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Genome-Wide Association Analyses to Identify SNPs Related to Drought Tolerance

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Abscisic Acid

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2462))

Abstract

Drought stress is a serious agronomic problem resulting in significant yield losses globally. Breeding cultivars with drought tolerance is an important strategy that can be used to address this problem. Drought tolerance, however, is a complex multigenic trait, making advancements with conventional breeding approaches very challenging. This emphasizes the importance of dissecting the genetics of this trait and the identification and cloning of genes responsible for drought tolerance. With the rapid development of sequencing technologies and analytic methodologies, genome-wide association study (GWAS) has become an important tool for detecting natural variations underlying complex traits in crops. Identified loci can serve as targets for genomic selection or precise editing that enables the molecular design of new cultivars. This chapter describes the pipeline of statistical methods used in GWAS analysis, and covers field design, quality control, population structure control, association tests, and visualization of data. GWAS methodology used to dissect the genetic basis of drought tolerance is presented, and perspectives for optimizing the design and analysis of GWAS are discussed. The provided information serves as a valuable resource for researchers interested in GWAS technology.

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References

  1. Gupta A, Rico-Medina A, Cano-Delgado AI (2020) The physiology of plant responses to drought. Science 368(6488):266–269. https://doi.org/10.1126/science.aaz7614

    Article  CAS  PubMed  Google Scholar 

  2. Levitt J (1980) Responses of plants to environmental stresses. Volume II. Water, radiation, salt, and other stresses. Academic Press, London. 607 p

    Google Scholar 

  3. Yoshida T, Mogami J, Yamaguchi-Shinozaki K (2014) ABA-dependent and ABA-independent signaling in response to osmotic stress in plants. Curr Opin Plant Biol 21:133–139. https://doi.org/10.1016/j.pbi.2014.07.009

    Article  CAS  PubMed  Google Scholar 

  4. Liu S, Qin F (2021) Genetic dissection of maize drought tolerance for trait improvement. Mol Breed 41(2):8. https://doi.org/10.1007/s11032-020-01194-w

    Article  Google Scholar 

  5. Yu J, Buckler ES (2006) Genetic association map** and genome organization of maize. Curr Opin Biotechnol 17(2):155–160. https://doi.org/10.1016/j.copbio.2006.02.003

    Article  CAS  PubMed  Google Scholar 

  6. Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551. https://doi.org/10.1146/annurev-arplant-050213-035715

    Article  CAS  PubMed  Google Scholar 

  7. Huang X, Zhao Y, Wei X et al (2011) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44(1):32–39. https://doi.org/10.1038/ng.1018

    Article  CAS  PubMed  Google Scholar 

  8. Jia G, Huang X, Zhi H et al (2013) A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nat Genet 45(8):957–961. https://doi.org/10.1038/ng.2673

    Article  CAS  PubMed  Google Scholar 

  9. Liu S, Wang X, Wang H et al (2013) Genome-wide analysis of ZmDREB genes and their association with natural variation in drought tolerance at seedling stage of Zea mays L. PLoS Genet 9(9):e1003790. https://doi.org/10.1371/journal.pgen.1003790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang X, Wang H, Liu S et al (2016) Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat Genet 48(10):1233–1241. https://doi.org/10.1038/ng.3636

    Article  CAS  PubMed  Google Scholar 

  11. Li X, Guo Z, Lv Y et al (2017) Genetic control of the root system in rice under normal and drought stress conditions by genome-wide association study. PLoS Genet 13(7):e1006889. https://doi.org/10.1371/journal.pgen.1006889

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bhandari A, Sandhu N, Bartholome J et al (2020) Genome-Wide Association Study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel. Rice 13(1):53. https://doi.org/10.1186/s12284-020-00406-3

    Article  PubMed  PubMed Central  Google Scholar 

  13. Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42(11):961–967. https://doi.org/10.1038/ng.695

    Article  CAS  PubMed  Google Scholar 

  14. Chia JM, Song C, Bradbury PJ et al (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet 44(7):803–807. https://doi.org/10.1038/ng.2313

    Article  CAS  PubMed  Google Scholar 

  15. Xu J, Chen G, Hermanson PJ et al (2019) Population-level analysis reveals the widespread occurrence and phenotypic consequence of DNA methylation variation not tagged by genetic variation in maize. Genome Biol 20(1):243. https://doi.org/10.1186/s13059-019-1859-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu S, Li C, Wang H et al (2020) Map** regulatory variants controlling gene expression in drought response and tolerance in maize. Genome Biol 21(1):163. https://doi.org/10.1186/s13059-020-02069-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mao H, Wang H, Liu S et al (2015) A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat Commun 6:8326. https://doi.org/10.1038/ncomms9326

    Article  CAS  PubMed  Google Scholar 

  18. **ang Y, Sun X, Gao S et al (2017) Deletion of an endoplasmic reticulum stress response element in a ZmPP2C-A gene facilitates drought tolerance of maize seedlings. Mol Plant 10(3):456–469. https://doi.org/10.1016/j.molp.2016.10.003

    Article  CAS  PubMed  Google Scholar 

  19. Lu Y, Zhang S, Shah T et al (2010) Joint linkage-linkage disequilibrium map** is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. Proc Natl Acad Sci U S A 107(45):19585–19590. https://doi.org/10.1073/pnas.1006105107

    Article  PubMed  PubMed Central  Google Scholar 

  20. Xue Y, Warburton ML, Sawkins M et al (2013) Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions. Theor Appl Genet 126(10):2587–2596. https://doi.org/10.1007/s00122-013-2158-x

    Article  CAS  PubMed  Google Scholar 

  21. Thirunavukkarasu N, Hossain F, Arora K et al (2014) Functional mechanisms of drought tolerance in subtropical maize (Zea mays L.) identified using genome-wide association map**. BMC Genomics 15(1):1182–1182. https://doi.org/10.1186/1471-2164-15-1182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Farfan ID, De La Fuente GN, Murray SC et al (2015) Genome wide association study for drought, aflatoxin resistance, and important agronomic traits of maize hybrids in the sub-tropics. PLoS One 10(2):e0117737. https://doi.org/10.1371/journal.pone.0117737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pantaliao GF, Narciso M, Guimaraes C et al (2016) Genome wide association study (GWAS) for grain yield in rice cultivated under water deficit. Genetica 144(6):651–664. https://doi.org/10.1007/s10709-016-9932-z

    Article  CAS  PubMed  Google Scholar 

  24. Ma X, Feng F, Wei H et al (2016) Genome-Wide Association Study for plant height and grain yield in rice under contrasting moisture regimes. Front Plant Sci 7:1801. https://doi.org/10.3389/fpls.2016.01801

    Article  PubMed  PubMed Central  Google Scholar 

  25. Li L, Mao X, Wang J et al (2019) Genetic dissection of drought and heat-responsive agronomic traits in wheat. Plant Cell Environ 42(9):2540–2553. https://doi.org/10.1111/pce.13577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gahlaut V, Jaiswal V, Singh S et al (2019) Multi-locus genome wide association map** for yield and its contributing traits in hexaploid wheat under different water regimes. Sci Rep 9(1):19486. https://doi.org/10.1038/s41598-019-55520-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Abou-Elwafa SF, Shehzad T (2021) Genetic diversity, GWAS and prediction for drought and terminal heat stress tolerance in bread wheat (Triticum aestivum L.). Genet Resour Crop Evol 68(2):711–728. https://doi.org/10.1007/s10722-020-01018-y

    Article  CAS  Google Scholar 

  28. Pace J, Gardner C, Romay C et al (2015) Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genomics 16(1):47. https://doi.org/10.1186/s12864-015-1226-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Phung NT, Mai CD, Hoang GT et al (2016) Genome-wide association map** for root traits in a panel of rice accessions from Vietnam. BMC Plant Biol 16:64. https://doi.org/10.1186/s12870-016-0747-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zaidi PH, Seetharam K, Krishna G et al (2016) Genomic regions associated with root traits under drought stress in tropical maize (Zea mays L.). PLoS One 11(10):e0164340. https://doi.org/10.1371/journal.pone.0164340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang X, Mi Y, Mao H et al (2020) Genetic variation in ZmTIP1 contributes to root hair elongation and drought tolerance in maize. Plant Biotechnol J 18(5):1271–1283. https://doi.org/10.1111/pbi.13290

    Article  CAS  PubMed  Google Scholar 

  32. Guo J, Li C, Zhang X et al (2020) Transcriptome and GWAS analyses reveal candidate gene for seminal root length of maize seedlings under drought stress. Plant Sci 292:110380. https://doi.org/10.1016/j.plantsci.2019.110380

    Article  CAS  PubMed  Google Scholar 

  33. Fu J, Cheng Y, Linghu J et al (2013) RNA sequencing reveals the complex regulatory network in the maize kernel. Nat Commun 4:2832. https://doi.org/10.1038/ncomms3832

    Article  CAS  PubMed  Google Scholar 

  34. Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association map** that accounts for multiple levels of relatedness. Nat Genet 38(2):203–208. https://doi.org/10.1038/ng1702

    Article  CAS  PubMed  Google Scholar 

  35. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120. https://doi.org/10.1093/bioinformatics/btu170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hardy OJ, Vekemans X (2002) spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620. https://doi.org/10.1046/j.1471-8286.2002.00305.x

    Article  CAS  Google Scholar 

  38. Flint-Garcia SA, Thuillet AC, Yu J et al (2005) Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44(6):1054–1064. https://doi.org/10.1111/j.1365-313X.2005.02591.x

    Article  CAS  PubMed  Google Scholar 

  39. Cavanagh C, Morell M, Mackay I et al (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11(2):215–221. https://doi.org/10.1016/j.pbi.2008.01.002

    Article  CAS  PubMed  Google Scholar 

  40. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325(5941):714–718. https://doi.org/10.1126/science.1174276

    Article  CAS  PubMed  Google Scholar 

  41. **ao Y, Tong H, Yang X et al (2016) Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol 210(3):1095–1106. https://doi.org/10.1111/nph.13814

    Article  CAS  PubMed  Google Scholar 

  42. Kim NS, Park NI, Kim SH et al (2000) Isolation of TC/AG repeat microsatellite sequences for fingerprinting rice blast fungus and their possible horizontal transfer to plant species. Mol Cell 10(2):127–134. https://doi.org/10.1007/s10059-000-0127-0

    Article  CAS  Google Scholar 

  43. Wang W, Mauleon R, Hu Z et al (2018) Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557(7703):43–49. https://doi.org/10.1038/s41586-018-0063-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4):1567–1587

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19(9):1655–1664. https://doi.org/10.1101/gr.094052.109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909. https://doi.org/10.1038/ng1847

    Article  CAS  PubMed  Google Scholar 

  47. Bradbury PJ, Zhang Z, Kroon DE et al (2007) TASSEL: software for association map** of complex traits in diverse samples. Bioinformatics 23(19):2633–2635. https://doi.org/10.1093/bioinformatics/btm308

    Article  CAS  PubMed  Google Scholar 

  48. Wang Y, Tao Z, Wang W et al (2020) Molecular variation in a functionally divergent homolog of FCA regulates flowering time in Arabidopsis thaliana. Nat Commun 11(1):5830. https://doi.org/10.1038/s41467-020-19666-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Thornsberry JM, Goodman MM, Doebley J et al (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28(3):286–289. https://doi.org/10.1038/90135

    Article  CAS  PubMed  Google Scholar 

  50. 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(1):43–50. https://doi.org/10.1038/ng.2484

    Article  CAS  PubMed  Google Scholar 

  51. Liu H, Luo X, Niu L et al (2017) Distant eQTLs and Non-coding sequences play critical roles in regulating gene expression and quantitative trait variation in maize. Mol Plant 10(3):414–426. https://doi.org/10.1016/j.molp.2016.06.016

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The authors would like to thank Dr Hongwei Wang for the helpful discussion. This research was supported by the National Natural Science Foundation of China (31625022 and 31971952), Bei**g Outstanding Young Scientist Program (BJJWZYJH01201910019026), and the National Key Research and Development Plan of China (2016YFD0100605).

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Authors and Affiliations

  1. College of Biological Sciences, China Agricultural University, Bei**g, China

    Shengxue Liu & Feng Qin

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  1. Shengxue Liu
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  2. Feng Qin
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Correspondence to Feng Qin .

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Editors and Affiliations

  1. Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany

    Takuya Yoshida

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Liu, S., Qin, F. (2022). Genome-Wide Association Analyses to Identify SNPs Related to Drought Tolerance. In: Yoshida, T. (eds) Abscisic Acid. Methods in Molecular Biology, vol 2462. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2156-1_16

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  • DOI: https://doi.org/10.1007/978-1-0716-2156-1_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2155-4

  • Online ISBN: 978-1-0716-2156-1

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