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A semiparametric test to detect associations between quantitative traits and candidate genes in structured populations
Author(s) -
Meijuan Li,
Cavan Reilly,
Timothy Hanson
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn455
Subject(s) - population stratification , population , statistics , candidate gene , covariate , trait , genetic association , parametric statistics , quantitative trait locus , mathematics , econometrics , biology , genetics , computer science , genotype , gene , demography , sociology , single nucleotide polymorphism , programming language
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.

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