Abstract
A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.
Highlights
Genomic selection Hybrid Marker Assisted Selection (MAS) schemes were the first tool proposed to include information on a few main genes or quantitative trait loci (QTL) into best linear unbiased prediction (BLUP) of breeding values e.g. [1]
In the study by Pimentel et al [15], the correlation was 0.22 and the regression was 0.06 using a model with the variance of each SNP equal to residual variance, while the correlation was 0.51 and the regression was 0.31 using a model with the variance σ2G/NSNPs. These results clearly show that the importance of fitting the SNP effects as random effects and providing a reasonable SNP variance increases with the number of markers included in the models
The comparison of the different methods applied to the dataset by the workshop participants clearly shows a distinct clustering of the three approaches, where the Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance
Summary
Genomic selection Hybrid Marker Assisted Selection (MAS) schemes were the first tool proposed to include information on a few main genes or quantitative trait loci (QTL) into best linear unbiased prediction (BLUP) of breeding values e.g. [1]. Genomic selection Hybrid Marker Assisted Selection (MAS) schemes were the first tool proposed to include information on a few main genes or quantitative trait loci (QTL) into best linear unbiased prediction (BLUP) of breeding values e.g. Genomic selection (GS) [2] was proposed This approach relies on a genome-wide dense marker map, such that markers in linkage disequilibrium (LD) with each QTL are available. Because predictions using GS are based on marker associations and not pedigree information, the requirement to have phenotypes on selection candidates or their close relatives is relaxed and a breeding value can be obtained as soon as the genotypes are available. In breeding schemes with shorter generation intervals the genetic gain may be increased due to higher accuracies of breeding values at the time of selection. Bovine Bead Chip http://www.illumin.com), GS is becoming a very attractive approach to predict breeding values
Sign-in/Register to view highlights & summary 
Published Version (
Free)
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 call