Univariate/multivariate genome-wide association scans using data from families and unrelated samples.

ABSTRACT: As genome-wide association studies (GWAS) are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs), we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test

SUBMITTER: Zhang L 

PROVIDER: S-EPMC2715864 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature