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This article is part of the supplement: Proceedings of the 14th European workshop on QTL mapping and marker assisted selection (QTL-MAS)

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Partial least square regression applied to the QTLMAS 2010 dataset

Albart Coster1* and Mario P L Calus2

Author Affiliations

1 Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands

2 Animal Breeding and Genomics Centre, Animal Science Group, Lelystad, The Netherlands

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BMC Proceedings 2011, 5(Suppl 3):S7  doi:10.1186/1753-6561-5-S3-S7

Published: 27 May 2011



Partial least square regression (PLSR) was used to analyze the data of the QTLMAS 2010 workshop to identify genomic regions affecting either one of the two traits and to estimate breeding values. PLSR was appropriate for these data because it enabled to simultaneously fit several traits to the markers.


A preliminary analysis showed phenotypic and genetic correlations between the two traits. Consequently, the data were analyzed jointly in a PLSR model for each chromosome independently. Regression coefficients for the markers were used to calculate the variance of each marker and inference of quantitative trait loci (QTL) was based on local maxima of a smoothed line traced through these variances. In this way, 25 QTL for the continuous trait and 22 for the discrete trait were found. There was evidence for pleiotropic QTL on chromosome 1. The 2000 most important markers were fitted in a second PLSR model to calculate breeding values of the individuals. The accuracies of these estimated breeding values ranged between 0.56 and 0.92.


Results showed the viability of PLSR for QTL analysis and estimating breeding values using markers.