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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 consider reported disease phenotype values as is without accounting for potential misclassification. RESULTS:Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation. CONCLUSION:PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

SUBMITTER: Shafquat A

PROVIDER: S-EPMC7204256 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Shafquat Afrah A Crystal Ronald G RG Mezey Jason G JG

BMC bioinformatics 20200507 1

<h4>Background</h4>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.<h4>Results</h4>Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx ...[more]

PMID: 32381021

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Project description:MotivationHigh-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.ResultsWe present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.Availability and implementationThe software is available at https://github.com/hao-peng/DEIsoM.Contactpengh@alumni.purdue.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Publications

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

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