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Bioinformatics Advance Access published online on February 22, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn070
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Tree-guided Bayesian Inference of Population Structures

Yu Zhang *

Department of Statistics, the Pennsylvania State University, 325 Thomas Bldg, State College, PA.

*To whom correspondence should be addressed. Dr. Yu Zhang, E-mail: yuzhang{at}stat.psu.edu


  Abstract

Motivation: Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.

Results: We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.

Availability: http://www.stat.psu.edu/~yuzhang/tips.tar

Contact: yuzhang{at}stat.psu.edu

Associate Editor: Prof. Keith Crandall

Received on December 5, 2007; revised on February 3, 2008; accepted on February 18, 2008

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