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Gaussian Process Based Bayesian Semiparametric Quantitative Trait Loci Interval Mapping
Author(s) -
Huang Hanwen,
Zhou Haibo,
Cheng Fuxia,
Hoeschele Ina,
Zou Fei
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01268.x
Subject(s) - bayesian probability , gaussian process , interval (graph theory) , computer science , trait , credible interval , semiparametric model , mathematics , process (computing) , quantitative trait locus , statistics , gaussian , artificial intelligence , econometrics , biology , nonparametric statistics , genetics , combinatorics , physics , quantum mechanics , gene , programming language , operating system
Summary 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.