A Markov blanket-based approach for finding high-dimensional genetic interactions associated with disease in family-based studies
- Authors
- Lee, Hyo Jung; Lee, Jae Won; Yoo, Hee Jeong; Jin, Seohoon; Park, Mira
- Issue Date
- 2017
- Publisher
- INDERSCIENCE ENTERPRISES LTD
- Keywords
- genetic associations; gene-gene interactions; Markov blanket; pedigree data; transmission disequilibrium test
- Citation
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, v.18, no.4, pp.269 - 280
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
- Volume
- 18
- Number
- 4
- Start Page
- 269
- End Page
- 280
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86233
- ISSN
- 1748-5673
- 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.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
- Graduate School > Department of Applied Statistics > 1. Journal Articles
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