Genetics and genomics of reproductive performance in dairy and beef cattle

Milk Production and Fertility in Cattle

Evolutionary biology provides reasons for why the intensive selection for milk production reduces reproductive success rates. There is considerable exploitable genetic variation in reproductive performance in both dairy and beef cattle, and examination of national genetic trends demonstrates that genetic gain for both reproductive performance and milk production is possible in a well-structured breeding program. Reproductive failure is often postulated to be a consequence of the greater negative energy balance associated with the genetic selection for increased milk production. However, experimental results indicate that the majority of the decline in reproductive performance cannot be attributed to early lactation energy balance, per se; reproductive success will, therefore, not be greatly improved by nutritional interventions aimed at reducing the extent of negative energy balance. Modeling can aid in better pinpointing the key physiological components governing reproductive success and, also, the impact of individual improvements on overall fertility, helping to prioritize variables for inclusion in breeding programs.

Selective breeding: the future of TB management in African buffalo?

The high prevalence of bovine tuberculosis (BTB) in African buffalo (Syncerus caffer) in regions of southern African has a negative economic impact on the trade of animals and animal products, represents an ecological threat to biodiversity, and poses a health risk to local communities through the wildlife-cattle-human interface. Test and cull methods may not be logistically feasible in many free-range wildlife systems, and with the presence of co-existing BTB hosts and the limited effectiveness of the BCG vaccine in buffalo, there is a need for alternative methods of BTB management. Selective breeding for increased resistance to BTB in buffalo may be a viable method of BTB management in the future, particularly if genetic information can be incorporated into these schemes. To explore this possibility, we discuss the different strategies that can be employed in selective breeding programmes, and consider the implementation of genetic improvement schemes. We reflect on the suitability of applying this strategy for enhanced BTB resistance in African buffalo, and address the challenges of this approach that must be taken into account. Conclusions and the implications for management are presented.

Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows

BACKGROUND

It has been suggested that traits with low heritability, such as fertility, may have proportionately more genetic variation arising from non-additive effects than traits with higher heritability, such as milk yield. Here, we performed a large genome scan with 408,255 single nucleotide polymorphism (SNP) markers to identify chromosomal regions associated with additive, dominance and epistatic (pairwise additive × additive) variability in milk yield and a measure of fertility, calving interval, using records from a population of 7,055 Holstein cows. The results were subsequently validated in an independent set of 3,795 Jerseys.

RESULTS

We identified genomic regions with validated additive effects on milk yield on Bos taurus autosomes (BTA) 5, 14 and 20, whereas SNPs with suggestive additive effects on fertility were observed on BTA 5, 9, 11, 18, 22, 27, 29 and the X chromosome. We also confirmed genome regions with suggestive dominance effects for milk yield (BTA 2, 3, 5, 26 and 27) and for fertility (BTA 1, 2, 3, 7, 23, 25 and 28). A number of significant epistatic effects for milk yield on BTA 14 were found across breeds. However on close inspection, these were likely to be associated with the mutation in the diacylglycerol O-acyltransferase 1 (DGAT1) gene, given that the associations were no longer significant when the additive effect of the DGAT1 mutation was included in the epistatic model.

CONCLUSIONS

In general, we observed a low statistical power (high false discovery rates and small number of significant SNPs) for non-additive genetic effects compared with additive effects for both traits which could be an artefact of higher dependence on linkage disequilibrium between markers and causative mutations or smaller size of non-additive effects relative to additive effects. The results of our study suggest that individual non-additive effects make a small contribution to the genetic variation of milk yield and fertility. Although we found no individual mutation with large dominance effect for both traits under investigation, a contribution to genetic variance is still possible from a large number of small dominance effects, so methods that simultaneously incorporate genotypes across all loci are suggested to test the variance explained by dominance gene actions.

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle

Background

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation.

Methods

Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs.

Results

The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance.

Conclusions

Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.

Genetic parameters of ovarian and uterine reproductive traits in dairy cows

The objective of the study was to estimate genetic parameters of detailed reproductive traits derived from ultrasound examination of the reproductive tract as well as their genetic correlations with traditional reproductive traits. A total of 226,141 calving and insemination records as well as 74,134 ultrasound records from Irish dairy cows were used. Traditional reproductive traits included postpartum interval to first service, conception, and next calving, as well as the interval from first to last service; number of inseminations, pregnancy rate to first service, pregnant within 42 d of the herd breeding season, and submission in the first 21 d of the herd breeding season were also available. Detailed reproductive traits included resumed cyclicity at the time of ultrasound examination, incidence of multiple ovulations, incidence of early postpartum ovulation, heat detection, ovarian cystic structures, embryo loss, and uterine score; the latter was a subjectively assessed on a scale of 1 (little fluid with normal uterine tone) to 4 (large quantity of fluid with a flaccid uterine tone). Variance (and covariance) components were estimated using repeatability animal linear mixed models. Heritability for all reproductive traits were generally low (0.001-0.05), with the exception of traits related to cyclicity postpartum, regardless if defined traditionally (0.07; calving to first service) or from ultrasound examination [resumed cyclicity at the time of examination (0.07) or early postpartum ovulation (0.10)]. The genetic correlations among the detailed reproductive traits were generally favorable. The exception was the genetic correlation (0.29) between resumed cyclicity and uterine score; superior genetic merit for cyclicity postpartum was associated with inferior uterine score. Superior genetic merit for most traditional reproductive traits was associated with superior genetic merit for resumed cyclicity (genetic correlations ranged from -0.59 to -0.36 and from 0.56 to 0.70) and uterine score (genetic correlations ranged from -0.47 to 0.32 and from 0.25 to 0.52). Genetic predisposition to an increased incidence of embryo loss was associated with both an inferior uterine score (0.24) and inferior genetic merit for traditional reproductive traits (genetic correlations ranged from -0.52 to -0.42 and from 0.33 to 0.80). The results from the present study indicate that selection based on traditional reproductive traits, such as calving interval or days open, resulted in improved genetic merit of all the detailed reproductive traits evaluated in this study. Additionally, greater accuracy of selection for calving interval is expected for a relatively small progeny group size when detailed reproductive traits are included in a multitrait genetic evaluation.

Factors associated with the differential in actual gestational age and gestational age predicted from transrectal ultrasonography in pregnant dairy cows

The objective of the study was to determine (1) how gestational age predicted using transrectal ultrasonography related to actual gestational age derived as the number of days from the most recent artificial insemination date, (2) what factors, if any, were associated with the differential between the two measures, and (3) the association between this differential in gestational age and the likelihood of subsequent pregnancy loss, stillbirth, or calving dystocia. The data set contained 7340 ultrasound records from 6805 Holstein Friesian dairy cows in 175 herds. Ultrasonography assessment underestimated gestational age relative to days since last service by 0.51 days (standard error [SE]: 0.040), although the differential was less during embryonic development phase (i.e., ≤42 days of gestation; mean overestimation of 0.31 days) versus fetal development phase (i.e., >42 days of gestation; mean underestimation of 0.81 days). Predicted calving date calculated from ultrasonography was 1.41 days (SE: 0.040) later than the actual subsequent calving date and was, on average, 0.52 days later than predicted calving date, assuming a gestation length of 282 days. Parity of the dam (P < 0.05), stage of pregnancy (P < 0.001), and sex of the calf born (P < 0.001) were all associated with the differential in gestational age based on ultrasonography versus days since last service. No obvious trend among parities was evident in the difference between the methods in predicting gestational age. Ultrasonography underestimated gestational age by 0.83 (SE: 0.15) days in parity 5+ cows and underestimated gestational age by 0.41 (SE: 0.14) days in the first-parity cows. Relative to gestational age predicted from the most recent service, ultrasonography underestimated gestational age by 0.75 (SE: 0.13) days for heifer fetuses and underestimated gestational age by 0.36 (SE: 0.13) days for bull fetuses. The heritability of the differential in gestational age between the methods of prediction was low 0.05 (SE: 0.022), corroborating heritability estimates for most cow reproductive traits. Overestimation of gestational age using ultrasonography was associated with an increased likelihood of pregnancy loss (P < 0.001). Gender of calf born (P < 0.001), sire breed of calf (P < 0.001), and parity (P < 0.001) were all associated with gestation length. Gestation length was 1.27 days longer (SE: 0.01) for bull calves compared to heifer calves. Calves from beef sires had a longer gestation length than calves from dairy sires, and older parity cows had a longer gestation length than younger cows. The results highlight factors associated with differences in gestational age obtained from ultrasonography and insemination data and illustrate the value of ultrasonography for the prediction of calving date and pregnancy loss.

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.

Breeding the dairy cow of the future: what do we need?

Genetics is responsible for approximately half the observed changes in animal performance in well structured breeding programs. Key characteristics of the dairy cow of the future include (1) production of a large quantity of high-value output (i.e. milk and meat), (2) good reproductive performance, (3) good health status, (4) good longevity, (5) no requirement for a large quantity of feed, yet being able to eat sufficient feed to meet its requirements, (6) easy to manage (i.e. easy calving, docile), (7) good conformation (over and above reflective of health, reproductive performance and longevity), (8) low environmental footprint, and (9) resilience to external perturbations. Pertinent and balanced breeding goals must be developed and implemented to achieve this type of animal; excluding any characteristic from the breeding goal could be detrimental for genetic gain in this characteristic. Attributes currently not explicitly considered in most dairy-cow breeding objectives include product quality, feed intake and efficiency, and environmental footprint; animal health is poorly represented in most breeding objectives. Lessons from the past deterioration in reproductive performance in the global Holstein population remind us of the consequences of ignoring or failing to monitor certain animal characteristics. More importantly, however, current knowledge clearly demonstrates that once unfavourable trends have been identified and the appropriate breeding strategy implemented, the reversal of genetic trends is achievable, even for low-heritability traits such as reproductive performance. Genetic variation exists in all the characteristics described. In the genomics era, the relevance of heritability statistics for most traits is less; the exception is traits not amenable to routine measurement in large populations. Phenotyping strategies (e.g. more detailed phenotypes, larger population) will remain a key component of an animal breeding strategy to achieve the cow of the future as well as providing the necessary tools and information to monitor performance. The inclusion of genomic information in genetic evaluations is, and will continue, to improve the accuracy of genetic evaluations, which, in turn, will augment genetic gain; genomics, however, can also contribute to gains in performance over and above support of increased genetic gain. Nonetheless, the faster genetic gain and thus reduced ability to purge out unfavourable alleles necessitates the appropriate breeding goal and breeding scheme and very close monitoring of performance, in particular for traits not included in the breeding goals. Developments in other disciplines (e.g. reproductive technologies), coupled with commercial struggle for increased market share of the breeding industry, imply a possible change in the landscape of dairy-cow breeding in the future.

Genetics of bovine respiratory disease in cattle: can breeding programs reduce the problem?

Genetics is responsible for approximately half the observed change in performance internationally in well-structured cattle breeding programs. Almost all, if not all, individual characteristics, including animal health, have a genetic basis. Once genetic variation exists then breeding for improvement is possible. Although the heritability of most health traits is low to moderate, considerable exploitable genetic variation does exist. From the limited studies undertaken, and mostly from limited datasets, the direct heritability of susceptibility to BRD varied from 0.07 to 0.22 and the maternal heritability (where estimated) varied from 0.05 to 0.07. Nonetheless, considerable genetic variation clearly exists; the genetic standard deviation for the direct component (binary trait), although differing across populations, varied from 0.08 to 0.20 while the genetic standard deviation for the maternal component varied from 0.04 to 0.07. Little is known about the genetic correlation between genetic predisposition to BRD and animal performance; the estimation of these correlations should be prioritized. (Long-term) Breeding strategies to reduce the incidence of BRD in cattle should be incorporated into national BRD eradication or control strategies.