Abstract
Large-scale GWAS results are typically obtained by meta-analyzing GWAS results from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of individual genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing’ heritability. However, a theoretical multi-study framework, relating statistical power and predictive accuracy to cross-study heterogeneity, is not available. We address this gap by developing an online Meta-GWAS Accuracy and Power calculator that accounts for the cross-study genetic correlation. This calculator enables to explore to what extent an imperfect cross-study genetic correlation (i.e., less than one) contributes to the missing heritability. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy inferred by this calculator are accurate. We use the calculator to assess recent GWAS efforts and show that the effect of cross-study genetic correlation on statistical power and predictive accuracy is substantial. Hence, cross-study genetic correlation explains a considerable part of the missing heritability. Therefore, a priori calculations of statistical power and predictive accuracy, accounting for heterogeneity in genetic effects across studies, are an important tool for adequately inferring whether an intended meta-analysis of GWAS results is likely to yield meaningful outcomes.
Author Summary Large-scale genome-wide association studies are uncovering the genetic architecture of traits which are affected by many genetic variants. Such studies typically meta-analyze association results from multiple studies spanning different regions and/or time periods. These efforts do not yet capture a large share of the total proportion of trait variation attributable to genetic variation. The origins of this so-called ‘missing’ heritability have been strongly debated. One factor exacerbating the missing heritability is heterogeneity in the effects of genetic variants across studies. Its influence on statistical power to detect associated genetic variants and the accuracy of polygenic predictions, is poorly understood. In the current study, we derive the precise effects of heterogeneity in genetic effects across studies on both the statistical power to detect associated genetic variants as well as the accuracy of polygenic predictions. We provide an online calculator, available at www.devlaming.eu, which accounts for these effects. By means of this calculator, we show that imperfect genetic correlations between studies substantially decrease statistical power and predictive accuracy and, thereby, contribute to the missing heritability. We argue that researchers should account for genetic heterogeneity across studies, when assessing whether a proposed large-scale genome-wide association study is likely to yield meaningful results.
