Meta-analysis of gene-level tests for rare variant association.
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ABSTRACT:
SUBMITTER: Liu DJ
PROVIDER: S-EPMC3939031 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
Publications
Meta-analysis of gene-level tests for rare variant association.
Liu Dajiang J DJ Liu Dajiang J DJ Peloso Gina M GM Zhan Xiaowei X Holmen Oddgeir L OL Zawistowski Matthew M Feng Shuang S Nikpay Majid M Auer Paul L PL Goel Anuj A Zhang He H Peters Ulrike U Farrall Martin M Orho-Melander Marju M Kooperberg Charles C McPherson Ruth R Watkins Hugh H Willer Cristen J CJ Hveem Kristian K Melander Olle O Kathiresan Sekar S Abecasis Gonçalo R GR
Nature genetics 20131215 2
The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new appr ...[more]
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Project description:The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers.
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