A pattern recognition-based methods for genetic networks construction with alcohol-related disease

Recently improvements in the accessibility of high-throughput genotyping have brought a deal of attention to genome-wide association studies for common complex diseases. Although, such diseases can be caused by multi-loci interactions. Locus-by-locus studies are prevailing. Recently, two-loci analysis has been shown promising. And multi-loci analysis is expected to find even deeper disease-associated interactions. Here, we focused on the relationship between the genetic interactions and the pathogenesis. From computational systems biological and three dimensional viewpoints, this work carefully review on identifying tar-get genes based on SNP genetic profiles for alcohol-related diseases, reverse engineering genetic networks from multi-levels (SNPs dummy networks, gene interaction networks) and map these heterogeneous networks from biology and topology. We propose mining approach to extract the relevant SNPs for alcoholism using sib-pair IBD profiles of pedigrees. Application to the Genetic Analysis Workshop 14 (GAW14) real dataset that the proposed ensemble decision approach has successfully identified most of the true loci, thus implicating that IBD statistic could be used as one of the informatics for mining the genetic underpins for complex human diseases. In the last section of this paper, the significance of these findings in the context of biological processes of alcoholism related disease is discussed. This study represents a pioneering and systematic endeavor to probe the underlying genetic complexities for complex diseases.