Abstract
Recent studies suggest that the traditional case-control study design does not have sufficient power to discover rare risk variants. Two different methods—collapsing and family data—are suggested as alternatives for discovering these rare variants. Compared with common variants, rare variants have unique characteristics. In this paper, we assess the distribution of rare variants in family data. We notice that a large number of rare variants exist only in one or two families and that the association result is largely shaped by those families. Therefore we explore the possibility of integrating both the collapsing method and the family data method. This combinational approach offers a potential power boost for certain causal genes, including VEGFA, VEGFC, SIRT1, SREBF1, PIK3R3, VLDLR, PLAT, and FLT4, and thus deserves further investigation.
Highlights
Genome-wide association studies have accelerated the discovery of genetic variants that cause disease
In the Genetic Analysis Workshop 17 (GAW17) data there are 18,131 rare single-nucleotide polymorphism (SNPs), 56.4% of which do not exist in the family data
Collapsing rare variants within family-based association test As we have shown, for a particular rare risk variant, only one or two families contribute to the signal, but one gene may have multiple risk variants, each of which may be possessed by different families
Summary
Genome-wide association studies have accelerated the discovery of genetic variants that cause disease. Nearly 600 genome-wide association studies have examined about 150 distinct diseases or traits, and more than 800 single-nucleotide polymorphism (SNPs) associated with these diseases or traits have been identified [1]. Two common approaches are used to increase the power to detect rare variants. By grouping risk variants together, the frequency of rare risk variants can be increased in the data set. Extensive research on collapsing has been done for populationbased data [2]. Another approach is to examine family data. The potential advantage of family data is that a particular rare variant found in an affected individual is more common in that individual’s family than in subjects randomly sampled in the population
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