Journal Article

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

Meijuan Li, Cavan Reilly and Timothy Hanson

in Bioinformatics

Volume 24, issue 20, pages 2356-2362
Published in print October 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI:
A semiparametric test to detect associations between quantitative traits and candidate genes in structured populations

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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 family-based 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 datasets.

Results: In this article, we proposed a new structured association (SA) 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 SA tests in terms of model fit, type I error rate, power, precision and accuracy by application to a real dataset as well as simulated datasets.


Journal Article.  6442 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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