An evaluation of statistical approaches to rare variant analysis in genetic association studies.
Bottom Line: Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits.Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants.Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits.
Affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom. email@example.comShow MeSH
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Mentions: Figure 1 shows the power of each of the tests of association as a function of the percentage of phenotypic variation explained by causal variants in the 50 kb region, assuming the trait mean is determined by the presence or absence of minor alleles at any of the causal variants. Results for two models are presented here, each assuming a total MAF of 5% for all causal variants in the region: (a) the maximum MAF of any individual causal variant is 0.5%; and (b) the maximum MAF of any individual causal variant is 2%. Model (b) incorporates fewer and, on average, more common causal variants than does (a), and thus represents a lower degree of allelic heterogeneity. Supplementary Figures 1 and 2 present power for a wider range of association models encompassing intermediate levels of allelic heterogeneity, where trait means are determined by the presence or absence of minor alleles at any causal variant, and by the proportion of causal variants at which a minor allele is present, respectively.
Affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom. firstname.lastname@example.org