R.F. Veerkamp

Accuracy of Genomic Selection Using Different Methods to Define Haplotypes

Genomic selection uses total breeding values for juvenile animals, predicted from a large number of estimated marker haplotype effects across the whole genome. In this study the accuracy of predicting breeding values is compared for four different models including a large number of markers, at different marker densities for traits with heritabilities of 50 and 10%. The models estimated the effect of (1) each single-marker allele [single-nucleotide polymorphism (SNP)1], (2) haplotypes constructed from two adjacent marker alleles (SNP2), and (3) haplotypes constructed from 2 or 10 markers, including the covariance between haplotypes by combining linkage disequilibrium and linkage analysis (HAP_IBD2 and HAP_IBD10). Between 119 and 2343 polymorphic SNPs were simulated on a 3-M genome. For the trait with a heritability of 10%, the differences between models were small and none of them yielded the highest accuracies across all marker densities. For the trait with a heritability of 50%, the HAP_IBD10 model yielded the highest accuracies of estimated total breeding values for juvenile and phenotyped animals at all marker densities. It was concluded that genomic selection is considerably more accurate than traditional selection, especially for a low-heritability trait.

Effects of genetic selection for milk yield on energy balance, levels of hormones, and metabolites in lactating cattle, and possible links to reduced fertility ☆

Selection for a higher milk yield increases metabolic load via a higher yield per se and/or via physiological processes that facilitate milk yield, and it is difficult to differentiate between these two. Here, we aim to identify important pathways that contribute to the reduction in fertility following selection for higher yield. The associations between milk yield and fertility may run via pleiotropic effects, i.e. via functional pathways (for example related to intake), or linkage of genes and may involve changes in levels of hormones and metabolites. A number of studies have investigated the effects of genetic merit for milk yield on fertility, feed intake, energy balance and levels of metabolic and fertility hormones or metabolites. Differences in genetic merit were associated with differences in: (1) feed intake; (2) energy balance; and (3) plasma levels especially of GH, IGF-I, prolactin, progesterone, insulin, glucose, NEFAs and ketones. In the discussion we focus on the possible roles that energy balance, the growth hormone axis, and glucose together with insulin may have in the reduced fertility that is associated with high yield. The overall conclusion is that many minor pathways probably contribute, but that reduced metabolic fuel availability, rather than direct effects of hormone concentrations, is an important cause of poorer fertility with increasing genetic merit.

Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection.

Mitigation of enteric methane (CH₄) emission in ruminants has become an important area of research because accumulation of CH₄ is linked to global warming. Nutritional and microbial opportunities to reduce CH₄ emissions have been extensively researched, but little is known about using natural variation to breed animals with lower CH₄ yield. Measuring CH₄ emission rates directly from animals is difficult and hinders direct selection on reduced CH₄ emission. However, improvements can be made through selection on associated traits (e.g., residual feed intake, RFI) or through selection on CH₄ predicted from feed intake and diet composition. The objective was to establish phenotypic and genetic variation in predicted CH₄ output, and to determine the potential of genetics to reduce methane emissions in dairy cattle. Experimental data were used and records on daily feed intake, weekly body weights, and weekly milk production were available from 548 heifers. Residual feed intake (MJ/d) is the difference between net energy intake and calculated net energy requirements for maintenance as a function of body weight and for fat- and protein-corrected milk production. Predicted methane emission (PME; g/d) is 6% of gross energy intake (Intergovernmental Panel on Climate Change methodology) corrected for energy content of methane (55.65 kJ/g). The estimated heritabilities for PME and RFI were 0.35 and 0.40, respectively. The positive genetic correlation between RFI and PME indicated that cows with lower RFI have lower PME (estimates ranging from 0.18 to 0.84). Hence, it is possible to decrease the methane production of a cow by selecting more-efficient cows, and the genetic variation suggests that reductions in the order of 11 to 26% in 10 yr are theoretically possible, and could be even higher in a genomic selection program. However, several uncertainties are discussed; for example, the lack of true methane measurements (and the key assumption that methane produced per unit feed is not affected by RFI level), as well as the limitations of predicting the biological consequences of selection. To overcome these limitations, an international effort is required to bring together data on feed intake and methane emissions of dairy cows.

Genome-wide associations for fertility traits in Holstein-Friesian dairy cows using data from experimental research herds in four European countries

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein-Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all <0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.

Leptin gene polymorphisms and their phenotypic associations

In an era of rapidly increasing prevalence of human obesity and associated health problems, leptin gene polymorphisms have drawn much attention in biomedical research. Leptin gene polymorphisms have furthermore drawn much attention from animal scientists for their possible roles in economically important production and reproduction traits. Of the polymorphisms reported for exonic, intronic, and promoter regions of the leptin gene, 16 have been included in association studies in humans, 19 in cattle, and 6 (all exonic or intronic) in pigs. In humans, associations have been found with overweight or (early-onset) obesity, non-insulin-dependent diabetes mellitus, prostate cancer, and non-Hodgkins lymphoma. In cattle, associations have been found with feed intake, milk yield traits, carcass traits, and reproduction-related traits, and in pigs with feed intake, average daily gain, carcass traits (backfat/leanness), and reproduction performance traits. Many of the polymorphisms were only included in a limited number of association studies, or the phenotypes studied varied largely for a given polymorphism between studies. Therefore, many of the associations found for these polymorphisms need to be confirmed in future studies before firm conclusions can be drawn.

Genetic parameters of pathogen-specific incidence of clinical mastitis in dairy cows

The objective of this study was to estimate heritabilities for and genetic correlations among different pathogen-specific mastitis traits. The traits were unspecific mastitis, which is all mastitis treatments regardless of the causative pathogen as well as mastitis caused by Streptococcus dysgalactiae, Escherichia coli, coagulase-negative staphylococci (CNS), Staphylococcus aureus and Streptococcus uberis. Also groups of pathogens were investigated, Gram-negative v. Gram-positive and contagious v. environmental pathogens. Data from 168 158 Danish Holstein cows calving first time between 1998 and 2006 were used in the analyses. Variances and covariances were estimated using uni- and bivariate threshold models via Gibbs sampling. Posterior means of heritabilities of pathogen-specific mastitis were lower than the heritability of unspecific mastitis, ranging from 0.035 to 0.076 for S. aureus and S. uberis, respectively. The heritabilities of groups of pathogen ranged from 0.053 to 0.087. Genetic correlations among the pathogen-specific mastitis traits ranged from 0.45 to 0.77. These estimates tended to be lowest for bacteria eliciting very different immune responses, which can be considered as the overall pleiotropic effect of genes affecting resistance to a specific pathogen, and highest for bacteria sharing characteristics regarding immune response. The genetic correlations between the groups of pathogens were high, 0.73 and 0.83. Results showed that the pathogen-specific traits used in this study should be considered as different traits. Genetic evaluation for pathogen-specific mastitis resistance may be beneficial despite lower heritabilities than unspecific mastitis because a pathogen-specific mastitis trait is a direct measure of an udder infection, and because the cost of a mastitis case caused by different pathogens has been shown to differ greatly. Sampling bias may be present because there were not pathogen information on all mastitis treatments and because some farms do not record pathogen information. Therefore, improved recording of pathogen information and mastitis treatments in general is critical for a successful genetic evaluation of udder health. Also, economic values have to be specified for each pathogen-specific trait separately.

Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped

Genetic parameters were estimated using relationships between animals that were based either on pedigree, 43,011 single nucleotide polymorphisms, or a combination of these, considering genotyped and non-genotyped animals. The standard error of the estimates and a parametric bootstrapping procedure was used to investigate sampling properties of the estimated variance components. The data set contained milk yield, dry matter intake and body weight for 517 first-lactation heifers with genotypes and phenotypes, and another 112 heifers with phenotypes only. Multivariate models were fitted using the different relationships in ASReml software. Estimates of genetic variance were lower based on genomic relationships than using pedigree relationships. Genetic variances from genomic and pedigree relationships were, however, not directly comparable because they apply to different base populations. Standard errors indicated that using the genomic relationships gave more accurate estimates of heritability but equally accurate estimates of genetic correlation. However, the estimates of standard errors were affected by the differences in scale between the 2 relationship matrices, causing differences in values of the genetic parameters. The bootstrapping results (with genetic parameters at the same level), confirmed that both heritability and genetic correlations were estimated more accurately with genomic relationships in comparison with using the pedigree relationships. Animals without genotype were included in the analysis by merging genomic and pedigree relationships. This allowed all phenotypes to be used, including those from non-genotyped animals. This combination of genomic and pedigree relationships gave the most accurate estimates of genetic variance. When a small data set is available it might be more advantageous for the estimation of genetic parameters to genotype existing animals, rather than collecting more phenotypes.

Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information.

The objective of this study was to investigate the genetic basis of energy balance (EB) and the potential use of genomic selection to enable EB to be incorporated into selection programs. Energy balance provides an essential link between production and nonproduction traits because both depend on a common source of energy. A small number (527) of Dutch Holstein-Friesian heifers with phenotypes for EB were genotyped. Direct genomic values were predicted for these heifers using a model that included the genotypic information. A polygenic model was also applied to predict estimated breeding values using only pedigree information. A 10-fold cross-validation approach was employed to assess the accuracies of the 2 sets of predicted breeding values by correlating them with phenotypes. Because of the small number of phenotypes, accuracies were relatively low (0.29 for the direct genomic values and 0.21 for the estimated breeding values), where the maximum possible accuracy was the square root of heritability (0.57). Despite this, the genomic model produced breeding values with reliability double that of the breeding values produced by the polygenic model. To increase the accuracy of the genomic breeding values and make it possible to select for EB, measurement and recording of EB would need to improve. The study suggests that it may be possible to select for minimally recorded traits; for instance, those measured on experimental farms, using genomic selection. Overall, the study demonstrated that genomic selection could be used to select for EB, confirming its genetic background.

Predicted accuracy of and response to genomic selection for new traits in dairy cattle.

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.

Estimation of genotype×environment interactions, in a grass-based system, for milk yield, body condition score, and body weight using random regression models

Abstract (Co)variance components for milk yield, body condition score (BCS), body weight (BW), BCS change and BW change over different herd-year mean milk yields (HMY) and nutritional environments (concentrate feeding level, grazing severity and silage quality) were estimated using a random regression model. The data analysed included records from 7478 multiparous upgraded Holstein–Friesian dairy cows. There were G×E interactions for BCS across all environments and for BW change across different concentrate levels and silage quality environments. There was a three-fold increase in the genetic standard deviation (S.D.) for BCS change to day 60 of lactation (CS60-5) and a doubling of the genetic S.D. for BCS at day 5 (CS5) as silage quality improved. The genetic variance for CS60-5 increased as concentrate level increased and as grazing severity became tighter. There was significant re-ranking of animals for milk yield, CS5 and CS60-5 over the different HMY environments; genetic correlations fell to −0.60 between extreme HMY environments for CS60-5 and were as low as 0.41 for CS5 across different HMY environments.