Dataset Information

Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families.

ABSTRACT: Although next-generation sequencing technology allows sequencing the whole genome of large groups of individuals, the development of powerful statistical methods for rare variant association studies is still underway. Even though many statistical methods have been developed for mapping rare variants, most of these methods are for unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. The majority of the existing methods for unrelated individuals is essentially testing the effect of a weighted combination of variants with different weighting schemes. The performance of these methods depends on the weights being used. Recently, researchers proposed a test for Testing the effect of an Optimally Weighted combination of variants (TOW) for unrelated individuals. In this article, we extend our previously developed TOW for unrelated individuals to family-based data and propose a novel test for Testing the effect of an Optimally Weighted combination of variants for Family-based designs (TOW-F). The optimal weights are analytically derived. The results of extensive simulation studies show that TOW-F is robust to population stratification in a wide range of population structures, is robust to the direction and magnitude of the effects of causal variants, and is relatively robust to the percentage of neutral variants.

SUBMITTER: Fang S

PROVIDER: S-EPMC3932153 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families.

Fang Shurong S Zhang Shuanglin S Sha Qiuying Q

Annals of human genetics 20130822 6

Although next-generation sequencing technology allows sequencing the whole genome of large groups of individuals, the development of powerful statistical methods for rare variant association studies is still underway. Even though many statistical methods have been developed for mapping rare variants, most of these methods are for unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. The majority of the existing methods for unrelated individuals ...[more]

PMID: 23968488

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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.

Dataset Information

Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families.

Publications

Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families.

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