Multiple-trait multiple-interval mapping of quantitative-trait loci

Abstract

QTL (quantitative-trait locus) analysis aims to locate and estimate the effects of genes that are responsible for quantitative traits, such as grain protein content and yield, by means of statistical methods that evaluate the association of genetic variation with trait (phenotypic) variation. Quantitative traits are typically polygenic, i.e., controlled by multiple genes, with varying degrees of in uence on the phenotype. Several methods have been developed to increase the accuracy of QTL location and effect estimates. One of them, multiple interval mapping (MIM) (Kao et al. 1999), has been shown to be more accurate than conventional methods such as composite interval mapping (CIM) (Zeng 1994). Other QTL analysis methods have been developed to perform additional analyses that might be useful for breeders, such as of pleiotropy and QTL-by-environment (QxE) interaction. It has been shown (Jiang and Zeng 1995) that these analyses can be carried out with a multivariate extension of CIM (MT-CIM) that exploits the correlation structure in a set of traits. In doing so, this method also improves the accuracy of QTL location detection. This thesis describes the multivariate extension of MIM (MT-MIM) using ideas from MT-CIM. The development of additional multivariate tests, such as of pleiotropy and QxE interaction, and several methods pertinent to the development of MT-MIM are also described. A small simulation study shows that MT-MIM is more accurate than MT-CIM and univariate MIM. Results for real data show that MT-MIM is able to provide a more accurate and precise estimate of QTL location.