GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis Article Swipe

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Taban Baghfalaki , Pierre‐Emmanuel Sugier , Yazdan Asgari , Thérèse Truong , Benoît Liquet ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.32614/rj-2023-028

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Pleiotropy

Genome-Wide Association Study

Meta-Analysis

Bayes' Theorem

Feature Selection

Computational Biology

Artificial Intelligence

Medicine

Internal Medicine

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Prior probability Bayesian probability Pleiotropy Genome-wide association study Genetic association Meta-analysis SNP Bayes' theorem Feature selection Covariate Bayesian hierarchical modeling Computational biology Biology Phenotype Statistics Computer science Genetics Single-nucleotide polymorphism Mathematics Gene Artificial intelligence Medicine Genotype Internal medicine

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Type: article
Language: en
Landing Page: https://doi.org/10.32614/rj-2023-028
PDF: https://journal.r-project.org/articles/RJ-2023-028/RJ-2023-028.pdf
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References: 40
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DOI: https://doi.org/10.32614/rj-2023-028

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Title: GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis

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Language: en

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Publication year: 2023

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Publication date: 2023-08-26

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Authors: Taban Baghfalaki, Pierre‐Emmanuel Sugier, Yazdan Asgari, Thérèse Truong, Benoît Liquet

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Landing page: https://doi.org/10.32614/rj-2023-028

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Concepts: Prior probability, Bayesian probability, Pleiotropy, Genome-wide association study, Genetic association, Meta-analysis, SNP, Bayes' theorem, Feature selection, Covariate, Bayesian hierarchical modeling, Computational biology, Biology, Phenotype, Statistics, Computer science, Genetics, Single-nucleotide polymorphism, Mathematics, Gene, Artificial intelligence, Medicine, Genotype, Internal medicine

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