A Guide to Genome‐Wide Association Mapping in Plants

Liana T. Burghardt1, Nevin D. Young2, Peter Tiffin1

1 Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, 2 Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota
Publication Name:  Current Protocols in Plant Biology
Unit Number:   
DOI:  10.1002/cppb.20041
Online Posting Date:  March, 2017
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

Genome‐wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.

Keywords: GWAS; genomics; QTL mapping; genotype; phenotype; association mapping

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • Introduction
  • Outline of the Basic Approach
  • Factors Affecting GWAS Performance
  • The Importance of Phenotype: Picking Traits and Minimizing Alternate Sources of Variance
  • Considerations for Generating Genomic Data and Calling Variants
  • Statistical Analysis, Interpretation of P‐Values, and Model Covariates
  • Prioritizing GWAS Candidates
  • Validation of Candidates
  • Limitations of GWAS
  • The Future of GWAS
  • Acknowledgements
  • Literature Cited
  • Figures
  • Tables
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

Literature Cited
  Aschard, H., Vilhjálmsson, B.J., Greliche, N., Morange, P.E., Trégouët, D.A., and Kraft, P. 2014. Maximizing the power of principal‐component analysis of correlated phenotypes in genome‐wide association studies. Am. J. Hum. Genet. 94:662‐676. doi: 10.1016/j.ajhg.2014.03.016.
  Atwell, S., Huang, Y.S., Vilhjálmsson, B.J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A.M., Hu, T.T., Jiang, R, Muliyati, N.W., Zhang, X., Amer, M.A., Baxter, I., Brachi, B., Chory, J, Dean, C., Debieu, M., de Meaux, J., Ecker, J.R., Faure, N., Kniskern, J.M., Jones, J.D., Michael, T., Nemri, A., Roux, F., Salt, D.E., Tang, C., Todesco, M., Traw, M.B., Weigel, D., Marjoram, P., Borevitz, J.O., Bergelson, J., and Nordborg, M. 2010. Genome‐wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627‐631. doi: 10.1038/nature08800.
  Barton, N.H. and Turelli, M. 1989. Evolutionary quantitative genetics: How little do we know? Annu. Rev. Genet. 23:337‐370. doi: 10.1146/annurev.ge.23.120189.002005.
  Bergelson, J. and Roux, F. 2010. Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana. Nat. Rev. Genet. 11:867‐879. doi: 10.1038/nrg2896.
  Bortesi, L., Zhu, C., Zischewski, J., Perez, L., Bassié, L., Nadi, R., Forni, G., Lade, S.B., Soto, E., Jin, X., Medina, V., Villorbina, G., Muñoz, P., Farré, G., Fischer, R., Twyman, R.M., Capell, T., Christou, P., and Schillberg, S. 2016. Patterns of CRISPR/Cas9 activity in plants, animals and microbes. Plant Biotechnol. J. 1‐14. doi: 10.1111/pbi.12634.
  Brachi, B., Meyer, C.G., Villoutreix, R., Platt, A., Morton, T.C., Roux, F., and Bergelson, J. 2015. Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 112:4032‐4037. doi: 10.1073/pnas.1421416112.
  Branca, A., Paape, T.D., Zhou, P., Briskine, R., Farmer, A.D., Mudge, J., Bharti, A.K., Woodward, J.E., May, G.D., Gentzbittel, L., Ben, C., Denny, R., Sadowsky, M.J., Ronfort, J., Bataillon, T., Young, N.D., and Tiffin, P. 2011. Whole‐genome nucleotide diversity, recombination, and linkage disequilibrium in the model legume Medicago truncatula. Proc. Natl. Acad. Sci. U.S.A. 108:E864‐870. doi: 10.1073/pnas.1104032108.
  Browning, S.R. and Browning, B.L. 2007. Rapid and accurate haplotype phasing and missing‐data inference for whole‐genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81:1084‐1097. doi: 10.1086/521987.
  Browning, B.L. and Browning, S.R. 2016. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98:116‐126. doi: 10.1016/j.ajhg.2015.11.020.
  Bunyavanich, S., Schadt, E.E., Himes, B.E., Lasky‐Su, J., Qiu, W., Lazarus, R., Ziniti, J.P., Cohain, A., Linderman, M., Torgerson, D.G., Eng, C.S., Pino‐Yanes, M., Padhukasahasram, B., Yang, J.J., Mathias, R.A., Beaty, T.H., Li, X., Graves, P., Romieu, I., Navarro Bdel, R., Salam, M.T., Vora, H., Nicolae, D.L., Ober, C., Martinez, F.D., Bleecker, E.R., Meyers, D.A., Gauderman, W.J., Gilliland, F., Burchard, E.G., Barnes, K.C., Williams, L.K., London, S.J., Zhang, B., Raby, B.A., and Weiss, S.T. 2014. Integrated genome‐wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis. BMC Med. Genomics 7:48. doi: 10.1186/1755‐8794‐7‐48.
  Bush, W.S. and Moore, J.H. 2012. Chapter 11: Genome‐wide association studies. PLoS Comput. Biol. 8:e1002822. doi: 10.1371/journal.pcbi.1002822.
  Chan, E.K.F., Rowe, H.C., Corwin, J.A., Joseph, B., and Kliebenstein, D.J. 2011. Combining genome‐wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol. 9:e1001125. doi: 10.1371/journal.pbio.1001125.
  Chen, W., Gao, Y., Xie, W., Gong, L., Lu, K., Wang, W., Li, Y., Liu, X., Zhang, H., Dong, H., Zhang, W., Zhang, L., Yu, S., Wang, G., Lian, X., and Luo, J. 2014. Genome‐wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat. Genet. 46:714‐721. doi: 10.1038/ng.3007.
  Chen, H., Wang, C., Conomos, M.P., Stilp, A.M., Li, Z., Sofer, T., Szpiro, A.A., Chen, W., Brehm, J.M., Celedón, J.C., Redline, S., Papanicolaou, G.J., Thornton, T.A., Laurie, C.C., Rice, K., and Lin, X. 2016. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98:653‐666. doi: 10.1016/j.ajhg.2016.02.012.
  Curtin, S.J., Tiffin, P., Guhlin, J., Trujillo, D.I., Burghardt, L.T., Atkins, P., Baltes, N.J., Denny, R., Voytas, D.F., Stupar, R.M., and Young, N.D. 2017. Validating GWAS candidates by characterizing genes that control quantitative variation in nodulation. Plant Physiol. [ePub ahead of print] doi: 10.1104/pp.16.01923.
  Dai, J.Y., Kooperberg, C., Leblanc, M., and Prentice, R.L. 2012. Two‐stage testing procedures with independent filtering for genome‐wide gene‐environment interaction. Biometrika 99:929‐944. doi: 10.1093/biomet/ass044.
  de los Campos, G., Hickey, J.M., Pong‐Wong, R., Daetwyler, H.D., and Calus, M.P.L. 2013. Whole‐genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327‐345. doi: 10.1534/genetics.112.143313.
  El‐Soda, M., Malosetti, M., Zwaan, B.J., Koornneef, M., and Aarts, M.G.M. 2014. Genotype × environment interaction QTL mapping in plants: Lessons from Arabidopsis. Trends Plant Sci. 19:390‐398. doi: 10.1016/j.tplants.2014.01.001.
  Falconer, D.S. and Mckay, T.E.C. 1996. Introduction to Quantitative Genetics, 4th ed. Pearson, London.
  Fisher, R.A. 1925. Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh, U.K.
  Gaut, B.S. and Long, A.D. 2003. The lowdown on linkage disequilibrium. Plant Cell 15:1502‐1506. doi: 10.1105/tpc.150730.
  Goddard, M. 2009. Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica 136:245‐257. doi: 10.1007/s10709‐008‐9308‐0.
  Goddard, M.E., Kemper, K.E., MacLeod, I.M., Chamberlain, A.J., and Hayes, B.J. 2016. Genetics of complex traits: Prediction of phenotype, identification of causal polymorphisms and genetic architecture. Proc. R. Soc. B Biol. Sci. 283:1173‐1186. doi: 10.1098/rspb.2016.0569.
  Goh, L. and Yap, V.B. 2009. Effects of normalization on quantitative traits in association test. BMC Bioinformatics 10:415. doi: 10.1186/1471‐2105‐10‐415.
  Greene, C.S., Penrod, N.M., Williams, S.M., and Moore, J.H. 2009. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One 4:e5639. doi: 10.1371/journal.pone.0005639.
  Halperin, E. and Stephan, D.A. 2009. SNP imputation in association studies. Nat. Biotechnol. 27:349‐351. doi: 10.1038/nbt0409‐349.
  Heslot, N., Jannink, J.‐L., and Sorrells, M.E. 2015. Perspectives for genomic selection applications and research in plants. Crop Sci. 55:1‐12. doi: 10.2135/cropsci2014.03.0249.
  Hoffman, G.E., Mezey, J.G., and Schadt, E.E. 2014. Lrgpr: Interactive linear mixed model analysis of genome‐wide association studies with composite hypothesis testing and regression diagnostics in R. Bioinformatics 30:3134‐3135. doi: 10.1093/bioinformatics/btu435.
  Houston, K., Burton, R.A., Sznajder, B., Rafalski, A.J., Dhugga, K.S., Mather, D.E., Taylor, J., Steffenson, B.J., Waugh, R., and Fincher, G.B. 2015. A genome‐wide association study for culm cellulose content in barley reveals candidate genes co‐expressed with members of the cellulose synthase a gene family. PLoS One 10:e0130890. doi: 10.1371/journal.pone.0130890.
  Howie, B.N., Donnelly, P., and Marchini, J. 2009. A flexible and accurate genotype imputation method for the next generation of genome‐wide association studies. PLoS Genet. 5:e1000529. doi: 10.1371/journal.pgen.1000529.
  Huang, X. and Han, B. 2014. Natural variations and genome‐wide association studies in crop plants. Annu. Rev. Plant Biol. 65:531‐551. doi: 10.1146/annurev‐arplant‐050213‐035715.
  Jannink, J.‐L., Lorenz, A.J., and Iwata, H. 2010. Genomic selection in plant breeding: From theory to practice. Brief. Funct. Genom. Proteom. 9:166‐177. doi: 10.1093/bfgp/elq001.
  Jia, P. and Zhao, Z. 2014. Network‐assisted analysis to prioritize GWAS results: Principles, methods and perspectives. Hum. Genet. 133:125‐138. doi: 10.1007/s00439‐013‐1377‐1.
  Kooke, R., Kruijer, W., Bours, R., Becker, F., Kuhn, A., van de Geest, H., Buntjer, J., Doeswijk, T., Guerra, J., Bouwmeester, H., Vreugdenhil, D., and Keurentjes, J.J. 2016. Genome‐wide association mapping and genomic prediction elucidate the genetic architecture of morphological traits in Arabidopsis thaliana. Plant Physiol. 170:pp.00997.2015. doi: 10.1104/pp.15.00997.
  Korte, A. and Farlow, A. 2013. The advantages and limitations of trait analysis with GWAS: A review. Plant Methods 9:29. doi: 10.1186/1746‐4811‐9‐29.
  Kover, P.X., Valdar, W., Trakalo, J., Scarcelli, N., Ehrenreich, I.M., Purugganan, M.D., Durrant, C., and Mott, R. 2009. A multiparent advanced generation inter‐cross to fine‐map quantitative traits in Arabidopsis thaliana. PLoS Genet. 5:e1000551. doi: 10.1371/journal.pgen.1000551.
  Kump, K.L., Bradbury, P.J., Wisser, R.J., Buckler, E.S., Belcher, A.R., Oropeza‐Rosas, M.A, Zwonitzer, J.C., Kresovich, S., McMullen, M.D., Ware, D., Balint‐Kurti, P.J., and Holland, J.B. 2011. Genome‐wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat. Genet. 43:163‐168. doi: 10.1038/ng.747.
  Li, J., Das, K., Fu, G., Li, R., and Wu, R. 2011. The Bayesian lasso for genome‐wide association studies. Bioinformatics 27:516‐523. doi: 10.1093/bioinformatics/btq688.
  Lipka, A.E., Tian, F., Wang, Q., Peiffer, J., Li, M., Bradbury, P.J., Gore, M.A., Buckler, E.S., and Zhang, Z. 2012. GAPIT: Genome association and prediction integrated tool. Bioinformatics 28:2397‐2399. doi: 10.1093/bioinformatics/bts444.
  Lipka, A.E., Gore, M.A., Magallanes‐Lundback, M., Mesberg, A., Lin, H., Tiede, T., Chen, C., Buell, C.R., Buckler, E.S., Rocheford, T., and DellaPenna, D. 2013. Genome‐wide association study and pathway level analysis of tocochromanol levels in maize grain. G3: Genes Genomes Genetics 3:1287‐1299. doi: 10.1534/g3.113.006148.
  Liu, Y.J., Papasian, C.J., Liu, J.F., Hamilton, J., and Deng, H.W. 2008. Is replication the gold standard for validating genome‐wide association findings? PLoS One 3:e4037. doi: 10.1371/journal.pone.0004037.
  Lowder, L.G., Zhang, D., Baltes, N.J., Paul, J.W., Tang, X., Zheng, X., Voytas, D.F., Hsieh, T.‐F., Zhang, Y., and Qi, Y. 2015. A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation. Plant Physiol. 169:971‐985. doi: 10.1104/pp.15.00636.
  Lowry, D.B., Hoban, S., Kelley, J.L., Lotterhos, K.E., Reed, L.K., Antolin, M.F., and Storfer, A. 2016. Breaking RAD: An evaluation of the utility of restriction site associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. [ePub ahead of print] doi: 10.1111/1755‐0998.12596.
  Mackay, T.F.C., Stone, E.A, and Ayroles, J.F. 2009. The genetics of quantitative traits: Challenges and prospects. Nat. Rev. Genet. 10:565‐577. doi: 10.1038/nrg2612.
  Marchini, J. and Howie, B. 2010. Genotype imputation for genome‐wide association studies. Nat. Rev. Genet. 11:499‐511. doi: 10.1038/nrg2796.
  Matsuda, F., Nakabayashi, R., Yang, Z., Okazaki, Y., Yonemaru, J.I., Ebana, K., Yano, M., and Saito, K. 2015. Metabolome‐genome‐wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism. Plant J. 81:13‐23. doi: 10.1111/tpj.12681.
  Meirmans, P.G. 2012. The trouble with isolation by distance. Mol. Ecol. 21:2839‐2846. doi: 10.1111/j.1365‐294X.2012.05578.x.
  Moser, G., Lee, S.H., Hayes, B.J., Goddard, M.E., Wray, N.R., and Visscher, P.M. 2015. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 11:e1004969. doi: 10.1371/journal.pgen.1004969.
  NCI‐NHGRI Working Group, Chanock, S.J., Manolio, T., Boehnke, M., Boerwinkle, E., Hunter, D.J., Thomas, G., Hirschhorn, J.N., Abecasis, G., Altshuler, D., Bailey‐Wilson, J.E., Brooks, L.D., Cardon, L.R., Daly, M., Donnelly, P., Fraumeni, J.F. Jr, Freimer, N.B., Gerhard, D.S., Gunter, C., Guttmacher, A.E., Guyer, M.S., Harris, E.L., Hoh, J., Hoover, R., Kong, C.A., Merikangas, K.R., Morton, C.C., Palmer, L.J., Phimister, E.G., Rice, J.P., Roberts, J., Rotimi, C., Tucker, M.A., Vogan, K.J., Wacholder, S., Wijsman, E.M., Winn, D.M., and Collins, F.S. 2007. Replicating genotype‐phenotype associations. Nature 447:655‐660. doi: 10.1038/447655a.
  Nemri, A., Atwell, S., Tarone, A.M., Huang, Y.S., Zhao, K., Studholme, D.J., Nordborg, M., and Jones, J.D.G. 2010. Genome‐wide survey of Arabidopsis natural variation in downy mildew resistance using combined association and linkage mapping. Proc. Natl. Acad. Sci. U.S.A. 107:10302‐10307. doi: 10.1073/pnas.0913160107.
  Ogura, T. and Busch, W. 2015. From phenotypes to causal sequences: Using genome wide association studies to dissect the sequence basis for variation of plant development. Curr. Opin. Plant Biol. 23:98‐108. doi: 10.1016/j.pbi.2014.11.008.
  Pease, J.B., Haak, D.C., Hahn, M.W., and Moyle, L.C. 2016. Phylogenomics reveals three sources of adaptive variation during a rapid radiation. PLoS Biol. 14:e1002379. doi: 10.1371/journal.pbio.1002379.
  Peiffer, J.A., Romay, M.C., Gore, M.A., Flint‐Garcia, S.A., Zhang, Z., Millard, M.J., Gardner, C.A., McMullen, M.D., Holland, J.B., Bradbury, P.J., and Buckler, E.S. 2014. The genetic architecture of maize height. Genetics 196:1337‐1356. doi: 10.1534/genetics.113.159152.
  Petryszak, R., Keays, M., Tang, Y.A., Fonseca, N.A., Barrera, E., Burdett, T., Füllgrabe, A., Fuentes, A.M., Jupp, S., Koskinen, S., Mannion, O., Huerta, L., Megy, K., Snow, C., Williams, E., Barzine, M., Hastings, E., Weisser, H., Wright, J., Jaiswal, P., Huber, W., Choudhary, J., Parkinson, H.E., and Brazma, A. 2016. Expression Atlas update—An integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44:D746‐D752. doi: 10.1093/nar/gkv1045.
  Phillips, P.C. 2008. Epistasis ‐ the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9:855‐867. doi: 10.1038/nrg2452.
  Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. 2006. Principal components analysis corrects for stratification in genome‐wide association studies. Nat. Genet. 38:904‐909. doi: 10.1038/ng1847.
  Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945‐959.
  Raj, A., Stephens, M., and Pritchard, J.K. 2014. FastSTRUCTURE: Variational inference of population structure in large SNP data sets. Genetics 197:573‐589. doi: 10.1534/genetics.114.164350.
  Remington, D.L., Thornsberry, J.M., Matsuoka, Y., Wilson, L.M., Whitt, S.R., Doebley, J., Kresovich, S., Goodman, M.M., and Buckler, E.S. 2001. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. U.S.A. 98:11479‐11484. doi: 10.1073/pnas.201394398.
  Ritchie, M.D., Holzinger, E.R., Li, R., Pendergrass, S.A., and Kim, D. 2015. Methods of integrating data to uncover genotype‐phenotype interactions. Nat. Rev. Genet. 16:85‐97. doi: 10.1038/nrg3868.
  Ron, M., Kajala, K., Pauluzzi, G., Wang, D., Reynoso, M.A., Zumstein, K., Garcha, J., Winte, S., Masson, H., Inagaki, S., Federici, F., Sinha, N., Deal, R.B., Bailey‐Serres, J., and Brady S.M. 2014. Hairy root transformation using Agrobacterium rhizogenes as a tool for exploring cell type‐specific gene expression and function using tomato as a model. Plant Physiol. 166:455‐469. doi: 10.1104/pp.114.239392.
  Sasaki, E., Zhang, P., Atwell, S., Meng, D., and Nordborg, M. 2015. “Missing” G x E variation controls flowering time in Arabidopsis thaliana. PLoS Genet. 11:e1005597. doi: 10.1371/journal.pgen.1005597.
  Schaefer, R.J., Michno, J.‐M., and Myers, C.L. 2016. Unraveling gene function in agricultural species using gene co‐expression networks. Biochim. Biophys. Acta S1874‐9399:30166‐30163. doi: 10.1016/j.bbagrm.2016.07.016.
  Scheet, P. and 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. doi: 10.1086/502802.
  Shakoor, N., Ziegler, G., Dilkes, B.P., Brenton, Z., Boyles, R., Connolly, E.L., Kresovich, S., and Baxter, I.R. 2016. Integration of experiments across diverse environments identifies the genetic determinants of variation in Sorghum bicolor seed element composition. Plant Physiol. 170:1989‐1998. doi: 10.1104/pp.15.01971.
  Slatkin, M. 2008. Linkage disequilibrium ‐ understanding the evolutionary past and mapping the medical future. Nat. Rev. Genet. 9:477‐485. doi: 10.1038/nrg2361.
  Speed, D. and Balding, D.J. 2015. Relatedness in the post‐genomic era: Is it still useful? Nat. Rev. Genet. 16:33‐44. doi: 10.1038/nrg3821.
  Stanton‐Geddes, J., Paape, T., Epstein, B., Briskine, R., Yoder, J., Mudge, J., Bharti, A.K., Farmer, A.D., Zhou, P., Denny, R., May, G.D., Erlandson, S., Yakub, M., Sugawara, M., Sadowsky, M.J., Young, N.D., and Tiffin, P. 2013. Candidate genes and genetic architecture of symbiotic and agronomic traits revealed by whole‐genome, sequence‐based association genetics in Medicago truncatula. PloS One 8:e65688. doi: 10.1371/journal.pone.0065688.
  Stephan, J., Stegle, O., and Beyer, A. 2015. A random forest approach to capture genetic effects in the presence of population structure. Nat. Commun. 6:7432. doi: 10.1038/ncomms8432.
  Stephens, M. and Scheet, P. 2005. Accounting for decay of linkage disequilibrium in haplotype inference and missing‐data imputation. Am. J. Hum. Genet. 76:449‐462. doi: 10.1086/428594.
  Strauch, R.C., Svedin, E., Dilkes, B., Chapple, C., and Li, X. 2015. Discovery of a novel amino acid racemase through exploration of natural variation in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 112:11726‐11731. doi: 10.1073/pnas.1503272112.
  Suren, H., Hodgins, K.A., Yeaman, S., Nurkowski, K.A., Smets, P., Rieseberg, L.H., Aitken, S.N., and Holliday, J.A. 2016. Exome capture from the spruce and pine giga‐genomes. Mol. Ecol. Resour. 16:1136‐1146. doi: 10.1111/1755‐0998.12570.
  Symonds, V.V., Godoy, A.V., Alconada, T., Botto, J.F., Juenger, T.E., Casal, J.J., and Lloyd, A.M. 2005. Mapping quantitative trait loci in multiple populations of Arabidopsis thaliana identifies natural allelic variation for trichome density. Genetics 169:1649‐1658. doi: 10.1534/genetics.104.031948.
  Szymczak, S., Biernacka, J.M., Cordell, H.J., González‐Recio, O., König, I.R., Zhang, H., and Sun, Y.V. 2009. Machine learning in genome‐wide association studies. Genet. Epidemiol. 33:51‐57. doi: 10.1002/gepi.20473.
  Vaistij, F.E., Gan, Y., Penfield, S., Gilday, A.D., Dave, A., He, Z., Josse, E.M., Choi, G., Halliday, K.J., and Graham, I.A. 2013. Differential control of seed primary dormancy in Arabidopsis ecotypes by the transcription factor SPATULA. Proc. Natl. Acad. Sci. U.S.A. 110:10866‐10871. doi: 10.1073/pnas.1301647110.
  van Leeuwen, E.M., Kanterakis, A., Deelen, P., Kattenberg, M.V., Genome of the Netherlands Consortium, Slagboom, P.E., de Bakker, P.I., Wijmenga, C., Swertz, M.A., Boomsma, D.I., van Duijn, C.M., Karssen, L.C., and Hottenga, J.J. 2015. Population‐specific genotype imputations using minimac or IMPUTE2. Nat. Protoc. 10:1285‐1296. doi: 10.1038/nprot.2015.077.
  Vilhjálmsson, B.J. and Nordborg, M. 2013. The nature of confounding in genome‐wide association studies. Nat. Rev. Genet. 14:1‐2. doi: 10.1038/nrg3382.
  Wolfe, M.D., Rabbi, I.Y., Egesi, C., Hamblin, M., Kawuki, R., Kulakow, P., Lozano, R., del Carpio, D.P., Ramu, P., and Jannink, J.‐L. 2016. Genome‐wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement. Plant Genome 9:1‐248. doi: 10.3835/plantgenome2015.11.0118.
  Wolt, J.D., Wang, K., Sashital, D., and Lawrence‐Dill, C.J. 2016. Achieving plant CRISPR targeting that limits off‐target effects. Plant Genome 9:1‐8. doi: 10.3835/plantgenome2016.05.0047.
  Yang, J., Benyamin, B., McEvoy, B.P., Gordon, S., Henders, A.K., Nyholt, D.R., Madden, P.A., Heath, A.C., Martin, N.G., Montgomery, G.W., Goddard, M.E., and Visscher, P.M. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:565‐569. doi: 10.1038/ng.608.
  Yang, J., Weedon, M.N., Purcell, S., Lettre, G., Estrada, K., Willer, C.J., Smith, A.V, Ingelsson, E., O'Connell, J.R., Mangino, M., Mägi, R., Madden, P.A., Heath, A.C., Nyholt, D.R., Martin, N.G., Montgomery, G.W., Frayling, T.M., Hirschhorn, J.N., McCarthy, M.I., Goddard, M.E., and Visscher, P.M., and GIANT Consortium. 2011. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19:807‐812. doi: 10.1038/ejhg.2011.39.
  Yeaman, S. and Whitlock, M.C. 2011. The genetic architecture of adaptation under migration‐selection balance. Evolution 65:1897‐1911. doi: 10.1111/j.1558‐5646.2011.01269.x.
  Yoder, J.B., Stanton‐Geddes, J., Zhou, P., Briskine, R., Young, N.D., and Tiffin, P. 2014. Genomic signature of adaptation to climate in Medicago truncatula. Genetics 196:1263‐1275. doi: 10.1534/genetics.113.159319.
  Yu, J., Pressoir, G., Briggs, W.H., Vroh Bi, I., Yamasaki, M., Doebley, J.F., McMullen, M.D., Gaut, B.S., Nielsen, D.M., Holland, J.B., Kresovich, S., and Buckler, E.S. 2006. A unified mixed‐model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38:203‐208. doi: 10.1038/ng1702.
  Zhang, F., Wang, Y., and Deng, H.W. 2008. Comparison of population‐based association study methods correcting for population stratification. PLoS One 3:e3392. doi: 10.1371/journal.pone.0003392.
  Zhao, K., Tung, C.W., Eizenga, G.C., Wright, M.H., Ali, M.L., Price, A.H., Norton, G.J., Islam, M.R., Reynolds, A., Mezey, J., McClung, A.M., Bustamante, C.D., and McCouch, S.R. 2011. Genome‐wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat. Commun. 2:467. doi: 10.1038/ncomms1467.
  Zhou, L. and Holliday, J.A. 2012. Targeted enrichment of the black cottonwood (Populus trichocarpa) gene space using sequence capture. BMC Genomics 13:703. doi: 10.1186/1471‐2164‐13‐703.
  Zhu, C., Gore, M., Buckler, E.S., and Yu, J. 2008. Status and prospects of association mapping in plants. Plant Genome J. 1:5‐20. doi: 10.3835/plantgenome2008.02.0089.
  Zöllner, S. and Pritchard, J.K. 2007. Overcoming the winner's curse: Estimating penetrance parameters from case‐control data. Am. J. Hum. Genet. 80:605‐615. doi: 10.1086/512821.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library