A groupwise association test for rare mutations using a weighted sum statistic

Abstract

Resequencing is an emerging tool for identification of rare disease-associated mutations. Rare mutations are difficult to tag with SNP genotyping, as genotyping studies are designed to detect common variants. However, studies have shown that genetic heterogeneity is a probable scenario for common diseases, in which multiple rare mutations together explain a large proportion of the genetic basis for the disease. Thus, we propose a weighted-sum method to jointly analyse a group of mutations in order to test for groupwise association with disease status. For example, such a group of mutations may result from resequencing a gene. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated genes, both on simulated and Encode data. Using the weighted-sum method, a resequencing study can identify a disease-associated gene with an overall population attributable risk (PAR) of 2%, even when each individual mutation has much lower PAR, using 1,000 to 7,000 affected and unaffected individuals, depending on the underlying genetic model. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used.

Figure 1. Genetic models.

Model descriptions and examples of predisposing genotypes are shown for the genetic models used. Lines symbolise haplotypes and dots symbolise disease-risk mutations.

Figure 2. Power versus PAR of group.

The power of the investigated methods is shown for different levels of group-PAR. The power simulations were performed using n^A = n^U = 1000 individuals, 50 D-variants, 50 N-variants and p_M = 10%.

Figure 3. Power versus number of D-variants.

The power of the investigated methods is shown for different number of D-variants (disease-risk contributing variants). The power simulations were performed using n^A = n^U = 1000 individuals, 50% D-variants, group PAR of 10% and p_M = 10%. Note that the jump in the power for the CMC method under the Recessive-set model occurs because a low number of variants yields a high allele-frequency of each variant, and the variants are hence not grouped by the CMC method.

Figure 4. Power versus proportion of D-variants.

The power of the investigated methods is shown for different proportions of D-variants (disease-risk contributing variants). The power simulations were performed using n^A = n^U = 1000 individuals, 50 D-variants, group PAR of 10% and p_M = 10%.