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http://dx.doi.org/10.5808/gi.22046

Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies  

Gyungbu Kim (Medical Genomics R&D, JLK Inc.)
Yoonsuk Lee (Medical Genomics R&D, JLK Inc.)
Jeong Ho Park (Medical Genomics R&D, JLK Inc.)
Dongmin Kim (Medical Genomics R&D, JLK Inc.)
Wonseok Lee (Medical Genomics R&D, JLK Inc.)
Publication Information
Genomics & Informatics / v.20, no.4, 2022 , pp. 49.1-49.7 More about this Journal
Abstract
Many packages for a meta-analysis of genome-wide association studies (GWAS) have been developed to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easily process a meta-analysis by automatically selecting between a fixed effects and a random effects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in a single process. It provides a step-by-step meta-analysis of GWAS for each association in the following order: heterogeneity test, two different calculations of an effect size and a p-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. These methods enable users to validate the results of individual studies with greater statistical power and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.
Keywords
genome-wide association studies; heterogeneity; meta-analysis; python application; single nucleotide polymorphism;
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