Abstract
Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
Highlights
It is widely accepted that many diseases are caused by a complex interplay between multiple genes and other non-genetic factors, and that different genetic susceptibility factors may be responsible for disease risks in different individuals
It can be seen that the overall prevalence is very close to 0.05, the baseline risk value used in the simulation and the estimated average population attributable rate (PAR) is between the range 0–0.05
Family-based design are robust from bias due to population substructure and is useful for rare variant analysis since the issue of population stratification is more prominent for rare variants
Summary
It is widely accepted that many diseases are caused by a complex interplay between multiple genes and other non-genetic factors, and that different genetic susceptibility factors may be responsible for disease risks in different individuals. Genome-wide association studies have been able to identify many variants associated with complex diseases that are common in the population. Most of the estimated genetic contribution explained by these common variants appears to be very modest. Rare variants are thought to have a larger effect size compared to common SNPs [1]. Availability of sequencing data from specific candidate genes and functional genomic regions such as exons for a large number of individuals and from whole genome for a smaller set of individuals [2], has made it possible to gain a wealth of information about the potential effect of multiple rare variants on complex phenotypes. Conventional statistical methods for common variants have low power for low frequency SNPs, when the power relies on the linkage disequilibrium (LD) between the causal variants and the observed markers
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