Abstract
Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.
Highlights
Phenotypic misclassification reduces substantially the power to detect association, in case-control studies [1,2,3,4,5,6]
There was a substantial loss of statistical power that was disproportionately larger than the degree of heterogeneity
We showed that the presence of heterogeneity reduced both the statistical power as well as the observed risks attributed to susceptibility alleles or genotypes
Summary
Phenotypic misclassification reduces substantially the power to detect association, in case-control studies [1,2,3,4,5,6]. It is conceivable that complex traits may be heterogeneous with respect to genetic susceptibility and disease pathophysiology, and that the effect of phenotypic or genetic heterogeneity ( referred to as ‘‘heterogeneity’’) is of a larger magnitude than that of phenotypic misclassification This is relevant for psychiatric disorders [7]. It is conceivable that these syndromes encompass diverse conditions, each with a distinct genetic basis and little overlap with the others [8] Several such subgroups of major psychiatric disorders, including lithium responsive BD [9] and mood incongruent psychosis [10] have been proposed based on clinical, familial and biological criteria
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