Abstract
Key message
The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.
Abstract
The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.
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Andolfatto P, Davison D, Erezyilmaz D, Hu TT, Mast J, Sunayama-Morita T, Stern DL (2011) Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Genome Res 21:610–617
Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT, Jiang R, Muliyati NW, Zhang X, Amer MA, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker JR, Faure N, Kniskern JM, Jones JD, Michael T, Nemri A, Roux F, Salt DE, Tang C, Todesco M, Traw MB, Weigel D, Marjoram P, Borevitz JO, Bergelson J, Nordborg M (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631
Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3:e3376
Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC, Altshuler D, Ardlie KG, Hirschhorn JN (2005) Demonstrating stratification in a European American population. Nat Genet 37:868–872
De Coninck A, De Baets B, Kourounis D, Verbosio F, Schenk O, Maenhout S, Fostier J (2016) Needles: toward large-scale genomic prediction with marker-by-environment interaction. Genetics 203:543–555
Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601
Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004
Dhanapal AP, Ray JD, Singh SK, Hoyos-Villegas V, Smith JR, Purcell LC, Andy King C, Cregan PB, Song Q, Fritschi FB (2015) Genome-wide association study (GWAS) of carbon isotope ratio (δ13C) in diverse soybean [Glycine max (L.) Merr.] genotypes. Theor Appl Genet 128:73–91
Ding K, Zhou K, Zhang J, Knight J, Zhang X, Shen Y (2005) The effect of haplotype-block definitions on inference of haplotype-block structure and htSNPs selection. Mol Biol Evol 22:148–159
Excoffier L, Lischer HE (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567
Farnir F, Coppieters W, Arranz JJ, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagenaar D, Georges M (2000) Extensive genome-wide linkage disequilibrium in cattle. Genome Res 10:220–227
Felsenstein J (1989) PHYLIP—phylogeny inference package (version 3.2). Cladistics 5:164–166
Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229
Hanson CH, Robinson HF, Comstock RE (1956) Biometrical studies of yield in segregating populations of Korean Lespedeza. Agron J 48:268
Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12
Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551
Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967
Jia G, Huang X, Zhi H, Zhao Y, Zhao Q, Li W, Chai Y, Yang L, Liu K, Lu H, Zhu C, Lu Y, Zhou C, Fan D, Weng Q, Guo Y, Huang T, Zhang L, Lu T, Feng Q, Hao H, Liu H, Lu P, Zhang N, Li Y, Guo E, Wang S, Wang S, Liu J, Zhang W, Chen G, Zhang B, Li W, Wang Y, Li H, Zhao B, Li J, Diao X, Han B (2013) A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nat Genet 45:957–961
Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768
Jonas E, de Koning DJ (2013) Does genomic selection have a future in plant breeding? Trends Biotechnol 31:497–504
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723
Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354
Karkkainen HP, Sillanpaa MJ (2012) Back to basics for Bayesian model building in genomic selection. Genetics 191:969–987
Li Z, Sillanpaa MJ (2012) Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theor Appl Genet 125:419–435
Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967
Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N, Liu J, Warburton ML, Cheng Y, Hao X, Zhang P, Zhao J, Liu Y, Wang G, Li J, Yan J (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50
Li S, Cao Y, He J, Zhao T, Gai J (2017) Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure. Theor Appl Genet. doi:10.1007/s00122-017-2960-y
Meng S, He J, Zhao T, Xing G, Li Y, Yang S, Lu J, Wang Y, Gai J (2016) Detecting the QTL-allele system of seed isoflavone content in Chinese soybean landrace population for optimal cross design and gene system exploration. Theor Appl Genet 129:1557–1576
Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
Mohammadi M, Tiede T, Smith KP (2015) PopVar: A genome-wide procedure for predicting genetic variance and correlated response in biparental breeding populations. Crop Sci 55:2068
Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD, Riera-Lizarazu O, Brown PJ, Acharya CB, Mitchell SE, Harriman J, Glaubitz JC, Buckler ES, Kresovich S (2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci USA 110:453–458
Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res 8:4321–4325
Nordborg M, Weigel D (2008) Next-generation genetics in plants. Nature 456:720–723
Pattaro C, Ruczinski I, Fallin DM, Parmigiani AG (2008) Haplotype block partitioning as a tool for dimensionality reduction in SNP association studies. BMC Genomics 9:405
Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2:e190
Peleman JD, van der Voort JR (2003) Breeding by design. Trends Plant Sci 8:330–334
Peng B, Kimmel M (2005) simuPOP: a forward-time population genetics simulation environment. Bioinformatics 21:3686–3687
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909
Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67:170–181
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
Rakitsch B, Lippert C, Stegle O, Borgwardt K (2013) A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29:206–214
Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644
Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL, Song Q, Thelen JJ, Cheng J, Xu D, Hellsten U, May GD, Yu Y, Sakurai T, Umezawa T, Bhattacharyya MK, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S, Goodstein D, Barry K, Futrell-Griggs M, Abernathy B, Du J, Tian Z, Zhu L, Gill N, Joshi T, Libault M, Sethuraman A, Zhang XC, Shinozaki K, Nguyen HT, Wing RA, Cregan P, Specht J, Grimwood J, Rokhsar D, Stacey G, Shoemaker RC, Jackson SA (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183
Segura V, Vilhjalmsson BJ, Platt A, Korte A, Seren U, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825–830
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423
Vazquez AI, Veturi Y, Behring M, Shrestha S, Kirst M, Resende MF Jr, de Los Campos G (2016) Increased proportion of variance explained and prediction accuracy of survival of breast cancer patients with use of whole-genome multiomic profiles. Genetics 203:1425–1438
Voight BF, Pritchard JK (2005) Confounding from cryptic relatedness in case–control association studies. PLoS Genet 1:e32
Wang N, Akey JM, Zhang K, Chakraborty R, Jin L (2002) Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation. Am J Hum Genet 71:1227–1234
Wang S-B, Feng J-Y, Ren W-L, Huang B, Zhou L, Wen Y-J, Zhang J, Dunwell JM, Xu S, Zhang Y-M (2016) Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep 6:19444
Weir BS (2008) Linkage disequilibrium and association mapping. Annu Rev Genom Hum Genet 9:129–142
Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208
Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc B 68:49–67
Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468
Zhang K, Deng M, Chen T, Waterman MS, Sun F (2002) A dynamic programming algorithm for haplotype block partitioning. Proc Natl Acad Sci USA 99:7335–7339
Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360
Zhang Y, He J, Wang Y, Xing G, Zhao J, Li Y, Yang S, Palmer RG, Zhao T, Gai J (2015a) Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. J Exp Bot 66:6311–6325
Zhang Y, Liu M, He J, Wang Y, Xing G, Li Y, Yang S, Zhao T, Gai J (2015b) Marker-assisted breeding for transgressive seed protein content in soybean [Glycine max (L.) Merr]. Theor Appl Genet 128:1061–1072
Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467
Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee SH, Wang W, Tian Z (2015) Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol 33:408–414
Acknowledgements
This work was supported by the China National Key R & D Program for Crop Breeding (2016YFD0100304), the China National Key Basic Research Program (2011CB1093), the China National Hightech R&D Program (2012AA101106), the Natural Science Foundation of China (31571695), the MOE 111 Project (B08025), Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT13073), the MOA Public Profit Program (201203026-4), the MOA CARS-04 program, the Jiangsu Higher Education PAPD Program, and the Jiangsu JCIC-MCP Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Communicated by Dr. Mikko J. Sillanpaa.
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He, J., Meng, S., Zhao, T. et al. An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding. Theor Appl Genet 130, 2327–2343 (2017). https://doi.org/10.1007/s00122-017-2962-9
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DOI: https://doi.org/10.1007/s00122-017-2962-9