Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

Abstract

BackgroundDue to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.MethodsWe aim to detect type 2 diabetes (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. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.ResultsA T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.ConclusionsWe 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.

Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification

“…Furthermore, we analyzed type 2 diabetes (T2D) associated SNPs that were identified in our previous study [ 22 ] by using Wellcome Trust Case Control Consortium (WTCCC) datasets [ 23 ]. T2D-associated SNPs with p-values < 1.0 × 10 -5 were identified from quality controlled (QC) 409,656 SNPs, based on Cochran-Armitage trend test statistics using PLINK 1.07 [ 24 ].…”

The Genomic Prediction of Disease: Example of type 2 diabetes (T2D)

“…This implies a very wide gap in ancestries for the WT and Starr databases; possible conclusions are that the WT Snps were poorly suited for the Starr County subjects, and that the Starr County gene array did not go deep enough in the set of SNPs to account for their ancestry, see Discussion. ¶Random Forests: Random Forests (RF), a machine learning algorithm has recently been applied to the WT T2D database (24,25).…”

Epigenomes: The missing heritability in human cardiovascular disease?

“…An alternative explanation is that the genetic risk reveals itself only though a combination of SNPs, which is in agreement with the common interpretation of GWAS findings in cardiovascular disease as indicating many common SNPs each with small contribution to the phenotype. Studies from the noncardiovascular arena have shown the utility of such multi‐SNP approaches, including for body mass index and type II diabetes . But what are the cellular mechanisms through which these multiple SNPs, many of which reside in nonprotein coding regions , interact to cause complex common disease?…”