Analysis of genome-wide association study data using the protein knowledge base.

ABSTRACT:

Background

Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.

Results

Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statis

SUBMITTER: Ballouz S 

PROVIDER: S-EPMC3261104 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature