Regionally Smoothed Meta-Analysis Methods for GWAS Datasets

• Type of Material: Article
• Authors: Ferdouse Begum, Johns Hopkins Bloomberg Sch Publ Hlth; Monir H. Sharker, University of Pittsburgh; Stephanie Sherman, Emory University; George C. Tseng, University of Pittsburgh; Eleanor Feingold, University of Pittsburgh
• Language: English
• Date: 2016-02-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21949
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 40
• Issue: 2
• Start Page: 154
• End Page: 160
• Grant/Funding Information: National Institutes of Health (NIH) (R01MH077159, RC2HL101715); NIH (R01HD38979, R01DE14899); NIH (R01HD038979); Funding for open access charge: University of Pittsburgh.
• Keywords: PROBABILITY; sliding-window; GENES; Science & Technology; Public, Environmental & Occupational Health; Life Sciences & Biomedicine; Genetics & Heredity; simulation; GWAS meta-analysis; window-based method; GENOME-WIDE ASSOCIATION; TESTS
• Research Categories: Health Sciences, Epidemiology; Biology, Genetics

Abstract

Genome-wide association studies are proven tools for finding disease genes, but it is often necessary to combine many cohorts into a meta-analysis to detect statistically significant genetic effects. Often the component studies are performed by different investigators on different populations, using different chips with minimal SNPs overlap. In some cases, raw data are not available for imputation so that only the genotyped single nucleotide polymorphisms (SNPs) results can be used in meta-analysis. Even when SNP sets are comparable, different cohorts may have peak association signals at different SNPs within the same gene due to population differences in linkage disequilibrium or environmental interactions. We hypothesize that the power to detect statistical signals in these situations will improve by using a method that simultaneously meta-analyzes and smooths the signal over nearby markers. In this study, we propose regionally smoothed meta-analysis methods and compare their performance on real and simulated data.