Genome Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods

Abstract

ABSTRACTA popular strategy (EMMAX) for genome wide association (GWA) analysis fits all marker effects as classical random effects (i.e., Gaussian prior) by which association for the specific marker of interest is inferred by treating its effect as fixed. It seems more statistically coherent to specify all markers as sharing the same prior distribution, whether it is Gaussian, heavy-tailed (BayesA), or has variable selection specifications based on a mixture of, say, two Gaussian distributions (SSVS). Furthermore, all such GWA inference should be formally based on posterior probabilities or test statistics as we present here, rather than merely being based on point estimates. We compared these three broad categories of priors within a simulation study to investigate the effects of different degrees of skewness for quantitative trait loci (QTL) effects and numbers of QTL using 43,266 SNP marker genotypes from 922 Duroc-Pietrain F2 cross pigs. Genomic regions were based either on single SNP associations, on non-overlapping windows of various fixed sizes (0.5 to 3 Mb) or on adaptively determined windows that cluster the genome into blocks based on linkage disequilibrium (LD). We found that SSVS and BayesA lead to the best receiver operating curve properties in almost all cases. We also evaluated approximate marginal a posteriori (MAP) approaches to BayesA and SSVS as potential computationally feasible alternatives; however, MAP inferences were not promising, particularly due to their sensitivity to starting values. We determined that it is advantageous to use variable selection specifications based on adaptively constructed genomic window lengths for GWA studies.SUMMARYGenome wide association (GWA) analyses strategies have been improved by simultaneously fitting all marker effects when inferring upon any single marker effect, with the most popular distributional assumption being normality. Using data generated from 43,266 genotypes on 922 Duroc-Pietrain F2 cross pigs, we demonstrate that GWA studies could particularly benefit from more flexible heavy-tailed or variable selection distributional assumptions. Furthermore, these associations should not just be based on single markers or even genomic windows of markers of fixed physical distances (0.5 − 3.0 Mb) but based on adaptively determined genomic windows using linkage disequilibrium information.