Literature DB >> 22714994

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

Qiuying Sha1, Xuexia Wang, Xinli Wang, Shuanglin Zhang.   

Abstract

Next-generation sequencing technology will soon allow sequencing the whole genome of large groups of individuals, and thus will make directly testing rare variants possible. Currently, most of existing methods for rare variant association studies are essentially testing the effect of a weighted combination of variants with different weighting schemes. Performance of these methods depends on the weights being used and no optimal weights are available. By putting large weights on rare variants and small weights on common variants, these methods target at rare variants only, although increasing evidence shows that complex diseases are caused by both common and rare variants. In this paper, we analytically derive optimal weights under a certain criterion. Based on the optimal weights, we propose a Variable Weight Test for testing the effect of an Optimally Weighted combination of variants (VW-TOW). VW-TOW aims to test the effects of both rare and common variants. VW-TOW is applicable to both quantitative and qualitative traits, allows covariates, can control for population stratification, and is robust to directions of effects of causal variants. Extensive simulation studies and application to the Genetic Analysis Workshop 17 (GAW17) data show that VW-TOW is more powerful than existing ones either for testing effects of both rare and common variants or for testing effects of rare variants only.
© 2012 Wiley Periodicals, Inc.

Mesh:

Year:  2012        PMID: 22714994     DOI: 10.1002/gepi.21649

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  43 in total

1.  Detecting association of rare and common variants by adaptive combination of P-values.

Authors:  Yajing Zhou; Yong Wang
Journal:  Genet Res (Camb)       Date:  2015-10-06       Impact factor: 1.588

2.  ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS.

Authors:  Xiaowei Wu; Ting Guan; Dajiang J Liu; Luis G León Novelo; Dipankar Bandyopadhyay
Journal:  Ann Appl Stat       Date:  2018-09-11       Impact factor: 2.083

3.  A rare variant association test based on combinations of single-variant tests.

Authors:  Qiuying Sha; Shuanglin Zhang
Journal:  Genet Epidemiol       Date:  2014-07-25       Impact factor: 2.135

4.  A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes.

Authors:  Lili Chen; Yajing Zhou
Journal:  Genes Genomics       Date:  2021-01-11       Impact factor: 1.839

5.  From exomes to genomes: challenges and solutions in population-based genetic association studies.

Authors:  Paul L Auer; Suzanne M Leal
Journal:  Eur J Hum Genet       Date:  2017-01-25       Impact factor: 4.246

6.  Test of rare variant association based on affected sib-pairs.

Authors:  Qiuying Sha; Shuanglin Zhang
Journal:  Eur J Hum Genet       Date:  2014-03-26       Impact factor: 4.246

7.  A Powerful Variant-Set Association Test Based on Chi-Square Distribution.

Authors:  Zhongxue Chen; Tong Lin; Kai Wang
Journal:  Genetics       Date:  2017-09-14       Impact factor: 4.562

8.  Association analysis of multiple traits by an approach of combining P values.

Authors:  Lili Chen; Yong Wang; Yajing Zhou
Journal:  J Genet       Date:  2018-03       Impact factor: 1.166

9.  Association analysis of rare and common variants with multiple traits based on variable reduction method.

Authors:  Lili Chen; Yong Wang; Yajing Zhou
Journal:  Genet Res (Camb)       Date:  2018-02-01       Impact factor: 1.588

10.  Permutation testing in the presence of polygenic variation.

Authors:  Mark Abney
Journal:  Genet Epidemiol       Date:  2015-03-10       Impact factor: 2.135

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