Strategies and tools for genetic selection in dairy cattle and their application to improving animal welfare

Genetic improvement of farm animals, especially selection within breeds focussed on high production and efficiency, is often cited as a potential threat to animal welfare. However, many animal welfare issues can be addressed, at least partially, by animal breeding and genetics. In this chapter, we explore the relationship between genetic selection and animal welfare, the strategies and tools for genetic improvement and how they can contribute to improved animal welfare. A growing public awareness of animal welfare and environmental issues has led to breeding goals being broadened beyond farmer profitability. As animal welfare and behaviour are complex and multi-factorial, so the emergence of selection indices that include a large number of traits to optimise animal welfare in a way that is consistent with enterprise sustainability for the farmer is necessary. This trend is likely to continue and will be aided by the advent of new technologies for measuring animal welfare in conjunction with DNA-based predictions of genetic merit (genomic selection). The dairy cattle industry has been exemplary for the application of genomic selection, in addition to enabling selection decisions to be made earlier in life, it can be used to select for traits where it was not possible to select for previously. These include important welfare-related traits, such as improved disease resistance and heat tolerance. Dairy cattle breeding is a very international activity with just a few breeding companies dominating the market in semen for the most numerous breeds, especially the Holstein. Consequently, genetic diversity within breeds is diminishing and although genetic gain has been significant, the rate of inbreeding now presents itself as a threat to the future success of breeding programmes. A greater emphasis on diversity in breeding programmes and the traits under selection is needed as major themes in research and application. Innovation in methods to measure these new traits, (e.g. molecular phenotyping, sensor development, digitalisation data science, etc.) could dramatically transform selection for animal welfare, as these technologies can enable large-scale objective measurements of animal behaviours. In addition to animal-based outcome measures, factors like housing, feeding, specific management practices pose other risks to welfare. Risk factors and their interactions have an impact on the development of diseases or other challenges to welfare. Collaborative efforts between animal behaviour scientists, geneticists, engineers, data scientists, and others will potentially provide solutions to these challenges

The repeatability and heritability of traits derived from accelerometer sensors associated with grazing and rumination time in an extensive sheep farming system

IntroductionThe automated collection of phenotypic measurements in livestock is of interest to both researchers and farmers. Real-time, low-cost, and accurate phenotyping can enhance precision livestock management and could lead to the optimized utilization of pasture and breeding of efficient animals. Wearable sensors provide the tools for researchers to develop novel phenotypes across all production systems, which is especially valuable for grazing conditions. The objectives of this study were to estimate the repeatability and heritability of traits related to grazing and rumination activities and their correlations with other traits.MethodsThis study was conducted on a commercial Merino farm in the west of Victoria, Australia, from 4 May 2020 to 29 May 2020. A total of 160 ActiGraph sensors embedded in halters were attached to the left side of the muzzles of Merino sheep (M = 74, F = 86) aged 10–11 months while the sheep were grazing on pasture. Support vector machine (SVM) algorithms classified the sensor output into the categories of grazing, rumination, walking, idle, and other activities. These activities were further classified into daily grazing time (GT), number of grazing events (NGE), grazing length (GL), rumination time (RT), number of rumination events (NRE), rumination length (RL), walking time (WT), and idle time (IT). The data were analyzed using univariate and bivariate models in ASReml-SA to estimate the repeatability, heritability, and phenotypic correlations among traits.ResultsThe heritability of GT was estimated to be 0.44 ± 0.23, whereas the other traits had heritability estimates close to zero. The estimated repeatability for all traits was moderate to high, with the highest estimate being for GT (0.70 ± 0.03) and the lowest for RT (0.44 ± 0.03). The intraclass correlation or repeatability at a 1-day interval (i.e., 2 consecutive days) was high for all traits, and steadily reduced when the interval between measurements was longer than 1 week.DiscussionThe estimated repeatability for the grazing traits showed that wearable sensors and SVM methods are reliable methods for recording sheep activities on pasture, and have a potential application in the ranking of animals for selective breeding

The use of mid-infrared spectra to map genes affecting milk composition.

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL

Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle

The current research was carried out to determine the associations between the rumen microbiota and traits related with feed efficiency in a Holstein cattle population (n = 30) using whole metagenome sequencing. Improving feed efficiency (FE) is important for a more sustainable livestock production. The variability for the efficiency of feed utilization in ruminants is partially controlled by the gastrointestinal microbiota. Modulating the microbiota composition can promote a more sustainable and efficient livestock. This study revealed that most efficient cows had larger relative abundance of Bacteroidetes (P = 0.041) and Prevotella (P = 0.003), while lower, but non-significant (P = 0.119), relative abundance of Firmicutes. Methanobacteria (P = 0.004) and Methanobrevibacter (P = 0.003) were also less abundant in the high-efficiency cows. A de novo metagenome assembly was carried out using de Bruijn graphs in MEGAHIT resulting in 496,375 contigs. An agnostic pre-selection of microbial contigs allowed high classification accuracy for FE and intake levels using hierarchical classification. These microbial contigs were also able to predict FE and intake levels with accuracy of 0.19 and 0.39, respectively, in an independent population (n = 31). Nonetheless, a larger potential accuracy up to 0.69 was foreseen in this study for datasets that allowed a larger statistical power. Enrichment analyses showed that genes within these contigs were mainly involved in fatty acids and cellulose degradation pathways. The findings indicated that there are differences between the microbiota compositions of high and low-efficiency animals both at the taxonomical and gene levels. These differences are even more evident in terms of intake levels. Some of these differences remain even between populations under different diets and environments, and can provide information on the feed utilization performance without information on the individual intake level.info:eu-repo/semantics/publishedVersio

Evaluating the potential impact of selection for the A2 milk allele on inbreeding and performance in Australian Holstein cattle

Selection decisions are generally based on estimated breeding values (EBV) for a combination of traits that are polygenic (e.g. milk production). However, in some cases, there is additional intense selection for a single allele, or SNP, for a perceived benefit, such as selection for polled or A2 milk. Using a dataset where the A2 mutation was imputed using a reference population with whole genome sequence, we tested the hypothesis that intense selection in Australian Holstein cattle for the A2 allele in the β-casein gene may have resulted in increased inbreeding. We also estimated the average difference in performance between animals homozygous for the A1 or A2 allele for a range of traits. Using high-density genotypes we compared differences in genome-wide and regional inbreeding between Holstein cows homozygous for either the A1 or A2 β-casein alleles i.e. A1/A1 or A2/A2. This study shows that between the years 2000 to 2017, the frequency of the A2/A2 genotype increased by 20% in Holstein cows (from 32% to 52%). Our results suggest that selection for homozygosity at the β-casein A2 allele has increased inbreeding both across the genome and on chromosome 6 in A2/A2 Holstein cows. Animals that were A2/A2 were twice as likely to have a run of homozygosity of at least 1Mb long across the β-casein locus compared to animals that were A1/A1. Cows that are homozygous for the A2 allele had an average protein yield EBV advantage of 0.24 genetic standard deviations (SD) compared to A1/A1 homozygous cows. In contrast, A2/A2 homozygous animals were on average 0.2 genetic SD inferior on fertility EBV. As a result, the difference in the overall economic index (that includes traits contributing to profitability) there was only a small advantage of 0.05 SD for A2/A2 cows compared to A1/A1 cows. However, strong selection for the A2 allele has likely led to a higher level of regional and overall inbreeding which in the long term could harm genetic progress for some or all economic traits. Therefore, applying approaches that mitigate rapid inbreeding while selecting for preferred alleles and quantitative traits may be desirable

The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO₂-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits

Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits

Background: Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. Results: Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. Conclusions: In both breeds, dominance effects were significant (

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.

BACKGROUND Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. RESULTS GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. CONCLUSIONS The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

peer reviewedKnowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points