Abstract
In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop. Five procedures were considered and tested in a reference population, i.e., the first four generations, from which phenotypes and genotypes were available. The procedures can be interpreted as variants of ridge regression, with different ways for defining the shrinkage parameter. Comparisons were made with respect to the correlation between genomic and conventional estimated breeding values. Moderate correlations were obtained from all methods. Two of them were used to predict genomic breeding values in the last three generations. Correlations between these and the true breeding values were also moderate. We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values.
Highlights
The development of appropriate methods to detect a large number of DNA sequence variations in the genome has launched a series of studies [1,2] attempting to associate such alterations with phenotypic variation in complex traits
High-density panels for genotyping thousands of single nucleotide polymorphisms (SNP) are commercially available and their costs are likely to decrease over time
If the number of markers in such a panel is large enough that it covers the entire genome, it may be assumed that most of the quantitative trait loci (QTL) associated with a given trait will be in linkage disequilibrium with at least some of these markers
Summary
The development of appropriate methods to detect a large number of DNA sequence variations in the genome has launched a series of studies [1,2] attempting to associate such alterations with phenotypic variation in complex traits. Μ is an overall mean; Statistical procedures If the number of markers is greater than the number of genotyped animals, ordinary or weighted least squares cannot be used to estimate the regression coefficients, unless some variable selection strategy is adopted, which may lead to unsatisfactory results [1]. This lack of degrees of freedom can be overcome if SNP genotype is treated as a random effect and mixed model methodology is employed to obtain best linear unbiased prediction (BLUP) of SNP effects. In this method equal variances were assumed for all segments and the ratio of the residual to the segment variances was assumed to be 1, regardless of the heritability of the trait
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
