Statistical Science

On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs

Ruth M. Pfeiffer, Mitchell H. Gail, and David Pee

Full-text: Open access

Abstract

Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies. We compared several procedures to combine GWA study data both in terms of the power to detect a disease-associated SNP while controlling the genome-wide significance level, and in terms of the detection probability (DP). The DP is the probability that a particular disease-associated SNP will be among the T most promising SNPs selected on the basis of low p-values. We studied both fixed effects and random effects models in which associations varied across studies. In settings of practical relevance, meta-analytic approaches that focus on a single degree of freedom had higher power and DP than global tests such as summing chi-square test-statistics across studies, Fisher’s combination of p-values, and forming a combined list of the best SNPs from within each study.

Article information

Source
Statist. Sci., Volume 24, Number 4 (2009), 547-560.

Dates
First available in Project Euclid: 20 April 2010

Permanent link to this document
https://projecteuclid.org/euclid.ss/1271770348

Digital Object Identifier
doi:10.1214/09-STS286

Mathematical Reviews number (MathSciNet)
MR2779343

Zentralblatt MATH identifier
1329.62434

Keywords
Whole genome scans hypothesis testing random effects Wald test multiple comparison

Citation

Pfeiffer, Ruth M.; Gail, Mitchell H.; Pee, David. On Combining Data From Genome-Wide Association Studies to Discover Disease-Associated SNPs. Statist. Sci. 24 (2009), no. 4, 547--560. doi:10.1214/09-STS286. https://projecteuclid.org/euclid.ss/1271770348


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