Abstract
Genome 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.
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