A two‐phase Bayesian methodology for the analysis of binary phenotypes in genome‐wide association studies

Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome‐wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely lar...

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Bibliographic Details
Published inBiometrical journal Vol. 62; no. 1; pp. 191 - 201
Main Authors Joyner, Chase, McMahan, Christopher, Baurley, James, Pardamean, Bens
Format Journal Article
LanguageEnglish
Published Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.01.2020
Subjects
Bayes factors
Bayes Theorem
Bayesian analysis
Biometry - methods
Colorectal cancer
Colorectal carcinoma
EM algorithm
Female
Gene sequencing
Genetic markers
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genotyping
GWAS
Humans
Male
MAP estimator
Markers
Performance evaluation
Phenotype
Phenotypes
shrinkage prior
Statistical analysis
MAP estimator
GWAS
Bayes factors
EM algorithm
shrinkage prior
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Summary:Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome‐wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two‐phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome‐wide application involving colorectal cancer.
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ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.201900050