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A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies.

TitleA Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies.
Publication TypeJournal Article
Year of Publication2015
AuthorsRay D, Li X, Pan W, Pankow JS
Secondary AuthorsBasu S
JournalHum Hered
Volume79
Issue2
Pagination69-79
Date Published2015
ISSN1423-0062
KeywordsAtherosclerosis, Bayes Theorem, Case-Control Studies, Computer Simulation, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Humans, Models, Genetic, Polymorphism, Single Nucleotide
Abstract

BACKGROUND: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS.

METHODS: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease.

RESULTS: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes.

CONCLUSION: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.

DOI10.1159/000369858
Alternate JournalHum Hered
PubMed ID26044550
PubMed Central IDPMC4499013
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
268201100011C / / PHS HHS / United States
268201100005C / / PHS HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
268201100007C / / PHS HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
268201100012C / / PHS HHS / United States
268201100008C / / PHS HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R21 DK089351 / DK / NIDDK NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
R21DK089351 / DK / NIDDK NIH HHS / United States
268201100009C / / PHS HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
268201100006C / / PHS HHS / United States
268201100010C / / PHS HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
R01DA033958 / DA / NIDA NIH HHS / United States
R01 DA033958 / DA / NIDA NIH HHS / United States