A Markov blanket-based approach for finding high-dimensional genetic interactions associated with disease in family-based studies. (2017)

• Record Type: Journal Article
• Authors: Lee, Hyo Jung; Lee, Jae Won; Yoo, Hee Jeong; Jin, Seohoon; Park, Mira
• Is Part Of: International journal of data mining and bioinformatics. Volume 18:Number 4(2017)
• Page Start: 269
• Page End: 280
• Publication Date: 2017
• Subjects: genetic associations -- gene-gene interactions -- Markov blanket -- pedigree data -- transmission disequilibrium test; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 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.