Gaussian Process Based Bayesian Semiparametric Quantitative Trait Loci Interval Mapping
Hanwen Huang,
Haibo Zhou,
Fuxia Cheng,
Ina Hoeschele,
Fei Zou,
Hanwen Huang
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Search for more papers by this authorHaibo Zhou
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Search for more papers by this authorFuxia Cheng
Department of Mathematics, Illinois State University, Normal, Illinois 61790, U.S.A.
Search for more papers by this authorIna Hoeschele
Virginia Bioinformatics Institute and Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061, U.S.A.
Search for more papers by this authorCorresponding Author
Fei Zou
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
email: [email protected]Search for more papers by this authorHanwen Huang,
Haibo Zhou,
Fuxia Cheng,
Ina Hoeschele,
Fei Zou,
Hanwen Huang
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Search for more papers by this authorHaibo Zhou
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Search for more papers by this authorFuxia Cheng
Department of Mathematics, Illinois State University, Normal, Illinois 61790, U.S.A.
Search for more papers by this authorIna Hoeschele
Virginia Bioinformatics Institute and Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061, U.S.A.
Search for more papers by this authorCorresponding Author
Fei Zou
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
email: [email protected]Search for more papers by this authorAbstract
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.
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