This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data
1 Division of Biology and Biomedical Sciences, Washington University School of Medicine, 660 South Euclid Avenue, Box 8226, St. Louis, MO 63110, USA
2 Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, Box 8232, St. Louis, MO 63110, USA
BMC Proceedings 2011, 5(Suppl 9):S109 doi:10.1186/1753-6561-5-S9-S109Published: 29 November 2011
Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.