The Impact of Phenotypic and Genetic Heterogeneity on Results of Genome Wide Association Studies of Complex Diseases

TLDR

Phenotypic misclassification reduces GWAS power, and heterogeneity in genetic susceptibility and disease pathophysiology may have an even larger impact, yet this effect has received little attention. The study investigates how phenotypic and genetic heterogeneity affect GWAS statistical power. The authors used simulated genotypic and phenotypic data and analyzed WTCCC diabetes data with varying proportions of type 1 and type 2 cases to assess heterogeneity’s impact on association strength and significance. Heterogeneity markedly reduces GWAS power and risk estimates, with 50 % heterogeneity tripling required sample sizes, indicating that precise phenotype definition may be more critical than simply increasing sample size.

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.