A Markov blanket-based approach for finding high-dimensional genetic interactions associated with disease in family-based studies
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyo Jung | - |
dc.contributor.author | Lee, Jae Won | - |
dc.contributor.author | Yoo, Hee Jeong | - |
dc.contributor.author | Jin, Seohoon | - |
dc.contributor.author | Park, Mira | - |
dc.date.accessioned | 2021-09-03T14:46:16Z | - |
dc.date.available | 2021-09-03T14:46:16Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 1748-5673 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86233 | - |
dc.description.abstract | Detecting genetic interactions associated with complex disease is a major issue in genetic studies. Although a number of methods to detect gene-gene interactions for population-based Genome-Wide Association Studies (GWAS) have been developed, the statistical methods for family-based GWAS have been limited. In this study, we propose a new Bayesian approach called MB-TDT to find high-order genetic interactions for pedigree data. The MB-TDT method combines the Markov blanket algorithm with classical Transmission Disequilibrium Test (TDT) statistic. The Incremental Association Markov Blanket (IAMB) algorithm was adopted for large-scale Markov blanket discovery. We evaluated the proposed method using both real and simulated data sets. In a simulation study, we compared the power of MB-TDT with conditional logistic regression, Multifactor Dimensionality Reduction (MDR) and MDR-pedigree disequilibrium test (MDR-PDT). We demonstrated the superior power of MB-TDT in many cases. To demonstrate the approach, we analysed the Korean autism disorder GWAS data. The MB-TDT method can identify a minimal set of causal SNPs associated with a specific disease, thus avoiding an exhaustive search. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | INDERSCIENCE ENTERPRISES LTD | - |
dc.subject | GENOME-WIDE ASSOCIATION | - |
dc.subject | ENVIRONMENT INTERACTIONS | - |
dc.subject | COMBINATORIAL APPROACH | - |
dc.subject | DESIGNS | - |
dc.subject | TRAITS | - |
dc.subject | PDT | - |
dc.subject | AGE | - |
dc.title | A Markov blanket-based approach for finding high-dimensional genetic interactions associated with disease in family-based studies | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Jae Won | - |
dc.contributor.affiliatedAuthor | Jin, Seohoon | - |
dc.identifier.scopusid | 2-s2.0-85035761643 | - |
dc.identifier.wosid | 000418434900001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, v.18, no.4, pp.269 - 280 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | - |
dc.citation.volume | 18 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 269 | - |
dc.citation.endPage | 280 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | GENOME-WIDE ASSOCIATION | - |
dc.subject.keywordPlus | ENVIRONMENT INTERACTIONS | - |
dc.subject.keywordPlus | COMBINATORIAL APPROACH | - |
dc.subject.keywordPlus | DESIGNS | - |
dc.subject.keywordPlus | TRAITS | - |
dc.subject.keywordPlus | PDT | - |
dc.subject.keywordPlus | AGE | - |
dc.subject.keywordAuthor | genetic associations | - |
dc.subject.keywordAuthor | gene-gene interactions | - |
dc.subject.keywordAuthor | Markov blanket | - |
dc.subject.keywordAuthor | pedigree data | - |
dc.subject.keywordAuthor | transmission disequilibrium test | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.