The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.Keywords: genomic selection, breeding programme, dairy cattle, genetic gain, inbreeding rates ImplicationsThe practical use of genomic information to select animals, or genomic selection, is currently revolutionizing the organization of dairy cattle breeding schemes. The success of this new technology is because of increased genetic progress on both the bull and cow genetic pathways by reducing costs compared with conventional selection schemes, but also by the potential of using this rich source of information to manage genetic resources. Practical results of the implementation of genomic selection in dairy cattle breeding schemes are of great importa...

Section: Impact Of Genomic Selection On Selection Response and Its Vamentioning

confidence: 99%

Integrating genomic selection into dairy cattle breeding programmes: a review

Bouquet

Juga

2013

“…The DGV reliability decays over generations if RP is not updated (Meuwissen et al, 2001;Calus, 2010;Wolc et al, 2011), because the relationships between the EVA and the RP drop (Habier et al, 2007;Wolc et al, 2011;Pszczola et al, 2012a). Another explanation for such decline in the DGV reliability is the decay in linkage-disequilibrium (LD) between SNPs and quantitative trait loci (QTL) over generations.…”

Section: Introductionmentioning

confidence: 99%

Updating the reference population to achieve constant genomic prediction reliability across generations

Pszczoła

Calus

2016

The reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP ( n = 2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.

“…All FDR values were <0.1, and thus nearly all of these effects can be considered Genomic selection in dairy cattle 'real' effects, unless the SNP effect is due to confounding with the polygenic effect due to relationships, as demonstrated by Habier et al (2007). Surprisingly, with the exception of persistency, there is an inverse relationship between the trait heritability and the FDR values; the higher the trait heritability, the lower the number of genes with detectable effects.…”

Section: Resultsmentioning

confidence: 94%

A simple method for genomic selection of moderately sized dairy cattle populations

Weller

Maymon

Glick

et al. 2012

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a 'cow model,' estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.Keywords: genomic selection, quantitative trait loci, single nucleotide polymorphisms, dairy cattle breeding ImplicationsTwo algorithms for genomic selection of moderately sized populations are described, and applied to the analysis of milk, fat and protein production; somatic cell score (SCS); female fertility; persistency; and herd-life of Israeli Holsteins. Method II genomic evaluations were less biased and more accurate ...