Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits

An approach to increase the efficiency of mapping quantitative trait loci ( QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD stores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher dete ction power, and better mapping resolution. However, the increased number o f parameters to be estimated complicates the application of this approach w hen a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contributions to the broad-sense heri tability based on interval-specific calculation of eigenvalues and eigenvec tors of the residual covariance matrix allows prediction of the expected QT L detection power and mapping resolution for an) subset of the initial mult ivariate trait complex. Permutation technique allows chromosome-wise testin g of significance for the whole trait complex and the significance of the c ontribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b . An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F-2/F-3 mapping population of te traploid wheat (Triticum durum X T. dicoccoides) is provided.