Optimizing genomic selection for scarcely recorded traits

Animal breeding aims to genetically improve animal populations by selecting the best individuals as parents of the next generation. New traits are being introduced to breeding goals to satisfy new demands faced by livestock production. Selecting for novel traits is especially challenging when recording is laborious and expensive and large scale recording is not possible. Genetic improvement of novel traits may be thus limited due to the small number of observations. New breeding tools, such as genomic selection, are therefore needed to enable the genetic improvement of novel traits. Using the limited available data optimally may, however, require alternative approaches and methodologies than currently used for conventional breeding goal traits. The overall objective of this thesis was to investigate different options for optimizing genomic selection for scarcely recorded novel traits. The investigated options were: (1) genotype imputation for ungenotyped but phenotyped animals to be used to enlarge the reference population; (2) optimization of the design of the reference population with respect to the relationships among the animals included in it; (3) prioritizing genotyping of the reference population or the selection candidates; and (4) using easily recordable predictor traits to improve the accuracy of breeding values for scarcely recorded traits. Results showed that: (1) including ungenotyped animals to the reference population can lead to a limited increase in the breeding values accuracy; (2) the reference population is designed optimally when the relationship within the reference are minimized and between reference population and potential selection candidates maximized; (3) the main gain in accuracy when moving from traditional to genomic selection is due to genotyping the selection candidates, but preferably both reference population and selection candidates should be genotyped; and (4) including the predictor traits in the analysis when it is recorded on both reference population and selection candidates can lead to a significant increase in the selection accuracy. The key factors for successful implementation of selection for a novel trait in a breeding scheme are: (1) maximizing accuracy of genotype prediction for ungenotyped animals to be used for updating the reference population; (2) optimizing the design of the reference population; (3) determining easy to record indicator traits that are also available on the selection candidates (4) developing large scale phenotyping techniques; and (5) establishing strategies and policies for increasing the engagement of farmers in the recording of novel traits.</p

Equivalent temperature for design of asphalt pavements in Poland

The paper presents application of equivalent temperature for design of asphalt pavements. The calculation methods of equivalent temperature: the Shell method, the French method, the methods that use the AASHTO and the Asphalt Institute fatigue criteria were presented. The results of calculations of equivalent temperature for Polish climatic conditions were presented and discussed. It was shown that different asphalt pavement design methods and utilization of Polish climatic data resulted in various calculated values of equivalent temperature that could be used in the design process

Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection

Background - For the XIV QTLMAS workshop, a dataset for traits with complex genetic architecture has been simulated and released for analyses by participants. One of the tasks was to estimate direct genomic values for individuals without phenotypes. The aim of this paper was to compare results of different approaches used by the participants to calculate direct genomic values for quantitative trait (QT) and binary trait (BT). Results - Participants applied 26 approaches for QT and 15 approaches for BT. Accuracy for QT was between 0.26 and 0.89 for males and between 0.31 and 0.89 for females, and for BT ranged from 0.27 to 0.85. For QT, percentage of lost response to selection varied from 8% to 83%, whereas for BT the loss was between 15% and 71%. Conclusions - Bayesian model averaging methods predicted breeding values slightly better than GBLUP in a simulated data set. The methods utilizing genomic information performed better than traditional pedigree based BLUP analyses. Bivariate analyses was slightly advantageous over single trait for the same method. None of the methods estimated the non-additivity of QTL affecting the QT, which may be one of the constrains in accuracy observed in real data. -------------------------------------------------------------------------------

Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods

Background: The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15 QTL-MAS workshop. Methods. Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion = 0.995 of SNPs assumed to have no effect, (ii) BayesC, where is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix. Results: BayesB, BayesC and GBLUP with various fitted models detected 6, 5, and 4 out of 8 simulated QTL, respectively. All five additive QTL were detected by Bayesian methods. When two QTL were in either coupling or repulsion phase, GBLUP only detected one of them and missed the other. In addition, GBLUP yielded more false positives. One imprinted QTL was detected by BayesB and GBLUP despite that only additive gene action was assumed. This QTL was missed by BayesC. None of the methods found two simulated additive-by-additive epistatic QTL. Variance components estimation correctly detected no evidence for dominance gene-action. Bayesian methods predicted additive genetic merit more accurately than GBLUP, and similar accuracies were observed between BayesB and BayesC. Conclusions: Bayesian methods and GBLUP mapped QTL to similar chromosome regions but Bayesian methods gave fewer false positives. Bayesian methods can be superior to GBLUP in GEBV prediction when genomic architecture is unknown