Advances in genome-wide association studies of complex traits in rice

  • Qin Wang
  • Jiali Tang
  • Bin Han
  • Xuehui HuangEmail author


Genome-wide association studies (GWAS), genetic surveys of the whole genome to detect variants associated with a trait in natural populations, are a powerful approach for dissecting complex traits. This genetic mapping approach has been applied in rice over the last 10 years. During the last decade, GWAS was used to identify the loci underlying tens of rice traits, and several important genes were detected in GWAS and further confirmed in follow-up functional experiments. In this review, we present an overview of the whole process in a typical GWAS, including population design, genotyping, phenotyping and analysis methods. Recent advances in rice GWAS are also provided, including several examples of the functional characterization of candidate genes. The possible breakthroughs of rice GWAS in the next decade are discussed with regard to their application in breeding, the consideration of epistatic interactions and in-depth functional annotations of DNA elements and genetic variants throughout the rice genome.



The research activities at our laboratory have been supported mainly by the National Key Research and Development Program of China (2016YFD0100902), the National Natural Science Foundation of China (31825015), Program of Shanghai Academic Research Leader (18XD1402900), Innovation Program of Shanghai Municipal Education Commission (2017-01-07-00-02-E00039) for supporting our research. We apologize to any authors whose work may not have been addressed owing to length restrictions.

Author contribution

QW, JT, BH and XH conceived, designed and wrote this review manuscript and prepared the figures.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Atwell S, Huang YS, Vilhjalmsson BJ et al (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631PubMedPubMedCentralCrossRefGoogle Scholar
  2. Bandillo N, Raghavan C, Muyco PA et al (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6:11PubMedPubMedCentralCrossRefGoogle Scholar
  3. Bellot P, de los Campos G, Perez-Enciso M (2018) Can deep learning improve genomic prediction of complex human traits? Genetics 210:809–819PubMedPubMedCentralCrossRefGoogle Scholar
  4. Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223PubMedPubMedCentralCrossRefGoogle Scholar
  5. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefPubMedCentralGoogle Scholar
  6. Burton PR, Clayton DG, Cardon LR et al (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  7. Caicedo AL, Williamson SH, Hernandez RD et al (2007) Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet 3:1745–1756PubMedCrossRefPubMedCentralGoogle Scholar
  8. Carpentier MC, Manfroi E, Wei FJ et al (2019) Retrotranspositional landscape of Asian rice revealed by 3000 genomes. Nat Commun 10:24PubMedPubMedCentralCrossRefGoogle Scholar
  9. Chen W, Gao Y, Xie W et al (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46:714–721PubMedCrossRefPubMedCentralGoogle Scholar
  10. Chen W, Wang W, Peng M et al (2016) Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat Commun 7:12767PubMedPubMedCentralCrossRefGoogle Scholar
  11. Chen J, Zhou H, Xie W et al (2019) Genome-wide association analyses reveal the genetic basis of combining ability in rice. Plant Biotechnol Journal. CrossRefGoogle Scholar
  12. Clark RM, Schweikert G, Toomajian C et al (2007) Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317:338–342PubMedCrossRefPubMedCentralGoogle Scholar
  13. Crowell S, Korniliev P, Falcao A et al (2016) Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters. Nat Commun 7:10527PubMedPubMedCentralCrossRefGoogle Scholar
  14. Dell’Acqua M, Gatti DM, Pea G et al (2015) Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol 16:167PubMedPubMedCentralCrossRefGoogle Scholar
  15. Dong H, Zhao H, Li S et al (2018) Genome-wide association studies reveal that members of bHLH subfamily 16 share a conserved function in regulating flag leaf angle in rice (Oryza sativa). PLoS Genet 14:e1007323PubMedPubMedCentralCrossRefGoogle Scholar
  16. Duan P, Xu J, Zeng D et al (2017) Natural variation in the promoter of GSE5 contributes to grain size diversity in rice. Mol Plant 10:685–694PubMedCrossRefPubMedCentralGoogle Scholar
  17. Dunham I, Kundaje A, Aldred SF et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74CrossRefGoogle Scholar
  18. Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379PubMedPubMedCentralCrossRefGoogle Scholar
  19. Forsberg SK, Bloom JS, Sadhu MJ et al (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat Genet 49:497–503PubMedPubMedCentralCrossRefGoogle Scholar
  20. Gong J, Miao J, Zhao Y et al (2017) Dissecting the genetic basis of grain shape and chalkiness traits in hybrid rice using multiple collaborative populations. Mol Plant 10:1353–1356PubMedCrossRefPubMedCentralGoogle Scholar
  21. Guo Z, Yang W, Chang Y et al (2018) Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Mol Plant 11:789–805PubMedCrossRefPubMedCentralGoogle Scholar
  22. Hickey LT, Hafeez AN, Robinson H et al (2019) Breeding crops to feed 10 billion. Nat Biotechnol. CrossRefPubMedPubMedCentralGoogle Scholar
  23. Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551CrossRefGoogle Scholar
  24. Huang X, Feng Q, Qian Q et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076PubMedPubMedCentralCrossRefGoogle Scholar
  25. Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–976PubMedCrossRefPubMedCentralGoogle Scholar
  26. Huang X, Kurata N, Wei X et al (2012a) A map of rice genome variation reveals the origin of cultivated rice. Nature 490:497–502CrossRefGoogle Scholar
  27. Huang X, Zhao Y, Wei X et al (2012b) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39CrossRefGoogle Scholar
  28. Huang BE, Verbyla KL, Verbyla AP et al (2015a) MAGIC populations in crops: current status and future prospects. Theor Appl Genet 128:999–1017PubMedCrossRefPubMedCentralGoogle Scholar
  29. Huang X, Yang S, Gong J et al (2015b) Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nat Commun 6:6258PubMedPubMedCentralCrossRefGoogle Scholar
  30. Huang X, Yang S, Gong J et al (2016) Genomic architecture of heterosis for yield traits in rice. Nature 537:629–633CrossRefGoogle Scholar
  31. Kang HM, Zaitlen NA, Wad CM et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedPubMedCentralCrossRefGoogle Scholar
  32. Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354PubMedPubMedCentralCrossRefGoogle Scholar
  33. Kawakatsu T, Huang SSC, Jupe F et al (2016) Epigenomic diversity in a global collection of Arabidopsis thaliana accessions. Cell 166:492–505PubMedPubMedCentralCrossRefGoogle Scholar
  34. Kremling KAG, Chen SY, Su MH et al (2018) Dysregulation of expression correlates with rare-allele burden and fitness loss in maize. Nature 555:520–523PubMedCrossRefGoogle Scholar
  35. Krieger U, Lippman ZB, Zamir D (2010) The flowering gene SINGLE FLOWER TRUSS drives heterosis for yield in tomato. Nat Genet 42:459–463PubMedCrossRefPubMedCentralGoogle Scholar
  36. Li H, Peng Z, Yang X et al (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–72PubMedCrossRefGoogle Scholar
  37. Li W, Zhu Z, Chern M et al (2017) A natural allele of a transcription factor in rice confers broad-spectrum blast resistance. Cell 170:114–126CrossRefGoogle Scholar
  38. Lippert C, Listgarten J, Liu Y et al (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835PubMedCrossRefPubMedCentralGoogle Scholar
  39. Listgarten J, Lippert C, Robert C et al (2012) Improved linear mixed models for genome-wide association studies. Nat Methods 9:525–526PubMedPubMedCentralCrossRefGoogle Scholar
  40. Liu C, Ou S, Mao B et al (2018) Early selection of bZIP73 facilitated adaptation of japonica rice to cold climates. Nat Commun 9:3302PubMedPubMedCentralCrossRefGoogle Scholar
  41. Liu H, Wang Q, Chen M et al (2019a) Genome-wide identification and analysis of heterotic loci in three maize hybrids. Plant Biotechnol J. CrossRefPubMedPubMedCentralGoogle Scholar
  42. Liu J, Li M, Zhang Q et al (2019b) Exploring the molecular basis of heterosis for plant breeding. J Integr Plant Biol. CrossRefPubMedPubMedCentralGoogle Scholar
  43. Liu Q, Wang C, Jiao X et al (2019c) Hi-TOM: a platform for high-throughput tracking of mutations induced by CRISPR/Cas systems. Sci China Life Sci 62:1–7PubMedCrossRefPubMedCentralGoogle Scholar
  44. Ma X, Feng F, Zhang Y et al (2019) A novel rice grain size gene OsSNB was identified by genome-wide association study in natural population. PLoS Genet 15:e1008191PubMedPubMedCentralCrossRefGoogle Scholar
  45. Manolio TA, Collins FS, Cox NJ et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedPubMedCentralCrossRefGoogle Scholar
  46. Marouli E, Graff M, Medina-Gomez C et al (2017) Rare and low-frequency coding variants alter human adult height. Nature 542:186–190PubMedPubMedCentralCrossRefGoogle Scholar
  47. Mather KA, Caicedo AL, Polato NR et al (2007) The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:2223–2232PubMedPubMedCentralCrossRefGoogle Scholar
  48. McCouch SR, Wright MH, Tung CW et al (2016) Open access resources for genome-wide association mapping in rice. Nat Commun 7:11346PubMedPubMedCentralCrossRefGoogle Scholar
  49. McMullen MD, Kresovich S, Villeda HS et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740PubMedCrossRefPubMedCentralGoogle Scholar
  50. McNally KL, Childs KL, Bohnert R et al (2009) Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc Natl Acad Sci USA 106:12273–12278PubMedCrossRefPubMedCentralGoogle Scholar
  51. Meyer RS, Choi JY, Sanches M et al (2016) Domestication history and geographical adaptation inferred from a SNP map of African rice. Nat Genet 48:1083–1088PubMedCrossRefPubMedCentralGoogle Scholar
  52. Myles S, Peiffer J, Brown PJ et al (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202PubMedPubMedCentralCrossRefGoogle Scholar
  53. Navarro JAR, Wilcox M, Burgueno J et al (2017) A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet 49:476–480CrossRefGoogle Scholar
  54. Nordborg M, Weigel D (2008) Next-generation genetics in plants. Nature 456:720–723PubMedCrossRefPubMedCentralGoogle Scholar
  55. Ogawa D, Nonoue Y, Tsunematsu H et al (2018) Discovery of QTL alleles for grain shape in the Japan-MAGIC rice population using haplotype information. G3 (Bethesda, MD) 8:3559–3565CrossRefGoogle Scholar
  56. Ouyang Y (2019) Understanding and breaking down the reproductive barrier between Asian and African cultivated rice: a new start for hybrid rice breeding. Sci China Life Sci 62(8):1114–1116PubMedCrossRefPubMedCentralGoogle Scholar
  57. Reynolds D, Ball J, Bauer A et al (2019a) CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience. CrossRefPubMedPubMedCentralGoogle Scholar
  58. Reynolds D, Baret F, Welcker C et al (2019b) What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Sci 282:14–22PubMedCrossRefPubMedCentralGoogle Scholar
  59. Si L, Chen J, Huang X et al (2016) OsSPL13 controls grain size in cultivated rice. Nat Genet 48:447–456PubMedCrossRefPubMedCentralGoogle Scholar
  60. Shen L, Wang C, Fu Y et al (2018) QTL editing confers opposing yield performance in different rice varieties. J Integr Plant Biol 61:122–125Google Scholar
  61. Sun S, Wang T, Wang L et al (2018) Natural selection of a GSK3 determines rice mesocotyl domestication by coordinating strigolactone and brassinosteroid signaling. Nat Commun 9:2523PubMedPubMedCentralCrossRefGoogle Scholar
  62. Takeda S, Matsuoka M (2008) Genetic approaches to crop improvement: responding to environmental and population changes. Nat Rev Genet 9:444–457PubMedCrossRefPubMedCentralGoogle Scholar
  63. Tian Z, Qian Q, Liu Q et al (2009) Allelic diversities in rice starch biosynthesis lead to a diversearray of rice eating and cooking qualities. Proc Natl Acad Sci U S A 106(51):21760–21765PubMedPubMedCentralCrossRefGoogle Scholar
  64. Wang Q, Tian F, Pan Y et al (2014) A super powerful method for genome wide association study. PLoS ONE 9:e107684PubMedPubMedCentralCrossRefGoogle Scholar
  65. Wang Q, Xie W, Xing H et al (2015) Genetic architecture of natural variation in rice chlorophyll content revealed by a genome-wide association study. Mol Plant 8:946–957PubMedCrossRefPubMedCentralGoogle Scholar
  66. Wang H, Xu X, Vieira FG et al (2016) The power of inbreeding: NGS-based GWAS of rice reveals convergent evolution during rice domestication. Mol Plant 9:975–985PubMedCrossRefPubMedCentralGoogle Scholar
  67. Wang DR, Agosto-Perez FJ, Chebotarov D et al (2018a) An imputation platform to enhance integration of rice genetic resources. Nat Commun 9:3519PubMedPubMedCentralCrossRefGoogle Scholar
  68. Wang W, Mauleon R, Hu Z et al (2018b) Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557:43–49PubMedPubMedCentralCrossRefGoogle Scholar
  69. Wojcik GL, Graff M, Nishimura KK et al (2019) Genetic analyses of diverse populations improves discovery for complex traits. Nature 570:514–518PubMedCrossRefPubMedCentralGoogle Scholar
  70. Xiao N, Gao Y, Qian H et al (2018) Identification of genes related to cold tolerance and a functional allele that confers cold tolerance. Plant Physiol 177:1108–1123PubMedPubMedCentralCrossRefGoogle Scholar
  71. Xie W, Wang G, Yuan M et al (2015) Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection. Proc Natl Acad Sci USA 112:5411–5419CrossRefGoogle Scholar
  72. Xing Y, Zhang Q (2010) Genetic and molecular bases of rice yield. Annu Rev Plant Biol 61:421–442PubMedCrossRefPubMedCentralGoogle Scholar
  73. Yan H, Xu W, Xie J et al (2019) Variation of a major facilitator superfamily gene contributes to differential cadmium accumulation between rice subspecies. Nat Commun 10:2526CrossRefGoogle Scholar
  74. Yang W, Guo Z, Huang C et al (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087PubMedPubMedCentralCrossRefGoogle Scholar
  75. Yano K, Yamamoto E, Aya K et al (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48:927–934PubMedCrossRefPubMedCentralGoogle Scholar
  76. Yu JM, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208PubMedPubMedCentralCrossRefGoogle Scholar
  77. Zeng D, Tian Z, Rao Y et al (2017) Rational design of high-yield and superior-quality rice. Nat Plants 3:17031PubMedCrossRefPubMedCentralGoogle Scholar
  78. Zhang Q (2007) Strategies for developing green super rice. Proc Natl Acad Sci USA 104:16402–16409PubMedCrossRefPubMedCentralGoogle Scholar
  79. Zhang D, Zhang H, Wang M et al (2009) Genetic structure and differentiation of Oryza sativa L. in China revealed by microsatellites. Theor Appl Genet 119:1105–1117PubMedCrossRefPubMedCentralGoogle Scholar
  80. Zhang Z, Ersoz E, Lai CQ et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360PubMedPubMedCentralCrossRefGoogle Scholar
  81. Zhang Z, Zhao H, Li W et al (2018) Genome-wide association study of callus induction variation to explore the callus formation mechanism of rice. J Integr Plant Biol. CrossRefPubMedPubMedCentralGoogle Scholar
  82. Zhang C, Zhu J, Chen S et al (2019) Wx(lv), the ancestral allele of rice waxy gene. Mol Plant. CrossRefPubMedGoogle Scholar
  83. Zhao K, Tung CW, Eizenga GC et al (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467PubMedPubMedCentralCrossRefGoogle Scholar
  84. Zhao Q, Feng Q, Lu H et al (2018) Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat Genet 50:278–284CrossRefGoogle Scholar
  85. Zhou X, Huang X (2019) Genome-wide association studies in rice: how to solve the low power problems? Mol Plant 12:10–12PubMedCrossRefPubMedCentralGoogle Scholar
  86. Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824PubMedPubMedCentralCrossRefGoogle Scholar
  87. Zhou H, Li P, Xie W et al (2017) Genome-wide association analyses reveal the genetic basis of stigma exsertion in rice. Mol Plant 10:634–644PubMedCrossRefPubMedCentralGoogle Scholar
  88. Zhu Q, Zheng X, Luo J et al (2007) Multilocus analysis of nucleotide variation of Oryza sativa and its wild relatives: severe bottleneck during domestication of rice. Mol Biol Evol 24:875–888PubMedCrossRefPubMedCentralGoogle Scholar
  89. Zuo J, Li J (2014) Molecular dissection of complex agronomic traits of rice: a team effort by Chinese scientists in recent years. Natl Sci Rev 1:253–276CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Plant Molecular Sciences, College of Life SciencesShanghai Normal UniversityShanghaiChina
  2. 2.National Center for Gene Research, CAS Center for Excellence of Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

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