Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Abstract Background Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data co...

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Bibliographic Details
Main Authors: Afrah Shafquat, Ronald G. Crystal, Jason G. Mezey
Format: Article
Language:English
Published: BMC 2020-05-01
Series:BMC Bioinformatics
Subjects:
GWAS
Misclassification
Bayesian
Hierarchical latent variable models
MCMC
UK Biobank
Online Access:http://link.springer.com/article/10.1186/s12859-020-3387-z

Internet

http://link.springer.com/article/10.1186/s12859-020-3387-z

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