A starting point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 add.cim.covar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 add.threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 addcovarint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 addint . ...

Since Lander and Botstein proposed the interval mapping method for QTL mapping data analysis in 1989, tremendous progress has been made in the last many years to advance new and powerful statistical methods for QTL analysis. Recent research progress has been focused on statistical methods and issues for mapping multiple QTL together. In this article, we review this progress. We focus the discus...

The problem of locating multiple interacting quantitative trait loci (QTL) can be addressed as a multiple regression problem, with marker genotypes being the regressor variables. An important and difficult part in fitting such a regression model is the estimation of the QTL number and respective interactions. Among the many model selection criteria that can be used to estimate the number of reg...

In 2003, Xu obtained remarkably precise estimates of QTL positions despite the many markers simultaneously in his Bayesian model. We extend his model and Gibbs algorithm to ensure a valid posterior distribution and convergence to it, without changing the attractiveness of the method.

The first and last names for the second and the fifth authors were inadvertently switched. The first name appears as the last name and the last name appears as the first name. Zhao Xinwang should be Xinwang Zhao and Tu Jinxing should be Jinxing Tu. The correct citation is: Bu SH, Zhao X, Yi C, Wen J, Tu J, Zhang YM (2015) Interacted QTL Mapping in Partial NCII Design Provides Evidences for Bree...

Most statistical methods for quantitative trait loci (QTL) mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While methods exist for QTL mapping with function-valued phenotypes,...

The contribution of size 3 and size 4 sibships to power in covariance structure modeling of a codominant QTL is investigated. Power calculations are based on the noncentral chi-square distribution. Sixteen sets of parameter values are considered. Results indicate that size 3 and size 4 sibships provided large increases in power over size 2 sibships. On average a size 3 (4) sibship is 3 (6 to 7)...