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F. Mora, A.I. Santos, and C.A. Scapim. 2008. Mappin g quantitative trait loci (QTLs) usin ga multivariate approach. Cien. Inv. Agr. 35(2):137-145. Statistical procedures for mappingquantitative trait loci (QTL) have been extensively studied because they are the essential for improving the accuracy of genetic analyses. The ob jective of the present study was to examineQTL using multivariate methods, considering the principle of simple interval mapping. A microsatellite marker data set from a F 2 population was simulated. It was assumed that theQTL control binomial and normal traits. In the normal case,  ve QTL mapping models wereevaluated that had the following residual covariance structures: variance components (VC),compound s ymmetry (CS), unstructured (UN), banded main diagonal (UN1) and heterogeneoustoeplitz. Akaike’s information criterion (AIC) was used to select the appropriate structure. Inthe binar y case, the Generalized Estimating Equations (GEE) procedure was used. UN structureminimized the AIC value on the interval that indicated a higher probability of the QTL. In thebinomial case, a non-independent working correlation matrix (WCM) was  tted (ρ = 0.47). Inboth cases, the additive effect of QTL was signi  cant (p 0.05). Thus, QTL mapping using a multivariate approach ma y be a useful tool for breedingprograms that aim to improve quantitative traits that have phenotypic values that change over time.

Mapeo de loci de caracteres cuantitativos (QTL) usando un enfoque multivariado