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

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 consider r...

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Detalles Bibliográficos
Autores principales: Shafquat, Afrah, Crystal, Ronald G., Mezey, Jason G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204256/
https://www.ncbi.nlm.nih.gov/pubmed/32381021
http://dx.doi.org/10.1186/s12859-020-3387-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204256/
https://www.ncbi.nlm.nih.gov/pubmed/32381021
http://dx.doi.org/10.1186/s12859-020-3387-z

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