Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.

ABSTRACT:

Background

Over the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques.

Methods

We propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iterative

SUBMITTER: Ren J 

PROVIDER: S-EPMC5434559 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature