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

<div><p>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.</p></div

Genome-wide analysis of the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes type 1 (T1D) and type 2 (T2D) under heterogeneity: twenty most significant associations [−log(p-values)].

<p>SNP = single nucleotide polymorphism; β = admixture.</p

The impact of heterogeneity on the sample size (cases and controls) required for 90% of statistical power.

<p>The minimum sample size to achieve to detect association was calculated in simulated case-control data with increasing proportion of “non-cases” considering a disease prevalence of 0.01. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk.</p

The impact of heterogeneity on the estimation of the genetic effect size.

<p>Odds ratios from simulated case-control data were calculated for each step of admixture. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk; OR = odds ratio.</p