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Article

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

BMC bioinformatics - 7 May 2020

Shafquat Afrah, Crystal Ronald G, Mezey Jason G

Abstract excerpt

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 reported disease phenotype values as is without accounting for potential misclassification. RESULTS: Here, we...

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Identifiers

PMID: 32381021
DOI: 10.1186/s12859-020-3387-z
PMCID: PMC7204256

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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