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

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 b...

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Detalles Bibliográficos
Autores principales: Kim, Gyungbu, Lee, Yoonsuk, Park, Jeong Ho, Kim, Dongmin, Lee, Wonseok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847376/
https://www.ncbi.nlm.nih.gov/pubmed/36617656
http://dx.doi.org/10.5808/gi.22046
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author Kim, Gyungbu
Lee, Yoonsuk
Park, Jeong Ho
Kim, Dongmin
Lee, Wonseok
author_facet Kim, Gyungbu
Lee, Yoonsuk
Park, Jeong Ho
Kim, Dongmin
Lee, Wonseok
author_sort Kim, Gyungbu
collection PubMed
description 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.
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spelling pubmed-98473762023-01-31 Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies Kim, Gyungbu Lee, Yoonsuk Park, Jeong Ho Kim, Dongmin Lee, Wonseok Genomics Inform Application Note 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. Korea Genome Organization 2022-12-30 /pmc/articles/PMC9847376/ /pubmed/36617656 http://dx.doi.org/10.5808/gi.22046 Text en (c) 2022, Korea Genome Organization https://creativecommons.org/licenses/by/4.0/(CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Application Note
Kim, Gyungbu
Lee, Yoonsuk
Park, Jeong Ho
Kim, Dongmin
Lee, Wonseok
Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title_full Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title_fullStr Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title_full_unstemmed Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title_short Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies
title_sort beta-meta: a meta-analysis application considering heterogeneity among genome-wide association studies
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847376/
https://www.ncbi.nlm.nih.gov/pubmed/36617656
http://dx.doi.org/10.5808/gi.22046
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