Leveraging Hierarchical Population Structure in Discrete Association Studies

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 h...

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Main Authors:	Carlson, Jonathan, Kadie, Carl, Mallal, Simon, Heckerman, David
Format:	Online
Language:	English
Published:	Public Library of Science 2007
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1899226/

id	pubmed-1899226
recordtype	oai_dc
spelling	pubmed-18992262007-07-04 Leveraging Hierarchical Population Structure in Discrete Association Studies Carlson, Jonathan Kadie, Carl Mallal, Simon Heckerman, David Research Article 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. Public Library of Science 2007-07-04 /pmc/articles/PMC1899226/ /pubmed/17611623 http://dx.doi.org/10.1371/journal.pone.0000591 Text en Carlson et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Carlson, Jonathan Kadie, Carl Mallal, Simon Heckerman, David
spellingShingle	Carlson, Jonathan Kadie, Carl Mallal, Simon Heckerman, David Leveraging Hierarchical Population Structure in Discrete Association Studies
author_facet	Carlson, Jonathan Kadie, Carl Mallal, Simon Heckerman, David
author_sort	Carlson, Jonathan
title	Leveraging Hierarchical Population Structure in Discrete Association Studies
title_short	Leveraging Hierarchical Population Structure in Discrete Association Studies
title_full	Leveraging Hierarchical Population Structure in Discrete Association Studies
title_fullStr	Leveraging Hierarchical Population Structure in Discrete Association Studies
title_full_unstemmed	Leveraging Hierarchical Population Structure in Discrete Association Studies
title_sort	leveraging hierarchical population structure in discrete association studies
description	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.
publisher	Public Library of Science
publishDate	2007
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1899226/
_version_	1611397667000156160

Leveraging Hierarchical Population Structure in Discrete Association Studies

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