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Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.

ABSTRACT: Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest. However, these technologies also include the challenges associated with the analysis of genome-wide data. Two big challenges are (1) how to reduce the effects of technical noise; and (2) how to handle the curse of dimensionality (i.e., number of variables are way larger than the number of samples). To tackle these challenges, we propose a constrained mixture of Bayesian hierarchical models (MBHM) for detecting disease-associated genomic outcomes for data obtained from paired/matched designs. Paired/matched designs can effectively reduce effects of confounding factors. MBHM does not involve multiple testing, hence does not have the problem of the curse of dimensionality. It also could borrow information across genes so that it can be used for whole genome data with small sample sizes.

SUBMITTER: Li Y

PROVIDER: S-EPMC5373614 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.

Li Yunfeng Y Morrow Jarrett J Raby Benjamin B Tantisira Kelan K Weiss Scott T ST Huang Wei W Qiu Weiliang W

PloS one 20170330 3

Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest. However, these technologies also include the challenges associated with the analysis of genome-wide data. Two big challenges are (1) how to reduce the effects of technical noise; and (2) how to handle the curse of dimensionality (i.e., number of variables a ...[more]

PMID: 28358896

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Dataset Information

Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.

Publications

Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.

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