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14 th QTL-MAS Workshop, Poznań University of Life Sciences, Poland 2010Partial least square regression applied to the QTL-MAS 2010 datasetAlbart Coster 1 , Mario P. L. Calus 2∗1Animal Breeding and Genomics Centre, Wageningen University, Wageningen,The Netherlands2Animal Breeding and Genomics Centre, Wageningen UR Livestock Research,Lelystad, The Netherlands∗ Presenting author: Mario Calus, email: mario.calus@wur.nlBackground. Partial least square regression (PLSR) was used to analyze the data of theQTL-MAS 2010 workshop. The objectives of this analysis were to identify genomic regionsaffecting either one of the two simulated traits and to accurately predict breeding values forthe two traits of the simulated individuals. PLSR was especially appropriate for the analysis ofthese data because it enables to simultaneously fit several traits to the large number ofmarkers in the data.Results. A preliminary analysis showed that the two simulated traits were phenotypically andgenetically correlated. Consequently, the data for the two traits were analyzed jointly in PLSRfor each chromosome independently. Regression coefficients estimated for the markers wereused to calculate the variance of each marker and QTL inference was based on local maximaof a smoothed line traced through these variances. In this way, 23 QTL for the continuoustrait were found and 30 for the discrete trait. There was clear evidence for pleiotropic QTL onchromosomes 1 and 3. Bootstrapping was used to calculate empirical standard errors of theregression coefficients and the 2000 most significant markers were used in a second PLSRmodel which was used to estimate breeding values of the individuals based on the markers.Breeding values estimated with this method correlated well (correlation > 0.50) with theobserved phenotypes of both traits.Conclusions. Results of this analysis show the viability of PLSR for QTL analysis ofmultivariate models and for estimating breeding values using markers. The methods used inthis study will need to be compared to the other methods used in this workshop.17