Machine Learning
DTMBIO 2012: international workshop on data and text mining in biomedical informatics
Proceedings of the 21st ACM international conference on Information and knowledge management
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The identification of causal single nucleotide polymorphisms (SNPs) for complex diseases like type 2 diabetes (T2D) is a challenge because of the low statistical power of individual markers from a genome-wide association study (GWAS). SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. Hence, we aim to detect T2D causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. The selected SNPs with SNP combinations are mapped with multi-dimensional levels of T2D-related information and gene set enrichment analysis (GSEA). A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected, with an error rate of 10.25%. Matching with known disease genes and gene sets revealed the relationships between T2D and SNP combinations. We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.