Detecting high-dimensional genetic associations using a Markov-Blanket in a family-based study

In recent years, detecting interactions between different genes has become a hot topic, for better understanding multigenic, complex diseases. For population-based genome-wide association studies (GWAS), a number of methods to detect gene-gene interactions such as logistic regression, multifactor dimensionality reduction (MDR) and support vector machine (SVM), have been applied. Bayesian approaches such as BEAM (Bayesian marker partition model) and DASSO-MB (detection of association using Markov Blanket) have also been suggested. However, the studies for family-based GWAS have been limited. In this study, we developed a new Markov Blanket-based algorithm called MB-TDT to find gene-gene interactions for pedigree data. A transmission disequilibrium test statistic was used as an association measure and the incremental association a Markov Blanket (IAMB) algorithm was applied to find Markov Blanket. This proposed MB-TDT method can identify a minimal set of causal SNPs, associated with a specific disease, thus avoiding an exhaustive search. By conducting a simulation study to compare MB-TDT with current methods, we show its superior high power in many cases, and lower false positive rates, in others.