Exploring the Major Sources and Extent of Heterogeneity in a Genome‐Wide Association Meta‐Analysis |
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| Authors: | Yu‐Fang Pei Qing Tian Lei Zhang Hong‐Wen Deng |
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| Affiliation: | 1. Department of Epidemiology and Medical Statistics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China;2. Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, P. R. China;3. Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA;4. Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, P. R. China |
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| Abstract: | Genome‐wide association (GWA) meta‐analysis has become a popular approach for discovering genetic variants responsible for complex diseases. The between‐study heterogeneity effect is a severe issue that may complicate the interpretation of results. Aiming to improve the interpretation of meta‐analysis results, we empirically explored the extent and source of heterogeneity effect. We analyzed a previously reported GWA meta‐analysis of obesity, in which over 21,000 subjects from seven individual samples were meta‐analyzed. We first evaluated the extent and distribution of heterogeneity across the entire genome. We then studied the effects of several potentially confounding factors, including age, ethnicity, gender composition, study type, and genotype imputation on heterogeneity with a random‐effects meta‐regression model. Of the total 4,325,550 SNPs being tested, heterogeneity was moderate to very large for 25.4% of the total SNPs. Heterogeneity was more severe in SNPs with stronger association signals. Ethnicity, average age, and genotype imputation accuracy had significant effects on the heterogeneity. Exploring the effects of ethnicity can provide clues to the potential ethnic‐specific effects for two loci known to affect obesity, MC4R, and MTCH2. Our analysis can help to clarify understanding of the obesity mechanism and may provide guidance for an effective design of future GWA meta‐analysis. |
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| Keywords: | Genome‐wide association study obesity heterogeneity meta‐analysis meta‐regression |
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