-Wide-Association-Study simulation tool for genotype simulation, phenotype simulation and power evaluation. Bioinformatics. 2019;35:3852–4.

    Article  CAS  PubMed  Google Scholar 

  77. Iotchkova V, Ritchie GRS, Geihs M, Morganella S, Min JL, Walter K, et al. GARFIELD - GWAS analysis of regulatory or functional information enrichment with LD correction. bioRxiv. 2016:085738.

    Google Scholar 

  78. Yung LS, Yang C, Wan X, Yu W. GBOOST: a GPU-based tool for detecting gene–gene interactions in genome–wide case control studies. Bioinformatics. 2011;27:1309–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. He K, Li Y, Zhu J, Liu H, Lee JE, Amos CI, et al. Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics. 2016;32:50–7.

    Article  CAS  PubMed  Google Scholar 

  80. Song M, Hao W, Storey JD. Testing for genetic associations in arbitrarily structured populations. Nat Genet. 2015;47:550–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sung P-Y, Wang Y-T, Hsiung CA, Chung R-H. GCORE-sib: an efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs. BMC Bioinformatics. 2016;17:273.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Chen W-M, Manichaikul A, Rich SS. A generalized family-based association test for dichotomous traits. Am J Hum Genet. 2009;85:364–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Lu Q, Yao X, Hu Y, Zhao H. GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics. 2016;32:542–8.

    Article  CAS  PubMed  Google Scholar 

  84. Zhu S, Qian T, Hoshida Y, Shen Y, Yu J, Hao K. GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data. Bioinformatics. 2019;35:160–3.

    Article  CAS  PubMed  Google Scholar 

  85. Kowalski J, Dwivedi B, Newman S, Switchenko JM, Pauly R, Gutman DA, et al. Gene integrated set profile analysis: a context-based approach for inferring biological endpoints. Nucl Acids Res. 2016;44:e69.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Stanhope SA, Abney M. GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations. Bioinformatics. 2012;28:1553–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hai-Ming X, Li-Feng X, Ting-Ting H, Lin-Feng L, Guo-Bo C, Xi-Wei S, et al. GMDR: versatile software for detecting gene-gene and gene-environment interactions underlying complex traits. Curr Genomics. 2016;17:396–402.

    Article  CAS  Google Scholar 

  88. Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10:e1004787.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Chung D, Kim HJ, Zhao H. graph-GPA: a graphical model for prioritizing GWAS results and investigating pleiotropic architecture. PLoS Comput Biol. 2017;13:e1005388.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Balasubramanian R, LaFramboise T, Scholtens D, Gentleman R. A graph-theoretic approach to testing associations between disparate sources of functional genomics data. Bioinformatics. 2004;20:3353–62.

    Article  CAS  PubMed  Google Scholar 

  92. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucl Acids Res. 2014;42:D1001–6.

    Article  CAS  PubMed  Google Scholar 

  93. McCouch SR, Wright MH, Tung C-W, Maron LG, McNally KL, Fitzgerald M, et al. Open access resources for genome-wide association mapping in rice. Nat Commun. 2016;7:10532.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Huang D, Yi X, Zhang S, Zheng Z, Wang P, Xuan C, et al. GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits. Nucl Acids Res. 2018;46:W114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. gwascat: representing and modeling data in the EMBL-EBI GWAS catalog version 2.22.0 from Bioconductor. [cited 2021 Apr 17]. https://rdrr.io/bioc/gwascat/.

  96. Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics. 2012;28:3329–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Fuchsberger C, Taliun D, Pramstaller PP, Pattaro C, on behalf of the CKDGen Consortium. GWAtoolbox: an R package for fast quality control and handling of genome-wide association studies meta-analysis data. Bioinformatics. 2012;28:444–5.

    Article  CAS  PubMed  Google Scholar 

  98. Patron J, Serra-Cayuela A, Han B, Li C, Wishart DS. Assessing the performance of genome-wide association studies for predicting disease risk. PLoS One. 2019;14:e0220215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.

    Article  CAS  PubMed  Google Scholar 

  100. Dai M, Ming J, Cai M, Liu J, Yang C, Wan X, et al. IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies. Bioinformatics. 2017;33:2882–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Chen CJ, Zhang Z. iPat: intelligent prediction and association tool for genomic research. Bioinformatics. 2018;34:1925–7.

    Article  CAS  PubMed  Google Scholar 

  102. Fan Y-H, Song Y-Q. IPGWAS: an integrated pipeline for rational quality control and association analysis of genome-wide genetic studies. Biochem Biophys Res Commun. 2012;422:363–8.

    Article  CAS  PubMed  Google Scholar 

  103. Julienne H, Lechat P, Guillemot V, Lasry C, Yao C, Araud R, et al. JASS: command line and web interface for the joint analysis of GWAS results. NAR Genomics Bioinformatics. 2020;2. https://doi.org/10.1093/nargab/lqaa003

  104. Terada A, Yamada R, Tsuda K, Sese J. LAMPLINK: detection of statistically significant SNP combinations from GWAS data. Bioinformatics. 2016;32:3513–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33:272–9.

    Article  CAS  PubMed  Google Scholar 

  107. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Statist Softw. 2015;67:1–48.

    Article  Google Scholar 

  108. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Vaez A, van der Most PJ, Prins BP, Snieder H, van den Heuvel E, Alizadeh BZ, et al. lodGWAS: a software package for genome-wide association analysis of biomarkers with a limit of detection. Bioinformatics. 2016;32:1552–4.

    Article  CAS  PubMed  Google Scholar 

  110. Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, et al. Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities. Hum Mutat. 2012;33:1708–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Grace C, Farrall M, Watkins H, Goel A. Manhattan++: displaying genome-wide association summary statistics with multiple annotation layers. BMC Bioinformatics. 2019;20:610.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Haller T, Tasa T, Metspalu A. Manhattan harvester and cropper: a system for GWAS peak detection. BMC Bioinformatics. 2019;20:22.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Climente-González H, Azencott C-A. martini: an R package for genome-wide association studies using SNP networks. bioRxiv. 2021;2021.01.25.428047.

    Google Scholar 

  114. Childs LH, Lisec J, Walther D. Matapax: an online high-throughput genome-wide association study pipeline. Plant Physiol. 2012;158:1534–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Cichonska A, Rousu J, Marttinen P, Kangas AJ, Soininen P, Lehtimäki T, et al. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics. 2016;32:1981–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Lin Y-C, Hsiao C-L, Hsieh A-R, Lian I-B, Fann CSJ. Using maximal segmental score in genome-wide association studies. Genet Epidemiol. 2012;36:594–601.

    Article  PubMed  Google Scholar 

  118. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38:209–13.

    Article  CAS  PubMed  Google Scholar 

  119. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One. 2015;10:e0120758.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Xavier A, Xu S, Muir WM, Rainey KM. NAM: association studies in multiple populations. Bioinformatics. 2015;31:3862–4.

    Article  CAS  PubMed  Google Scholar 

  121. Fabregat-Traver D, Sharapov SZ, Hayward C, Rudan I, Campbell H, Aulchenko Y, et al. High-performance mixed models based genome-wide association analysis with omicABEL software. F1000Res. 2014;3:200.

    Article  PubMed  PubMed Central  Google Scholar 

  122. German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ. Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. Genet Epidemiol. 2020;44:248–60.

    Article  PubMed  Google Scholar 

  123. Wen Y, Wang W, Guo X, Zhang F. PAPA: a flexible tool for identifying pleiotropic pathways using genome-wide association study summaries. Bioinformatics. 2016;32:946–8.

    Article  CAS  PubMed  Google Scholar 

  124. Yaspan BL, Bush WS, Torstenson ES, Ma D, Pericak-Vance MA, Ritchie MD, et al. Genetic analysis of biological pathway data through genomic randomization. Hum Genet. 2011;129:563–71.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Alonso-Gonzalez A, Calaza M, Rodriguez-Fontenla C, Carracedo A. Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes. BMC Med Genomics. 2019;12:143.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Tucker G, Price AL, Berger B. Improving the power of GWAS and avoiding confounding from population stratification with PC-select. Genetics. 2014;197:1045–9.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Zhang W, Dai X, Wang Q, Xu S, Zhao PX. PEPIS: a pipeline for estimating epistatic effects in quantitative trait locus mapping and genome-wide association studies. PLoS Comput Biol. 2016;12:e1004925.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Cheng Z, Chu H, Fan Y, Li C, Song Y-Q, Zhou J, et al. PExFInS: an integrative post-GWAS explorer for functional indels and SNPs. Sci Rep. 2015;5:17302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. George G, Gan S, Huang Y, Appleby P, Nar AS, Venkatesan R, et al. PheGWAS: a new dimension to visualize GWAS across multiple phenotypes. Bioinformatics. 2020;36:2500–5.

    Article  CAS  PubMed  Google Scholar 

  130. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics. 2010;26:1205–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD. Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data. Pac Symp Biocomput. 2010:315–26.

    Google Scholar 

  132. Slifer SH. PLINK: key functions for data analysis. Curr Protoc Hum Genet. 2018;97:e59.

    Article  PubMed  Google Scholar 

  133. Kooperberg C, LeBlanc M. Increasing the power of identifying gene × gene interactions in genome-wide association studies. Genet Epidemiol. 2008;32:255–63.

    Article  PubMed  PubMed Central  Google Scholar 

  134. Tsai S-F, Tung C-W, Tsai C-A, Liao C-T. An exhaustive scan method for SNP main effects and SNP × SNP interactions over highly homozygous genomes. J Comput Biol. 2017;24:1254–64.

    Article  CAS  PubMed  Google Scholar 

  135. van der Most PJ, Vaez A, Prins BP, Munoz ML, Snieder H, Alizadeh BZ, et al. QCGWAS: a flexible R package for automated quality control of genome-wide association results. Bioinformatics. 2014;30:1185–6.

    Article  PubMed  CAS  Google Scholar 

  136. Wigginton JE, Cutler DJ, Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum Genet. 2005;76:887–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Daye ZJ, Li H, Wei Z. A powerful test for multiple rare variants association studies that incorporates sequencing qualities. Nucl Acids Res. 2012;40:e60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Abecasis GR, Cardon LR, Cookson WOC. A general test of association for quantitative traits in nuclear families. Am J Hum Genet. 2000;66:279–92.

    Article  CAS  PubMed  Google Scholar 

  139. Julienne H, Shi H, Pasaniuc B, Aschard H. RAISS: robust and accurate imputation from summary statistics. Bioinformatics. 2019;35:4837–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Shabalin AA, Hattab MW, Clark SL, Chan RF, Kumar G, Aberg KA, et al. RaMWAS: fast methylome-wide association study pipeline for enrichment platforms. Bioinformatics. 2018;34:2283–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Gel B, Díez-Villanueva A, Serra E, Buschbeck M, Peinado MA, Malinverni R. regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests. Bioinformatics. 2016;32:289–91.

    Article  CAS  PubMed  Google Scholar 

  142. Haller T, Kals M, Esko T, Mägi R, Fischer K. RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations. Brief Bioinformatics. 2015;16:39–44.

    Article  CAS  PubMed  Google Scholar 

  143. Zhbannikov IY, Arbeev KG, Yashin AI. rqt: an R package for gene-level meta-analysis. Bioinformatics. 2017;33:3129–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 2016;17:238.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  145. Wang B, Cunningham JM. (Holly) Yang X. Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data. Bioinformatics. 2015;31:3043–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Wang GT, Li B, Lyn Santos-Cortez RP, Peng B, Leal SM. Power analysis and sample size estimation for sequence-based association studies. Bioinformatics. 2014;30:2377–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Tzeng J-Y, Zhang D, Pongpanich M, Smith C, McCarthy MI, Sale MM, et al. Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression. Am J Hum Genet. 2011;89:277–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X. Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. 2013;92:841–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Wang S, Dvorkin D, Da Y. SNPEVG: a graphical tool for GWAS graphing with mouse clicks. BMC Bioinformatics. 2012;13:319.

    Article  PubMed  PubMed Central  Google Scholar 

  150. Mei H, Li L, Jiang F, Simino J, Griswold M, Mosley T, et al. snpGeneSets: an R package for genome-wide study annotation. G3 Genes Genome Genet. 2016;6:4087–95.

    Google Scholar 

  151. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012;28:3326–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Clayton D. snpStats: SnpMatrix and XSnpMatrix classes and methods version 1.40.0 from Bioconductor. [cited 2021 Apr 18]. https://rdrr.io/bioc/snpStats/.

  153. Curk T, Rot G, Zupan B. SNPsyn: detection and exploration of SNP–SNP interactions. Nucl Acids Res. 2011;39:W444–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511.

    Article  CAS  PubMed  Google Scholar 

  155. Kochunov P, Jahanshad N, Marcus D, Winkler A, Sprooten E, Nichols TE, et al. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. NeuroImage. 2015;111:300–11.

    Article  PubMed  Google Scholar 

  156. Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, et al. SNP-based pathway enrichment analysis for genome-wide association studies. BMC Bioinformatics. 2011;12:99.

    Article  PubMed  PubMed Central  Google Scholar 

  157. Schlauch D, Fier H, Lange C. Identification of genetic outliers due to sub-structure and cryptic relationships. Bioinformatics. 2017;33:1972–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Syed H, Jorgensen AL, Morris AP. SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes. BMC Bioinformatics. 2017;18:265.

    Article  PubMed  PubMed Central  Google Scholar 

  159. Chen L, Qin ZS. traseR: an R package for performing trait-associated SNP enrichment analysis in genomic intervals. Bioinformatics. 2016;32:1214–6.

    Article  CAS  PubMed  Google Scholar 

  160. Collins C, Didelot X. A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination. PLoS Comput Biol. 2018;14:e1005958.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  161. Alexander J, Mantzaris D, Georgitsi M, Drineas P, Paschou P. Variant Ranker: a web-tool to rank genomic data according to functional significance. BMC Bioinformatics. 2017;18:341.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  162. Burren OS, Guo H, Wallace C. VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes. Bioinformatics. 2014;30:3342–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Sun R, Xia X, Chong KC, Zee BC-Y, WKK W, Wang MH. wtest: an integrated R package for genetic epistasis testing. BMC Med Genomics. 2019;12:180.

    Article  PubMed  PubMed Central  Google Scholar 

  164. Fang H, Knezevic B, Burnham KL, Knight JC. XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med. 2016;8:129.

    Article  PubMed  PubMed Central  Google Scholar 

  165. Montes JM, Melchinger AE, Reif JC. Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 2007;12:433–6.

    Article  CAS  PubMed  Google Scholar 

  166. Agrama HA, Eizenga GC, Yan W. Association mapping of yield and its components in rice cultivars. Mol Breed. 2007;19:341–56.

    Article  Google Scholar 

  167. Gupta PK, Rustgi S, Kulwal PL. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol. 2005;57:461–85.

    Article  CAS  PubMed  Google Scholar 

  168. Zhao Y, Wang H, Chen W, Li Y. Genetic structure, linkage disequilibrium and association mapping of Verticillium wilt resistance in elite cotton (Gossypium hirsutum L.) germplasm population. PLoS One. 2014;9:e86308.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  169. Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, et al. Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol. 2012;30:174–8.

    Article  CAS  PubMed  Google Scholar 

  170. Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe A, Mitsuoka C, et al. MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytol. 2013;200:276–83.

    Article  CAS  PubMed  Google Scholar 

  171. Garris AJ, McCOUCH SR, Kresovich S. Population structure and its effect on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics. 2003;165:759–69.

    Article  PubMed  PubMed Central  Google Scholar 

  172. Olsen KM, Caicedo AL, Polato N, McClung A, McCouch S, Purugganan MD. Selection under domestication: evidence for a sweep in the rice waxy genomic region. Genetics. 2006;173:975–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, et al. A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics. 1998;148:479–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Huang X, Han B. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol. 2014;65:531–51.

    Article  CAS  PubMed  Google Scholar 

  175. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, et al. Genetic properties of the maize nested association mapping population. Science. 2009;325:737–40.

    Article  CAS  PubMed  Google Scholar 

  176. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The genetic architecture of maize flowering time. Science. 2009;325:714–8.

    Article  CAS  PubMed  Google Scholar 

  177. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, et al. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet. 2011;43:163–8.

    Article  CAS  PubMed  Google Scholar 

  178. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci U S A. 2011;108:6893–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet. 2011;43:159–62.

    Article  CAS  PubMed  Google Scholar 

  180. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, et al. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 2009;5:e1000551.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  181. Huang X, Paulo M-J, Boer M, Effgen S, Keizer P, Koornneef M, et al. Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. Proc Natl Acad Sci U S A. 2011;108:4488–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Austin RS, Vidaurre D, Stamatiou G, Breit R, Provart NJ, Bonetta D, et al. Next-generation mapping of Arabidopsis genes. Plant J. 2011;67:715–25.

    Article  CAS  PubMed  Google Scholar 

  183. James GV, Patel V, Nordström KJ, Klasen JR, Salomé PA, Weigel D, et al. User guide for mapping-by-sequencing in Arabidopsis. Genome Biol. 2013;14:1–13.

    Article  CAS  Google Scholar 

  184. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42:961–7.

    Article  CAS  PubMed  Google Scholar 

  185. Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, et al. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell. 2009;21:2194–202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Platt A, Vilhjálmsson BJ, Nordborg M. Conditions under which genome-wide association studies will be positively misleading. Genetics. 2010;186:1045–52.

    Article  PubMed  PubMed Central  Google Scholar 

  187. Vilhjálmsson BJ, Nordborg M. The nature of confounding in genome-wide association studies. Nat Rev Genet. 2013;14:1–2.

    Article  PubMed  CAS  Google Scholar 

  188. Zhang Z, Buckler ES, Casstevens TM, Bradbury PJ. Software engineering the mixed model for genome-wide association studies on large samples. Brief Bioinformatics. 2009;10:664–75.

    Article  PubMed  CAS  Google Scholar 

  189. Qin H, Chen M, Yi X, Bie S, Zhang C, Zhang Y, et al. Identification of associated SSR markers for yield component and fiber quality traits based on frame map and upland cotton collections. PLoS One. 2015;10:e0118073.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  190. Wang Z, Qiang H, Zhao H, Xu R, Zhang Z, Gao H, et al. Association mapping for fiber-related traits and digestibility in alfalfa (Medicago sativa). Front Plant Sci. 2016;7:331.

    PubMed  PubMed Central  Google Scholar 

  191. Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2012;44:32–9.

    Article  CAS  Google Scholar 

  192. Jia G, Huang X, Zhi H, Zhao Y, Zhao Q, Li W, et al. A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nat Genet. 2013;45:957–61.

    Article  CAS  PubMed  Google Scholar 

  193. Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, et al. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet. 2013;45:43–50.

    Article  CAS  PubMed  Google Scholar 

  194. Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, et al. Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci U S A. 2013;110:453–8.

    Article  CAS  PubMed  Google Scholar 

  195. Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, Price AH, et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011;2:467.

    Article  PubMed  CAS  Google Scholar 

  196. Huang X, Kurata N, Wang Z-X, Wang A, Zhao Q, Zhao Y, et al. A map of rice genome variation reveals the origin of cultivated rice. Nature. 2012;490:497–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Wang M, Jiang N, Jia T, Leach L, Cockram J, Waugh R, et al. Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars. Theor Appl Genet. 2012;124:233–46.

    Article  PubMed  Google Scholar 

  198. Harper AL, Trick M, Higgins J, Fraser F, Clissold L, Wells R, et al. Associative transcriptomics of traits in the polyploid crop species Brassica napus. Nat Biotechnol. 2012;30:798–802.

    Article  CAS  PubMed  Google Scholar 

  199. Ling H-Q, Zhao S, Liu D, Wang J, Sun H, Zhang C, et al. Draft genome of the wheat A-genome progenitor Triticum urartu. Nature. 2013;496:87–90.

    Article  CAS  PubMed  Google Scholar 

  200. Seren Ü, Vilhjálmsson BJ, Horton MW, Meng D, Forai P, Huang YS, et al. GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell. 2012;24:4793–805.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q, Nordborg M. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet. 2012;44:1066–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. 2012;44:825–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Jin L, Lu Y, Xiao P, Sun M, Corke H, Bao J. Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theor Appl Genet. 2010;121:475–87.

    Article  PubMed  Google Scholar 

  204. Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, Droc G, et al. Genome-wide association mapping of root traits in a Japonica Rice Panel. Baxter I, editor. PLoS One. 2013;8:e78037.

    Google Scholar 

  205. Zhao W-G, Chung J-W, Kwon S-W, Lee J-H, Ma K-H, Park Y-J. Association analysis of physicochemical traits on eating quality in rice (Oryza sativa L.). Euphytica. 2013;191:9–21.

    Article  Google Scholar 

  206. Dang X, Thi TGT, Dong G, Wang H, Edzesi WM, Hong D. Genetic diversity and association mapping of seed vigor in rice (Oryza sativa L.). Planta. 2014;239:1309–19.

    Article  CAS  PubMed  Google Scholar 

  207. Lu Q, Zhang M, Niu X, Wang S, Xu Q, Feng Y, et al. Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics. 2015;16:1–17.

    Article  CAS  Google Scholar 

  208. Zhang N, Xu Y, Akash M, McCouch S, Oard JH. Identification of candidate markers associated with agronomic traits in rice using discriminant analysis. Theor Appl Genet. 2005;110:721–9.

    Article  CAS  PubMed  Google Scholar 

  209. Ma X, Feng F, Wei H, Mei H, Xu K, Chen S, et al. Genome-wide association study for plant height and grain yield in rice under contrasting moisture regimes. Front Plant Sci. 2016;7. http://journal.frontiersin.org/article/10.3389/fpls.2016.01801/full

  210. Pan Y, Zhang H, Zhang D, Li J, Xiong H, Yu J, et al. Genetic analysis of cold tolerance at the germination and booting stages in rice by association mapping. PLoS One. 2015;10:e0120590.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  211. Kumar V, Singh A, Mithra SVA, Krishnamurthy SL, Parida SK, Jain S, et al. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res. 2015;22:133–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Donde R, Mohapatra S, Baksh SY, Padhy B, Mukherjee M, Roy S, et al. Identification of QTLs for high grain yield and component traits in new plant types of rice. bioRxiv. 2020.

    Google Scholar 

  213. Anandan A, Anumalla M, Pradhan SK, Ali J. Population structure, diversity and trait association analysis in rice (Oryza sativa L.) germplasm for early seedling vigor (ESV) using trait linked SSR markers. PLoS One. 2016;11:e0152406.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  214. Pandit E, Tasleem S, Barik SR, Mohanty DP, Nayak DK, Mohanty SP, et al. Genome-wide association mapping reveals multiple QTLs governing tolerance response for seedling stage chilling stress in indica rice. Front Plant Sci. 2017;8:552.

    Article  PubMed  PubMed Central  Google Scholar 

  215. Swamy BM, Shamsudin NAA, Abd Rahman SN, Mauleon R, Ratnam W, Cruz MTS, et al. Association mapping of yield and yield-related traits under reproductive stage drought stress in rice (Oryza sativa L.). Rice. 2017;10:1–13.

    Article  Google Scholar 

  216. Rebolledo MC, Peña AL, Duitama J, Cruz DF, Dingkuhn M, Grenier C, et al. Combining image analysis, genome wide association studies and different field trials to reveal stable genetic regions related to panicle architecture and the number of spikelets per panicle in rice. Front Plant Sci. 2016;7:1384.

    Article  PubMed  PubMed Central  Google Scholar 

  217. Yuan J, Wang X, Zhao Y, Khan NU, Zhao Z, Zhang Y, et al. Genetic basis and identification of candidate genes for salt tolerance in rice by GWAS. Sci Rep. 2020;10:9958.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  218. Schläppi MR, Jackson AK, Eizenga GC, Wang A, Chu C, Shi Y, et al. Assessment of five chilling tolerance traits and GWAS mapping in rice using the USDA mini-core collection. Front Plant Sci. 2017;8. https://www.frontiersin.org/articles/10.3389/fpls.2017.00957/full

  219. Bollinedi H, Yadav AK, Vinod KK, Gopala Krishnan S, Bhowmick PK, Nagarajan M, et al. Genome-Wide Association study reveals novel Marker-Trait Associations (MTAs) governing the localization of Fe and Zn in the rice grain. Front Genet. 2020;11. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188789/

  220. Kumar J, Saripalli G, Gahlaut V, Goel N, Meher PK, Mishra KK, et al. Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS. Euphytica. 2018;214:219.

    Article  CAS  Google Scholar 

  221. Caicedo AL, Williamson SH, Hernandez RD, Boyko A, Fledel-Alon A, York TL, et al. Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet. 2007;3:e163.

    Article  PubMed Central  CAS  Google Scholar 

  222. Zhu Q, Zheng X, Luo J, Gaut BS, Ge S. Multilocus analysis of nucleotide variation of Oryza sativa and its wild relatives: severe bottleneck during domestication of rice. Mol Biol Evol. 2007;24:875–88.

    Article  CAS  PubMed  Google Scholar 

  223. Mather KA, Caicedo AL, Polato NR, Olsen KM, McCouch S, Purugganan MD. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics. 2007;177:2223–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, et al. Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science. 2007;317:338–42.

    Article  CAS  PubMed  Google Scholar 

  225. Nordborg M, Weigel D. Next-generation genetics in plants. Nature. 2008;456:720–3.

    Article  CAS  PubMed  Google Scholar 

  226. Zhang D, Zhang H, Wang M, Sun J, Qi Y, Wang F, et al. Genetic structure and differentiation of Oryza sativa L. in China revealed by microsatellites. Theor Appl Genet. 2009;119:1105–17.

    Article  CAS  PubMed  Google Scholar 

  227. McNally KL, Childs KL, Bohnert R, Davidson RM, Zhao K, Ulat VJ, et al. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc Natl Acad Sci U S A. 2009;106:12273–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Tian Z, Qian Q, Liu Q, Yan M, Liu X, Yan C, et al. Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proc Natl Acad Sci U S A. 2009;106:21760–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Goutam Kumar Dash, S. Sabarinathan, Ravindra Donde, Gayatri Gouda, Manoj Kumar Gupta, Lambodar Behera & Padmini Swain

  2. Department of Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India

    S. Sabarinathan

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  1. Goutam Kumar Dash
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  2. S. Sabarinathan
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  3. Ravindra Donde
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  4. Gayatri Gouda
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  5. Manoj Kumar Gupta
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  7. Padmini Swain
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  1. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Manoj Kumar Gupta

  2. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Lambodar Behera

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Dash, G.K. et al. (2021). Status and Prospectives of Genome-Wide Association Studies in Plants. In: Gupta, M.K., Behera, L. (eds) Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3993-7_19

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