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Gaussian Process Based Bayesian Semiparametric Quantitative Trait Loci Interval Mapping
Hanwen Huang, Haibo Zhou, Fuxia Cheng, Ina Hoeschele and Fei Zou
Vol. 66, No. 1 (MARCH 2010), pp. 222-232
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/40663170
Page Count: 11
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In linkage analysis, it is often necessary to include covariates such as age or weight to increase power or avoid spurious false positive findings. However, if a covariate term in the model is specified incorrectly (e.g., a quadratic term misspecified as a linear term), then the inclusion of the covariate may adversely affect power and accuracy of the identification of quantitative trait loci (QTL). Furthermore, some covariates may interact with each other in a complicated fashion. We implement semiparametric models for single and multiple QTL mapping. Both mapping methods include an unspecified function of any covariate found or suspected to have a more complex than linear but unknown relationship with the response variable. They also allow for interactions among different covariates. This analysis is performed in a Bayesian inference framework using Markov chain Monte Carlo. The advantages of our methods are demonstrated via extensive simulations and real data analysis.
Biometrics © 2010 International Biometric Society