title: Identifying QTL for multiple complex traits in experimental crosses.
abstract: In this chapter, we consider the problem of jointly analyzing multiple (correlated) complex traits in the context of identifying quantitative trait loci (QTL). The advantages of joint analysis (as opposed independent analysis) is the detection of pleiotropy and improved precision of estimates. The multivariate model is introduced along with a brief description of the setup. The model is evaluated in a Bayesian framework to perform model selection (strategy to identify QTL for each trait). A detailed vignette of a statistical software (R/qtlbim) which uses a Markov Chain Monte Carlo (MCMC) approach to draw samples from the posterior distribution is presented. Strategies of checking MCMC convergence, visualization of posterior samples, model building, and testing pleiotropy with the software are described. Relevant code to perform the analysis on an example (simulated) dataset is also provided.
authors.affiliation: Division of Biostatistics and Epidemiology, Department of Public Health, Weill Cornell Medical College, New York, NY, USA. sab2028@med.cornell.edu
authors.forename: Samprit
authors.initials: S
authors.lastname: Banerjee
authors.forename: Nengjun
authors.initials: N
authors.lastname: Yi
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003433:Crosses, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci / Q000235:genetics*; D012984:Software