Year: 2012
Type: article
Abstract: Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect.
Source: PLoS Genetics
Authors: Buhm Han, Eleazar Eskin
Institution: University of California, Los Angeles
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G1104670545, Substance Abuse & Behavioral Disinhibition: Integrating Genes & Environment, III: Small: Inference of Causal Regulatory Relationships from Genetic Studies, Lipid and Lipoprotein Metabolism in Atherosclerosis, G3755850621, III: Medium: Private Identification of Relatives and Private GWAS: First Steps in the New Field of CryptoGenomics, Discovering the Genetic Basis of Hypertension, Collaborative Research: SEIII: Estimating Haplotype Frequencies, Collaborative Research: SEIII: Estimating Haplotype Frequencies, Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays, Molecular Genetic Approaches in Atherosclerosis Research, G9000065387, G9000097946, G9000148945, G9000225580
OpenAlex ID: W2086493366