A multivariate approach to the problem of QTL localization

QTL mapping with statistical likelihood-based procedures or asymptotically equivalent regression methods is usually carried out in a univariate way, e ven if many traits were observed in the experiment. Some proposals for mult ivariate QTL mapping by an extension of the maximum likelihood method for m ixture models or by an application of the canonical transformation have bee n given in the literature. This paper describes a method of analysis of mul titrait data sets, aimed at localization of QTLs contributing to many trait s simultaneously, which is based on the linear model of multivariate multip le regression. A special form of the canonical analysis is employed to deco mpose the test statistic for the general no-QTL hypothesis into components pertaining to individual traits and individual, putative QTLs. Extended lin ear hypotheses are used to formulate conjectures concerning pleiotropy. A p ractical mapping algorithm is described. The theory is illustrated with the analysis of data from a study of maize drought resistance.