Comparing linkage and association analyses in sheep points to a better way of doing GWAS

Abstract

SummaryGenome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects.

Impacts of Population Structure and Analytical Models in Genome-Wide Association Studies of Complex Traits in Forest Trees: A Case Study in Eucalyptus globulus

“…Additionally genome-wide associations and QTLs are typically estimated ignoring the rest of the genome which also contributes to an upward bias. This problem can be mitigated by fitting all markers simultaneously as random effects, an approach that has been proposed and adopted for Genomic Selection [78] or improved ways of carrying out GWAS [79]. This approach, successfully applied in several animal and plant species, has been shown to result in a much larger number of effects detected (hundreds), with considerably smaller magnitudes (<1%), converging to a quasi-infinitesimal model while explaining very large proportions of the heritability for complex traits [80].…”

Pitfalls of predicting complex traits from SNPs

“…Even in dairy cattle however, traits that could reasonably be assumed to be under strong natural selection, such as fertility, have greater missing heritability 31 . Moreover, when the SNPs are fitted together with a pedigree as much as half of the genetic variance is explained by the pedigree and not the SNPs 33 . The simplest explanation is that in livestock as in humans some causal variants are rare and in poor LD with the SNPs.…”

A meta analysis of genome-wide association studies for limb bone lengths in four pig populations

“…To date, a total of 39 quantitative trait loci (QTL) for limb bone length have been identified across the pig genome [ 8 – 10 ]. With the availability of Illumina PorcineSNP60 Beadchip [ 11 ], genome-wide association studies have been conducted on a variety of traits to improve the resolution of traditional QTL mapping [ 12 ]. GWAS meta analysis based on multiple populations can further increase the detection power and reduce false-positive findings by utilizing information from multiple independent studies [ 13 ].…”