Evaluating the Power to Discriminate Between Highly Correlated SNPs in Genetic Association Studies

被引：40

作者：

Udler, Miriam S. ^{[1
]}

Tyrer, Jonathan ^{[2
]}

Easton, Douglas F. ^{[1
]}

机构：

[1] Univ Cambridge, Dept Publ Hlth & Primary Care, Strangeways Res Lab, Cambridge CB1 8RN, England

[2] Univ Cambridge, Dept Oncol, Strangeways Res Lab, Cambridge CB1 8RN, England

来源：

GENETIC EPIDEMIOLOGY | 2010年 / 34卷 / 05期

关键词：

genetic association study; power calculation; SNPs; linkage disequilibrium; fine-scale mapping; genome-wide association study; GENOME-WIDE ASSOCIATION; BREAST-CANCER; CAUSAL VARIANTS; RISK; SUSCEPTIBILITY; POPULATIONS;

D O I：

10.1002/gepi.20504

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Neighboring common polymorphisms are often correlated (in linkage disequilibrium (LD)) as a result of shared ancestry. An association between a polymorphism and a disease trait may therefore be the indirect result of a correlated functional variant, and identifying the true causal variant(s) from an initial disease association is a major challenge in genetic association studies. Here, we present a method to estimate the sample size needed to discriminate between a functional variant of a given allele frequency and effect size, and other correlated variants. The sample size required to conduct such fine-scale mapping is typically 1-4 times larger than required to detect the initial association. Association studies in populations with different LD patterns can substantially improve the power to isolate the causal variant. An online tool to perform these calculations is available at http://moya.srl.cam.ac.uk/ocac/FineMappingPowerCalculator.html. Genet. Epidemiol. 34 : 463-468, 2010. (C) 2010 Wiley-Liss, Inc.

引用

页码：463 / 468

页数：6

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