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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...
Shranjeno v:
| izdano v: | BMC Bioinformatics |
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| Main Authors: | , , |
| Format: | Artigo |
| Jezik: | Inglês |
| Izdano: |
BioMed Central
2020
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| Teme: | |
| Online dostop: | 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 |
| Oznake: |
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