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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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| I publikationen: | BMC Bioinformatics |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
BioMed Central
2020
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7204256/ https://ncbi.nlm.nih.gov/pubmed/32381021 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-020-3387-z |
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