Dataset Information

Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data.

ABSTRACT: Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW.

SUBMITTER: Wang S

PROVIDER: S-EPMC4143720 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data.

Wang Shuaicheng S Fang Shurong S Sha Qiuying Q Zhang Shuanglin S

BMC proceedings 20140617 Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo

Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurem ...[more]

PMID: 25519418

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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 and common variants by testing an optimally weighted combination of variants with longitudinal data.

Publications

Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data.

Similar Datasets

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