Inferring Alcoholism SNPs and Regulatory Chemical Compounds Based on Ensemble Bayesian Network

Title:Inferring Alcoholism SNPs and Regulatory Chemical Compounds Based on Ensemble Bayesian Network

Volume: 20 Issue: 2

Author(s): Huan Chen, Jiatong Sun, Hong Jiang, Xianyue Wang, Lingxiang Wu, Wei Wu and Qh Wang*

Affiliation:

• Department of Bioinformatics, Nanjing Medical University, Nanjing 210029,China

Keywords: SNPs, ensemble bayesian network, alcoholism, eQTLs, chemical compound.

Abstract: Aim and Objective: The disturbance of consciousness is one of the most common symptoms of those have alcoholism and may cause disability and mortality. Previous studies indicated that several single nucleotide polymorphisms (SNP) increase the susceptibility of alcoholism. In this study, we utilized the Ensemble Bayesian Network (EBN) method to identify causal SNPs of alcoholism based on the verified GAW14 data.

Materials and Methods: We built a Bayesian network combining random process and greedy search by using Genetic Analysis Workshop 14 (GAW14) dataset to establish EBN of SNPs. Then we predicted the association between SNPs and alcoholism by determining Bayes’ prior probability.

Results and Conclusion: Thirteen out of eighteen SNPs directly connected with alcoholism were found concordance with potential risk regions of alcoholism in OMIM database. As many SNPs were found contributing to alteration on gene expression, known as expression quantitative trait loci (eQTLs), we further sought to identify chemical compounds acting as regulators of alcoholism genes captured by causal SNPs. Chloroprene and valproic acid were identified as the expression regulators for genes C11orf66 and SALL3 which were captured by alcoholism SNPs, respectively.

Cite this article as:

Chen Huan, Sun Jiatong, Jiang Hong, Wang Xianyue, Wu Lingxiang, Wu Wei and Wang Qh*, Inferring Alcoholism SNPs and Regulatory Chemical Compounds Based on Ensemble Bayesian Network, Combinatorial Chemistry & High Throughput Screening 2017; 20 (2) . https://dx.doi.org/10.2174/1386207319666161220114917

DOI https://dx.doi.org/10.2174/1386207319666161220114917	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402