Bayesian QTL Mapping for Multiple Traits

@article{citeulike:3106575, abstract = {Most QTL mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genome-wide mapping strategy for correlated traits in the literature. We develop Bayesian multiple QTL mapping methods for correlated traits using two multivariate models; one which assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the Seemingly Unrelated Regression (SUR) model allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single trait model. Our methods have been implemented in the freely available package R/qtlbim (www.qtlbim.org), which greatly facilitate the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits. 10.1534/genetics.108.088427}, author = {Banerjee, Samprit and Yandell, Brian S. and Yi, Nengjun }, citeulike-article-id = {3106575}, doi = {http://dx.doi.org/10.1534/genetics.108.088427}, journal = {Genetics}, keywords = {bayes, qtl, r}, month = {August}, pages = {genetics.108.088427+}, posted-at = {2008-08-16 10:09:31}, priority = {2}, title = {Bayesian QTL Mapping for Multiple Traits}, url = {http://dx.doi.org/10.1534/genetics.108.088427}, year = {2008} }

TY - JOUR ID - citeulike:3106575 L3 - citeulike-article-id:3106575 N2 - Most QTL mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genome-wide mapping strategy for correlated traits in the literature. We develop Bayesian multiple QTL mapping methods for correlated traits using two multivariate models; one which assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the Seemingly Unrelated Regression (SUR) model allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single trait model. Our methods have been implemented in the freely available package R/qtlbim (www.qtlbim.org), which greatly facilitate the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits. 10.1534/genetics.108.088427 JF - Genetics TI - Bayesian QTL Mapping for Multiple Traits SP - genetics.108.088427 KW - bayes KW - qtl KW - r AU - Banerjee, Samprit AU - Yandell, Brian AU - Yi, Nengjun PY - 2008/08/09/ UR - http://dx.doi.org/10.1534/genetics.108.088427 ER -