You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
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
You can always find the topics here!Topics: Quantitative trait loci, Genotypes, Simulations, Genetics, Linear regression, Quantitative traits, Genetic loci, Statistical variance, Phenotypic traits, Musical intervals
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
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