Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.

ABSTRACT:

Background

GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimo

SUBMITTER: Alekseyenko AV 

PROVIDER: S-EPMC3118953 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature