Computational Cell Biology pp 93-136 | Cite as
Methods and Tools in Genome-wide Association Studies
- 6 Citations
- 5 Mentions
- 1.8k Downloads
Abstract
Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual’s risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies—or associations—between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.
Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.
This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.
Key words
Genome-wide association studies Missing heritability Linkage disequilibrium Phenotypes Univariate mapping Population structure correction Genomic inflation Multilocus mapping Multiple hypothesis correction Meta-analysis GWAS toolsReferences
- 1.MacDonald ME, Novelletto A, Lin C et al (1992) The Huntington’s disease candidate region exhibits many different haplotypes. Nat Genet 1:99–103PubMedCrossRefGoogle Scholar
- 2.Kerem B-S (1989) Identification of the cystic fibrosis gene: genetic analysis. Trends Genet 5:363CrossRefGoogle Scholar
- 3.Bush WS, Moore JH (2012) Chapter 11: Genome-wide association studies. PLoS Comput Biol 8:e1002822PubMedPubMedCentralCrossRefGoogle Scholar
- 4.Visscher PM, Brown MA, McCarthy MI et al (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24PubMedPubMedCentralCrossRefGoogle Scholar
- 5.Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322:881–888PubMedPubMedCentralCrossRefGoogle Scholar
- 6.The 1000 Genomes Project Consortium (2015) A global reference for human genetic variation. Nature 526:68–74PubMedCentralCrossRefGoogle Scholar
- 7.Gibbs RA, Belmont JW, Hardenbol P et al (2003) The international HapMap project. Nature 426:789–796CrossRefGoogle Scholar
- 8.Davey JW, Hohenlohe PA, Etter PD et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510PubMedCrossRefPubMedCentralGoogle Scholar
- 9.Fan J-B, Chee MS, Gunderson KL (2006) Highly parallel genomic assays. Nat Rev Genet 7:632–644PubMedCrossRefPubMedCentralGoogle Scholar
- 10.Dudoit S, van der Laan MJ (2008) Multiple hypothesis testing. In: Multiple testing procedures with applications to genomics. Springer, New York, NY, pp 1–47CrossRefGoogle Scholar
- 11.Fairweather D, Frisancho-Kiss S, Rose NR (2008) Sex differences in autoimmune disease from a pathological perspective. Am J Pathol 173:600–609PubMedPubMedCentralCrossRefGoogle Scholar
- 12.Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CrossRefGoogle Scholar
- 13.Atwell S, Huang YS, Vilhjálmsson BJ et al (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631PubMedPubMedCentralCrossRefGoogle Scholar
- 15.Alonso-Blanco C, Andrade J, Becker C et al (2016) 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166:481–491Google Scholar
- 16.Zhao K, Tung C-W, 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
- 17.Mackay TFC, Richards S, Stone EA et al (2012) The Drosophila melanogaster genetic reference panel. Nature 482:173–178Google Scholar
- 18.Kirby A, Kang HM, Wade CM et al (2010) Fine mapping in 94 inbred mouse strains using a high-density haplotype resource. Genetics 185:1081–1095PubMedPubMedCentralCrossRefGoogle Scholar
- 21.Freilinger T, Anttila V, de Vries B et al (2012) Genome-wide assoiation analysis identifies susceptibility loci for migraine without aura. Nat Genet 44:777–782Google Scholar
- 23.Manolio TA, Collins FS, Cox NJ et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedPubMedCentralCrossRefGoogle Scholar
- 24.Lee SH, Wray NR, Goddard ME et al (2011) Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88:294–305PubMedPubMedCentralCrossRefGoogle Scholar
- 25.Pedroso I, Breen G (2011) Gene set analysis and network analysis for genome-wide association studies. Cold Spring Harb Protoc 2011:pdb.top065581PubMedCrossRefPubMedCentralGoogle Scholar
- 26.Cordell HJ (2002) Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468PubMedCrossRefPubMedCentralGoogle Scholar
- 27.Kam-Thong T, Czamara D, Tsuda K et al (2011) EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur J Hum Genet 19:465–471PubMedCrossRefPubMedCentralGoogle Scholar
- 28.Kam-Thong T, Azencott C-A, Cayton L et al (2012) GLIDE: GPU-based linear regression for detection of epistasis. Hum Hered 73:220–236PubMedCrossRefPubMedCentralGoogle Scholar
- 29.Liu JZ, Mcrae AF, Nyholt DR et al (2010) A versatile gene-based test for genome-wide association studies. Am J Hum Genet 87:139–145PubMedPubMedCentralCrossRefGoogle Scholar
- 32.Lamparter D, Marbach D, Rueedi R et al (2016) Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput Biol 12:e1004714PubMedPubMedCentralCrossRefGoogle Scholar
- 33.Jia P, Zheng S, Long J et al (2011) dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics (Oxford, England) 27:95–102CrossRefGoogle Scholar
- 34.Rossin EJ, Lage K, Raychaudhuri S et al (2011) Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet 7:e1001273PubMedPubMedCentralCrossRefGoogle Scholar
- 35.Azencott C-A, Grimm D, Sugiyama M et al (2013) Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics 29:i171–i179PubMedPubMedCentralCrossRefGoogle Scholar
- 36.Wang Q, Yu H, Zhao Z et al (2015) EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics (Oxford, England). 31:2591–2594PubMedCentralCrossRefPubMedGoogle Scholar
- 37.Llinares-López F, Grimm DG, Bodenham DA et al (2015) Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinformatics 31:i240–i249PubMedPubMedCentralCrossRefGoogle Scholar
- 38.Buzdugan L, Kalisch M, Navarro A et al (2016) Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics 32:1990–2000PubMedPubMedCentralCrossRefGoogle Scholar
- 39.Matsuzaki H, Dong S, Loi H et al (2004) Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat Methods 1:109–111PubMedCrossRefGoogle Scholar
- 40.Clarke GM, Anderson CA, Pettersson FH et al (2011) Basic statistical analysis in genetic case-control studies. Nat Protoc 6:121–133PubMedPubMedCentralCrossRefGoogle Scholar
- 41.Plomin R, Haworth CMA, Davis OSP (2009) Common disorders are quantitative traits. Nat Rev Genet 10:872–878PubMedCrossRefGoogle Scholar
- 42.Power RA, Parkhill J, de Oliveira T (2017) Microbial genome-wide association studies: lessons from human GWAS. Nat Rev Genet 18:41–50PubMedCrossRefGoogle Scholar
- 43.Wu MC, Lee S, Cai T et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93PubMedPubMedCentralCrossRefGoogle Scholar
- 46.Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188–193PubMedCrossRefGoogle Scholar
- 47.Neale BM, Rivas MA, Voight BF et al (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7:e1001322PubMedPubMedCentralCrossRefGoogle Scholar
- 48.Anderson CA, Pettersson FH, Clarke GM et al (2010) Data quality control in genetic case-control association studies. Nat Protoc 5:1564–1573PubMedPubMedCentralCrossRefGoogle Scholar
- 49.Marchini J, Howie B (2010) Genotype imputation for genome-wide association studies. Nat Rev Genet 11:499–511PubMedCrossRefGoogle Scholar
- 50.Fisher RA (1925) Statistical methods for research workers. Genesis Publishing Pvt Ltd., EdinburghGoogle Scholar
- 51.Pearson K (1900) X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos Mag Ser 5(50):157–175CrossRefGoogle Scholar
- 52.Fahrmeir L, Kneib T, Lang S et al (2013) Regression: models, methods and applications. Springer Science & Business Media, New York, NYCrossRefGoogle Scholar
- 53.Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 46:100–106PubMedPubMedCentralCrossRefGoogle Scholar
- 54.Loh P-R, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284–290PubMedPubMedCentralCrossRefGoogle Scholar
- 55.Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004PubMedCrossRefGoogle Scholar
- 56.Yang J, Weedon MN, Purcell S et al (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet 19:807–812PubMedPubMedCentralCrossRefGoogle Scholar
- 57.Devlin B, Roeder K, Wasserman L (2001) Genomic control, a new approach to genetic-based association studies. Theor Popul Biol 60:155–166PubMedCrossRefGoogle Scholar
- 58.Lippert C, Listgarten J, Liu Y et al (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835PubMedCrossRefGoogle Scholar
- 59.Widmer C, Lippert C, Weissbrod O et al (2014) Further improvements to linear mixed models for genome-wide association studies. Sci Rep 4:6874PubMedPubMedCentralCrossRefGoogle Scholar
- 60.Kang HM, Zaitlen NA, Wade CM et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedPubMedCentralCrossRefGoogle Scholar
- 62.Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824PubMedPubMedCentralCrossRefGoogle Scholar
- 63.Veyrieras J-B, Kudaravalli S, Kim SY et al (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet 4:e1000214PubMedPubMedCentralCrossRefGoogle Scholar
- 64.Mooney MA, Nigg JT, McWeeney SK et al (2014) Functional and genomic context in pathway analysis of GWAS data. Trends Genet 30:390–400PubMedPubMedCentralCrossRefGoogle Scholar
- 65.Sedeño-Cortés AE, Pavlidis P (2014) Pitfalls in the application of gene-set analysis to genetics studies. Trends Genet 30:513–514PubMedCrossRefGoogle Scholar
- 66.Ballard DH, Cho J, Zhao H (2010) Comparisons of multi-marker association methods to detect association between a candidate region and disease. Genet Epidemiol 34:201–212PubMedPubMedCentralCrossRefGoogle Scholar
- 67.Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575PubMedPubMedCentralCrossRefGoogle Scholar
- 68.Listgarten J, Lippert C, Kang EY et al (2013) A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics 29:1526–1533PubMedPubMedCentralCrossRefGoogle Scholar
- 69.Zuk O, Hechter E, Sunyaev SR et al (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci 109:1193–1198PubMedCrossRefGoogle Scholar
- 70.Ueki M, Cordell HJ (2012) Improved statistics for genome-wide interaction analysis. PLoS Genet 8:e1002625PubMedPubMedCentralCrossRefGoogle Scholar
- 72.Szklarczyk D, Franceschini A, Kuhn M et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:D561–D568PubMedCrossRefGoogle Scholar
- 73.Franceschini A, Szklarczyk D, Frankild S et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–D815PubMedCrossRefPubMedCentralGoogle Scholar
- 74.Li T, Wernersson R, Hansen RB et al (2017) A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods 14:61–64PubMedCrossRefPubMedCentralGoogle Scholar
- 75.Johnson RC, Nelson GW, Troyer JL et al (2010) Accounting for multiple comparisons in a genome-wide association study (GWAS). BMC Genomics 11:724PubMedPubMedCentralCrossRefGoogle Scholar
- 76.Bonferroni CE (1936) Teoria statistica delle classi e calcolo delle probabilita. Libreria internazionale Seeber, FirenzeGoogle Scholar
- 77.Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol 57:289–300Google Scholar
- 78.Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188CrossRefGoogle Scholar
- 79.Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci 100:9440–9445CrossRefGoogle Scholar
- 80.Thompson JR, Attia J, Minelli C (2011) The meta-analysis of genome-wide association studies. Brief Bioinform 12:259–269PubMedCrossRefPubMedCentralGoogle Scholar
- 81.Evangelou E, Ioannidis JPA (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 14:379–389PubMedCrossRefPubMedCentralGoogle Scholar
- 82.Stouffer SA, Suchman EA, DeVinney LC et al (1949) The American soldier: adjustment during army life. In: Studies in social psychology in World War II, vol 1. Princeton University Press, Princeton, NJGoogle Scholar
- 83.Borenstein M, Hedges LV, Higgins JPT et al (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Syn Methods 1:97–111CrossRefGoogle Scholar
- 84.Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108PubMedCrossRefGoogle Scholar
- 85.Yang J, Lee SH, Goddard ME et al (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82PubMedPubMedCentralCrossRefGoogle Scholar
- 86.Kang HM, Sul JH, S.K. Service et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354PubMedPubMedCentralCrossRefGoogle Scholar
- 87.Svishcheva GR, Axenovich TI, Belonogova NM et al (2012) Rapid variance components-based method for whole-genome association analysis. Nat Genet 44:1166–1170PubMedCrossRefGoogle Scholar
- 88.de Leeuw CA, Mooij JM, Heskes T et al (2015) MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 11:e1004219PubMedPubMedCentralCrossRefGoogle Scholar
- 89.Childs LH, Lisec J, Walther D (2012) Matapax: an online high-throughput genome-wide association study pipeline. Plant Physiol 158:1534–1541PubMedPubMedCentralCrossRefGoogle Scholar
- 90.Seren Ü, Vilhjálmsson BJ, Horton MW et al (2012) GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell 24:4793–4805PubMedPubMedCentralCrossRefGoogle Scholar
- 91.Grimm DG, Roqueiro D, Salome P et al (2017) easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies. Plant Cell 29:5PubMedCrossRefPubMedCentralGoogle Scholar
- 92.Galinsky KJ, Bhatia G, Loh P-R et al (2016) Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am J Hum Genet 98:456–472PubMedPubMedCentralCrossRefGoogle Scholar
- 93.Cormen TH, Leiserson CE, Rivest RL et al (2009) Introduction to algorithms. MIT Press, Cambridge, MAGoogle Scholar
- 94.Llinares-López, Papaxanthos L, Bodenham D, Roqueiro D (2017) COPDGene Investigators, Karsten Borgwardt; Genome-wide genetic heterogeneity discovery with categorical covariates. Bioinformatics 33(2): 1820--1828 PubMedPubMedCentralCrossRefGoogle Scholar
- 95.Papaxanthos L, Llinares-Lopez F, Bodenham D et al (2016) Finding significant combinations of features in the presence of categorical covariates. In: Lee DD, Sugiyama M, Luxburg UV et al (eds) Advances in neural information processing systems, vol 29. Curran Associates, Inc, Red Hook, NY, pp 2271–2279Google Scholar
- 96.Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83CrossRefGoogle Scholar
- 99.Seren Ü, Grimm D, Fitz J et al (2017) AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Res 45:D1054–D1059Google Scholar
- 100.McGaughran A, Rödelsperger C, Grimm DG et al (2016) Genomic profiles of diversification and genotype-phenotype association in Island nematode lineages. Mol Biol Evol 33:2257–2272PubMedCrossRefPubMedCentralGoogle Scholar
- 101.Easton DF, Eeles RA (2008) Genome-wide association studies in cancer. Hum Mol Genet 17:R109–R115PubMedCrossRefPubMedCentralGoogle Scholar
- 102.Kraft P, Hunter DJ (2009) Genetic risk prediction--are we there yet? N Engl J Med 360:1701–1703PubMedCrossRefPubMedCentralGoogle Scholar
- 103.Couzin J (2008) DNA test for breast cancer risk draws criticism. Science 322:357–357PubMedCrossRefPubMedCentralGoogle Scholar
- 104.Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427CrossRefGoogle Scholar
- 106.Fuchsberger C, Flannick J, Teslovich TM et al (2016) The genetic architecture of type 2 diabetes. Nature 536:41–47PubMedPubMedCentralCrossRefGoogle Scholar
- 107.Welter D, MacArthur J, Morales J et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–D1006PubMedCrossRefPubMedCentralGoogle Scholar
- 108.T. Burdett, P.N. Hall, E. Hastings, et al. The NHGRI-EBI catalog of published genome-wide association studies. www.ebi.ac.uk/gwas.
- 109.Gusev A, Bhatia G, Zaitlen N et al (2013) Quantifying missing heritability at known GWAS loci. PLoS Genet 9:e1003993PubMedPubMedCentralCrossRefGoogle Scholar
- 110.Bergen SE, Petryshen TL (2012) Genome-wide association studies (GWAS) of schizophrenia: does bigger lead to better results? Curr Opin Psychiatry 25:76–82PubMedPubMedCentralCrossRefGoogle Scholar
- 111.O’Donovan MC, Craddock N, Norton N et al (2008) Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 40:1053–1055PubMedCrossRefPubMedCentralGoogle Scholar
- 112.Williams HJ, Norton N, Dwyer S et al (2011) Fine mapping of ZNF804A and genome wide significant evidence for its involvement in schizophrenia and bipolar disorder. Mol Psychiatry 16:429–441PubMedCrossRefPubMedCentralGoogle Scholar
- 113.Richardson WC, Berwick DM, Bisgard J et al (2001) Crossing the quality chasm: a new health system for the 21st century. Institute of Medicine, National Academy Press, Washington, DCGoogle Scholar
- 114.Manolio TA (2013) Bringing genome-wide association findings into clinical use. Nat Rev Genet 14:549–558PubMedCrossRefGoogle Scholar
- 115.Lencz T, Malhotra AK (2015) Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic. Mol Psychiatry 20:820–826PubMedPubMedCentralCrossRefGoogle Scholar
- 116.Chan SL, Jin S, Loh M et al (2015) Progress in understanding the genomic basis for adverse drug reactions: a comprehensive review and focus on the role of ethnicity. Pharmacogenomics 16:1161–1178PubMedPubMedCentralCrossRefGoogle Scholar
- 117.Huang W, Massouras A, Inoue Y et al (2014) Natural variation in genome architecture among 205 Drosophila melanogaster genetic reference panel lines. Genome Res 24:1193–1208PubMedPubMedCentralCrossRefGoogle Scholar
- 118.Andersen EC, Gerke JP, Shapiro JA et al (2012) Chromosome-scale selective sweeps shape Caenorhabditis elegans genomic diversity. Nat Genet 44:285–290PubMedPubMedCentralCrossRefGoogle Scholar
- 119.Farber CR, Bennett BJ, Orozco L et al (2011) Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet 7:e1002038PubMedPubMedCentralCrossRefGoogle Scholar
- 120.Hayward JJ, Castelhano MG, Oliveira KC et al (2016) Complex disease and phenotype mapping in the domestic dog. Nat Commun 7:10460PubMedPubMedCentralCrossRefGoogle Scholar
- 121.Tang R, Noh HJ, Wang D et al (2014) Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder. Genome Biol 15:R25PubMedPubMedCentralCrossRefGoogle Scholar
- 122.Flint J, Eskin E (2012) Genome-wide association studies in mice. Nat Rev Genet 13:807–817PubMedPubMedCentralCrossRefGoogle Scholar
- 123.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 45:43–50PubMedCrossRefPubMedCentralGoogle Scholar
- 124.Lin T, Zhu G, Zhang J et al (2014) Genomic analyses provide insights into the history of tomato breeding. Nat Genet 46:1220–1226PubMedPubMedCentralCrossRefGoogle Scholar
- 125.Nicolas SD, Péros J-P, Lacombe T et al (2016) Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies. BMC Plant Biol 16:74PubMedPubMedCentralCrossRefGoogle Scholar
- 126.Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551PubMedCrossRefPubMedCentralGoogle Scholar
- 127.Sharma A, Lee JS, Dang CG et al (2015) Stories and challenges of genome wide association studies in livestock — a review. Asian Australas J Anim Sci 28: 1371–1379PubMedPubMedCentralCrossRefGoogle Scholar
- 130.Llinares-López F, Sugiyama M, Papaxanthos L et al (2015) Fast and memory-efficient significant pattern mining via permutation testing. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Sydney, NSW, pp 725–734CrossRefGoogle Scholar