Overview of Statistical Methods for Genome-Wide Association Studies (GWAS).

This chapter provides an overview of statistical methods for genome-wide association studies (GWAS) in animals, plants, and humans. The simplest form of GWAS, a marker-by-marker analysis, is illustrated with a simple example. The problem of selecting a significance threshold that accounts for the large amount of multiple testing that occurs in GWAS is discussed. Population structure causes false positive associations in GWAS if not accounted for, and methods to deal with this are presented. Methodology for more complex models for GWAS, including haplotype-based approaches, accounting for identical by descent versus identical by state, and fitting all markers simultaneously are described and illustrated with examples.

[1]  R. Fernando,et al.  Optimal Haplotype Structure for Linkage Disequilibrium-Based Fine Mapping of Quantitative Trait Loci Using Identity by Descent , 2006, Genetics.

[2]  J. Pritchard,et al.  Linkage disequilibrium in humans: models and data. , 2001, American journal of human genetics.

[3]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[4]  R. Fernando,et al.  Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers. , 2012, Journal of animal science.

[5]  M. Goddard,et al.  Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle. , 2007, Genetical research.

[6]  J. Weller,et al.  A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. , 1998, Genetics.

[7]  M. Goddard,et al.  Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data , 2004, Genetics Selection Evolution.

[8]  M. Calus,et al.  Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips , 2010, BMC proceedings.

[9]  R. Doerge,et al.  Empirical threshold values for quantitative trait mapping. , 1994, Genetics.

[10]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[11]  Robert D Schnabel,et al.  Genome-Wide Survey of SNP Variation Uncovers the Genetic Structure of Cattle Breeds , 2009, Science.

[12]  R. Fernando,et al.  Controlling the Proportion of False Positives in Multiple Dependent Tests , 2004, Genetics.

[13]  R. Fernando,et al.  Comparing Linkage Disequilibrium-Based Methods for Fine Mapping Quantitative Trait Loci , 2004, Genetics.

[14]  Rohan L. Fernando,et al.  Extension of the bayesian alphabet for genomic selection , 2011, BMC Bioinformatics.

[15]  M. Goddard,et al.  A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. , 2010, Journal of dairy science.

[16]  J H J van der Werf,et al.  Components of the accuracy of genomic prediction in a multi-breed sheep population. , 2012, Journal of animal science.

[17]  B. Han,et al.  Identification of 15 loci influencing height in a Korean population , 2010, Journal of Human Genetics.

[18]  C. Gieger,et al.  Identification of ten loci associated with height highlights new biological pathways in human growth , 2008, Nature Genetics.

[19]  David M. Evans,et al.  Genome-wide association analysis identifies 20 loci that influence adult height , 2008, Nature Genetics.

[20]  D. Reich,et al.  Population Structure and Eigenanalysis , 2006, PLoS genetics.

[21]  Michael E Goddard,et al.  Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. , 2009, Genetics research.

[22]  M. Calus,et al.  Accuracy of Genomic Selection Using Different Methods to Define Haplotypes , 2008, Genetics.

[23]  John D. Storey.  A direct approach to false discovery rates , 2002 .

[24]  Bjarni V. Halldórsson,et al.  Many sequence variants affecting diversity of adult human height , 2008, Nature Genetics.

[25]  M Erbe,et al.  Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. , 2012, Journal of dairy science.

[26]  F. Dudbridge,et al.  Estimation of significance thresholds for genomewide association scans , 2008, Genetic epidemiology.

[27]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[28]  M. Goddard,et al.  Technical note: prediction of breeding values using marker-derived relationship matrices. , 2008, Journal of animal science.

[29]  W. G. Hill,et al.  Linkage disequilibrium in finite populations , 1968, Theoretical and Applied Genetics.

[30]  G. McVean.  A Genealogical Interpretation of Principal Components Analysis , 2009, PLoS genetics.

[31]  Peter M Visscher,et al.  Recent human effective population size estimated from linkage disequilibrium. , 2007, Genome research.

[32]  Sharon R. Browning,et al.  Detecting Rare Variant Associations by Identity-by-Descent Mapping in Case-Control Studies , 2012, Genetics.

[33]  Richa Saxena,et al.  A common variant of HMGA2 is associated with adult and childhood height in the general population , 2007, Nature Genetics.

[34]  M. Goddard,et al.  Polymorphic Regions Affecting Human Height Also Control Stature in Cattle , 2011, Genetics.

[35]  Z. Luo,et al.  Detecting linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations , 1998, Heredity.

[36]  M. Goddard,et al.  Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms. , 2010, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[37]  R. Fernando,et al.  Power and Precision of Alternate Methods for Linkage Disequilibrium Mapping of Quantitative Trait Loci , 2007, Genetics.

[38]  M. Goddard,et al.  Prediction of identity by descent probabilities from marker-haplotypes , 2001, Genetics Selection Evolution.

[39]  R. Fernando,et al.  Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods , 2011, BMC proceedings.

[40]  Robin Thompson,et al.  ASREML user guide release 1.0 , 2002 .

[41]  W. Ewens,et al.  Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). , 1993, American journal of human genetics.

[42]  P. Donnelly,et al.  Association mapping in structured populations. , 2000, American journal of human genetics.

[43]  R. Fernando,et al.  Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods , 2012, BMC Proceedings.

[44]  James M Reecy,et al.  Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models , 2011, BMC proceedings.