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Gyungbu Kim;Yoonsuk Lee;Jeong Ho Park;Dongmin Kim;Wonseok Lee
- Genomics & Informatics
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- v.20 no.4
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- pp.49.1-49.7
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- 2022
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.
https://doi.org/10.5808/gi.22046 인용 PDF KSCI

Li, Bai-Rong;Zhou, Guo-Wu;Bian, Qi;Song, Bin
- Asian Pacific Journal of Cancer Prevention
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- v.13 no.4
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- pp.1145-1149
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- 2012
Aim: To clarify any association between the hOGG1 Ser326Cys polymorphism and susceptibility to gastric cancer. Methods: A meta-analysis based on 11 eligible case-control studies involving 5,107 subjects was carried out to summarize the data on the association between hOGG1 Ser326Cys polymorphism and gastric cancer risk. Results: No association was found between hOGG1 Ser326Cys polymorphism and gastric cancer risk (dominant model: OR = 0.95, 95% CI: 0.83-1.09, p = 0.486, ph (p values for heterogeneity) = 0.419; additive model: OR = 1.02, 95% CI: 0.81-1.30, p = 0.850, ph = 0.181; recessive model: OR = 1.09, 95% CI: 0.80-1.48, p = 0.586, ph = 0.053). Subgroup analysis based on ethnicity (Asian and Caucasian) and smoking status (ever smoker and never smoker) did did notpresent any significant association. Sensitivity analysis did not perturb the results. Conclusions: This study strongly suggested there might be no association between the hOGG1 Ser326Cys polymorphism and gastric cancer risk. However, larger scale studies are needed for confirmation.
https://doi.org/10.7314/APJCP.2012.13.4.1145 인용 PDF KSCI

Jiang, Nan;Lee, Sungyoung;Park, Taesung
- Genomics & Informatics
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- v.18 no.1
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- pp.11.1-11.3
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- 2020
In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.
https://doi.org/10.5808/GI.2020.18.1.e11 인용 PDF KSCI