Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/ LBBsoft/IBMS. This journal is © the Partner Organisations 2014.
Original languageEnglish
JournalMolecular BioSystems
DOIs
Publication statusPublished - 2014

Fingerprint

Genome
Linkage Disequilibrium
Alzheimer Disease
Software
Logistic Models
Regression Analysis
Phenotype
Power (Psychology)

Cite this

@article{095402208d4f4eb386974caa427c91aa,
title = "Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data",
abstract = "In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/ LBBsoft/IBMS. This journal is {\circledC} the Partner Organisations 2014.",
author = "M. Aflakparast",
year = "2014",
doi = "10.1039/c4mb00123k",
language = "English",
journal = "Molecular BioSystems",
issn = "1742-206X",
publisher = "Royal Society of Chemistry",

}

Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data. / Aflakparast, M.

In: Molecular BioSystems, 2014.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data

AU - Aflakparast, M.

PY - 2014

Y1 - 2014

N2 - In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/ LBBsoft/IBMS. This journal is © the Partner Organisations 2014.

AB - In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/ LBBsoft/IBMS. This journal is © the Partner Organisations 2014.

U2 - 10.1039/c4mb00123k

DO - 10.1039/c4mb00123k

M3 - Article

JO - Molecular BioSystems

JF - Molecular BioSystems

SN - 1742-206X

ER -