Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases.

ABSTRACT:

Background

Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases.

Results

Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prio

SUBMITTER: Arnold M 

PROVIDER: S-EPMC3782362 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature