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

ABSTRACT:

Background

Due 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.

Methods

We 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 disequilib

SUBMITTER: Kang C 

PROVIDER: S-EPMC3618247 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature