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A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children.

ABSTRACT: With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population-based methods for unrelated individuals. A limitation of population-based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, because the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population-based rare variant associations. A solution to the problem of population stratification is to use family-based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW-PAC). TOW-PAC is a family-based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family-based association tests are robust to population stratification although population-based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW-PAC increases with an increase of the number of affected children in each family and TOW-PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.

SUBMITTER: Sha Q

PROVIDER: S-EPMC4162402 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Project description:BackgroundWith the advent of next-generation sequencing (NGS) technologies, researchers are now generating a deluge of data on high dimensional genomic variations, whose analysis is likely to reveal rare variants involved in the complex etiology of disease. Standing in the way of such discoveries, however, is the fact that statistics for rare variants are currently designed for use with population-based data. In this paper, we introduce a pedigree-based statistic specifically designed to test for rare variants in family-based data. The additional power of pedigree-based statistics stems from the fact that while rare variants related to diseases or traits of interest occur only infrequently in populations, in families with multiple affected individuals, such variants are enriched. Note that while the proposed statistic can be applied with and without statistical weighting, our simulations show that its power increases when weighting (WSS and VT) are applied.ResultsOur working hypothesis was that, since rare variants are concentrated in families with multiple affected individuals, pedigree-based statistics should detect rare variants more powerfully than population-based statistics. To evaluate how well our new pedigree-based statistics perform in association studies, we develop a general framework for sequence-based association studies capable of handling data from pedigrees of various types and also from unrelated individuals. In short, we developed a procedure for transforming population-based statistics into tests for family-based associations. Furthermore, we modify two existing tests, the weighted sum-square test and the variable-threshold test, and apply both to our family-based collapsing methods. We demonstrate that the new family-based tests are more powerful than corresponding population-based test and they generate a reasonable type I error rate.To demonstrate feasibility, we apply the newly developed tests to a pedigree-based GWAS data set from the Framingham Heart Study (FHS). FHS-GWAS data contain approximately 5000 uncommon variants with frequencies less than 0.05. Potential association findings in these data demonstrate the feasibility of the software PB-STAR (note, PB-STAR is now freely available to the public).ConclusionOur tests show that when analyzing for rare variants, a pedigree-based design is more powerful than a population-based case-control design. We further demonstrate that a pedigree-based statistic's power to detect rare variants increases in direct relation to the proportion of affected individuals within the pedigree.

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A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children.

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