Leveraging hierarchical population structure in discrete association studies.

ABSTRACT: Population structure can confound the identification of correlations in biological data. Such confounding has been recognized in multiple biological disciplines, resulting in a disparate collection of proposed solutions. We examine several methods that correct for confounding on discrete data with hierarchical population structure and identify two distinct confounding processes, which we call coevolution and conditional influence. We describe these processes in terms of generative models and show that these generative models can be used to correct for the confounding effects. Finally, we apply the models to three applications: identification of escape mutations in HIV-1 in response to specific HLA-mediated immune pressure, prediction of coevolving residues in an HIV-1 peptide, and a search

SUBMITTER: Carlson J 

PROVIDER: S-EPMC1899226 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature