Abstract
Data that are collected for whole-genome prediction can also be used for genome-wide association studies (GWAS). This paper discusses how Bayesian multiple-regression methods that are used for whole-genome prediction can be adapted for GWAS. It is argued here that controlling the posterior type I error rate (PER) is more suitable than controlling the genomewise error rate (GER) for controlling false positives in GWAS. It is shown here that under ideal conditions, i.e., when the model is correctly specified, PER can be controlled by using Bayesian posterior probabilities that are easy to obtain. Computer simulation was used to examine the properties of this Bayesian approach when the ideal conditions were not met. Results indicate that even then useful inferences can be made.
Highlights
High-density SNP genotypes are currently being used in livestock for whole-genome prediction (VanRaden et al 2009; Hayes et al 2009; Habier et al 2010; Wolc et al 2011)
The genotype and phenotype data obtained for whole-genome prediction can be used for genome-wide association studies (GWAS) to locate causal variants (QTL) for traits of economic importance
The objectives of this paper are to: (1) address the problem of signal dependence and review the advantages of managing false positives in GWAS by controlling posterior type I error rate (PER) or related measures such as false discovery rate (FDR) or proportion of false positives (PFP) rather than by controlling the genomewise error rate (GER), and (2) use computer simulation to examine the relationship between Bayesian posterior probabilities of association and the frequency of a true association
Summary
High-density SNP genotypes are currently being used in livestock for whole-genome prediction (VanRaden et al 2009; Hayes et al 2009; Habier et al 2010; Wolc et al 2011) This requires obtaining genotypes and phenotypes on several thousand animals in a training population to estimate effects of the SNP genotypes on the traits of interest. Many GWAS for quantitative traits are based on testing one SNP at a time using simple regression models or using mixed models with a fixed substitution effect of the SNP genotype along with a polygenic effect correlated according to a pedigree-based or a genomic relationship matrix to capture the effects of all the other genes. Such GWAS have been successful in detecting many associations, but the established associations typically explain only a small fraction of the genetic variability of quantitative traits (Maher 2008; Manolio et al 2009; Visscher et al 2010)
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