Pooled association tests for rare variants in exon-resequencing studies

Abstract

Deep sequencing will soon generate comprehensive sequence information in large disease samples. Although the power to detect association with an individual rare variant is limited, pooling variants by gene or pathway into a composite test provides an alternative strategy for identifying susceptibility genes. We describe a statistical method for detecting association of multiple rare variants in protein-coding genes with a quantitative or dichotomous trait. The approach is based on the regression of phenotypic values on individuals' genotype scores subject to a variable allele-frequency threshold, incorporating computational predictions of the functional effects of missense variants. Statistical significance is assessed by permutation testing with variable thresholds. We used a rigorous population-genetics simulation framework to evaluate the power of the method, and we applied the method to empirical sequencing data from three disease studies.

Figure 1

Probability ϕ(p) that a Nonsynonymous Variant Is Functional as a Function of Allele Frequency p Results are based on 10,000 independent simulations in which we vary the selection parameter s: s = 0.01 (log₁₀s = −2), s = 0.001 (log₁₀s = −3), or s = 0.0001 (log₁₀s = −4). We also plot the value ϕ(p) ∼$1/\sqrt{p\left(1-p\right)}$ (as implicitly assumed by the weighted approach). All probabilities are normalized by the corresponding probability for singleton mutations. Panels (A) and (B) are identical except for the different range of the allele frequency p, with panel (B) restricted to very rare alleles.