A Markov blanket-based approach for finding high-dimensional genetic interactions associated with disease in family-based studies
by Hyo Jung Lee; Jae Won Lee; Hee Jeong Yoo; Seohoon Jin; Mira Park
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 18, No. 4, 2017

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.

Online publication date: Fri, 24-Nov-2017

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