Bayesian detection of causal rare variants under posterior consistency.

Handle URI:
http://hdl.handle.net/10754/596769
Title:
Bayesian detection of causal rare variants under posterior consistency.
Authors:
Liang, Faming; Xiong, Momiao
Abstract:
Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.
Citation:
Liang F, Xiong M (2013) Bayesian Detection of Causal Rare Variants under Posterior Consistency. PLoS ONE 8: e69633. Available: http://dx.doi.org/10.1371/journal.pone.0069633.
Publisher:
Public Library of Science (PLoS)
Journal:
PLoS ONE
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
26-Jul-2013
DOI:
10.1371/journal.pone.0069633
PubMed ID:
23922764
PubMed Central ID:
PMC3724943
Type:
Article
ISSN:
1932-6203
Sponsors:
FL's research was partially supported by grants from the National Science Foundation (DMS-1007457 and DMS-1106494) and an award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorLiang, Famingen
dc.contributor.authorXiong, Momiaoen
dc.date.accessioned2016-02-21T08:50:18Zen
dc.date.available2016-02-21T08:50:18Zen
dc.date.issued2013-07-26en
dc.identifier.citationLiang F, Xiong M (2013) Bayesian Detection of Causal Rare Variants under Posterior Consistency. PLoS ONE 8: e69633. Available: http://dx.doi.org/10.1371/journal.pone.0069633.en
dc.identifier.issn1932-6203en
dc.identifier.pmid23922764en
dc.identifier.doi10.1371/journal.pone.0069633en
dc.identifier.urihttp://hdl.handle.net/10754/596769en
dc.description.abstractIdentification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.en
dc.description.sponsorshipFL's research was partially supported by grants from the National Science Foundation (DMS-1007457 and DMS-1106494) and an award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.publisherPublic Library of Science (PLoS)en
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subject.meshModels, Geneticen
dc.subject.meshGenetic Variationen
dc.titleBayesian detection of causal rare variants under posterior consistency.en
dc.typeArticleen
dc.identifier.journalPLoS ONEen
dc.identifier.pmcidPMC3724943en
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texas, United States of America. fliang@stat.tamu.eduen
kaust.grant.numberKUS-C1-016-04en

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