Partial least square regression applied to the QTLMAS 2010 dataset
Detection of genomic regions affecting traits is a goal in many genetic studies. Studies applying distinct methods for detection of these regions, called quantitative trait loci (QTL), have been described, ranging from single marker regression [1] to methods that enable to fit several markers simultaneously [2,3]. Simultaneously fitting all markers leads to more accurate detection of QTL compared to independent fitting of single markers in a regression model when there is linkage disequilibrium (LD) between the genomic regions that affect the trait but comes at the cost of increased computational requirements [2]. Partial least square regression (PLSR) is one method for simultaneously fitting multiple markers and was applied by Bjornstad et al. for detection of QTL [3]. An interesting characteristic of PLSR its straightforward application of to simultaneous analysis of data of multiple traits [3]. The objectives of this study were to use PLSR to search for QTL and to estimate breeding values in the dataset of the QTLMAS 2010 workshop
| Main Authors: | , |
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| Format: | Article in monograph or in proceedings biblioteca |
| Language: | English |
| Subjects: | Life Science, |
| Online Access: | https://research.wur.nl/en/publications/partial-least-square-regression-applied-to-the-qtlmas-2010-datase |
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