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Bioinformatics Advance Access published online on August 27, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn455
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A semiparametric test to detect associations between quantitative traits and candidate genes in structured populations

Meijuan Li *, Cavan Reilly and Timothy Hanson

Division of Biostatistics, School of Public Health,University of Minnesota, A460 Mayo Building MMC 303, Minneapolis, MN 55455-0378, USA

*To whom correspondence should be addressed. Dr. Meijuan Li, E-mail: meijuanl{at}biostat.umn.edu


  Abstract

Motivation: Although population-based association mapping may be subject to the bias caused by population stratification, alternative methods that are robust to population stratification such as familybased linkage analysis have lower mapping resolution. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate genes and traits of interest. The association between a candidate gene and a quantitative trait is often evaluated via a regression model with inferred population structure variables as covariates, where the residual distribution is customarily assumed to be from a symmetric and unimodal parametric family, such as a Gaussian, although this may be inappropriate for the analysis of many real-life data sets.

Results: In this paper, we proposed a new structured association test. Our method corrects for continuous population stratification by first deriving population structure and kinship matrices through a set of random genetic markers and then modeling the relationship between trait values, genotypic scores at a candidate marker, and genetic background variables through a semiparametric model, where the error distribution is modeled as a mixture of Polya trees centered around a normal family of distributions. We compared our model to the existing structured association tests in terms of model fit, type I error rate, power, precision, and accuracy by application to a real data set as well as simulated data sets.

Contact: meijuanl{at}biostat.umn.edu

Associate Editor: Prof Martin Bishop

Received on April 28, 2008; revised on August 20, 2008; accepted on August 21, 2008

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