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PLoS One. 2007 Jul 4;2(7):e591.

Leveraging hierarchical population structure in discrete association studies.

Author information

1
Machine Learning and Applied Statistics Group, Microsoft Research, Redmond, Washington, United States of America; Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America.

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 for genotypes that are associated with bacterial resistance traits in Arabidopsis thaliana. We show that coevolution is a better description of confounding in some applications and conditional influence is better in others. That is, we show that no single method is best for addressing all forms of confounding. Analysis tools based on these models are available on the internet as both web based applications and downloadable source code at http://atom.research.microsoft.com/bio/phylod.aspx.

PMID:
17611623
PMCID:
PMC1899226
DOI:
10.1371/journal.pone.0000591
[Indexed for MEDLINE]
Free PMC Article

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