Meta-analysis of gene-level tests for rare variant association

Abstract

The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable-threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach retains useful features from single-variant meta-analysis approaches and demonstrate its use in a study of blood lipid levels in ∼18,500 individuals genotyped with exome arrays.

Figure 1

Power comparison for our approach, Fisher's method and the minimal p-value approach. Three phenotype models were simulated: (1) half of low frequency variants with MAF < 0.5% are causal, each increasing expected trait values by 1/4 standard deviation; (2) half of all variants are causal, irrespective of frequency, and increase trait values by 1/4 standard deviation; (3) 50% of the variants are casual, irrespective of frequency, and 80% of these increase expected trait values by 1/4 standard deviation, while the remaining 20% decrease trait values by the same amount. A number of 2-100 samples of size 1000 were simulated for each model, with each sample drawn from a randomly chosen population. Meta-analysis was performed using our approach or using Fisher's method and the minimal p-value approach to combine burden test, SKAT and variable threshold (VT) test statistics for variants with MAF<5%. The power was evaluated at the significance threshold of α=2.5×10^-6 using 10,000 replicates. Panel A displays the power for three meta-analysis methods using simple burden test under model (1). Panel B displays the results for three meta-analysis methods using VT under model (1). Panel C displays the results for three meta-analysis methods using SKAT under model (1). Panel D displays the results for three meta-analysis methods using simple burden test under model (2). Panel E displays the results for three meta-analysis methods using VT under model (2). Panel F displays the results for three meta-analysis methods using SKAT under model (2). Panel G displays the results for three meta-analysis methods using simple burden test under model (3). Panel H displays the results for three meta-analysis methods using VT under model (3). Panel I displays the results for three meta-analysis methods using SKAT under model (3). Note that differences between our approach and these alternatives become more marked when more studies are meta-analyzed.