Review: Opportunities and challenges for the genetic selection of dairy calf disease traits.

Interest in dairy cow health continues to grow as we better understand health's relationship with production potential and animal welfare. Over the past decade, efforts have been made to incorporate health traits into national genetic evaluations. However, they have focused on the mature cow, with calf health largely being neglected. Diarrhoea and respiratory disease comprise the main illnesses with regard to calf health. Conventional methods to control calf disease involve early separation of calves from the dam and housing calves individually. However, public concern regarding these methods, and growing evidence that these methods may negatively impact calf development, mean the dairy industry may move away from these practices. Genetic selection may be a promising tool to address these major disease issues. In this review, we examined current literature for enhancing calf health through genetics and discussed alternative approaches to improve calf health via the use of epidemiological modelling approaches, and the potential of indirectly selecting for improved calf health through improving colostrum quality. Heritability estimates on the observed scale for diarrhoea ranged from 0.03 to 0.20, while for respiratory disease, estimates ranged from 0.02 to 0.24. The breadth in these ranges is due, at least in part, to differences in disease prevalence, population structure, data editing and models, as well as data collection practices, which should be all considered when comparing literature values. Incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in disease traits, as it accounts for disease transmission among contemporaries. Colostrum intake is a major determinant of whether a calf develops either respiratory disease or diarrhoea. Colostrum traits have the advantage of being measured and reported on a continuous scale, which removes the issues classically associated with binary disease traits. Overall, genetic selection for improved calf health is feasible. However, to ensure the maximum response, first steps by any industry members should focus efforts on standardising recording practices and encouragement of uploading information to genetic evaluation centres through herd management software, as high-quality phenotypes are the backbone of any successful breeding programme.

Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows.

The large-scale recording of traits such as feed efficiency and methane emissions for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated to feed efficiency and methane emission traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with feed efficiency, methane production (methane emission in g/day), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time, feed efficiency, methane and production traits using 7,358 records from 656 first lactation Holstein cows. The estimated heritabilities were moderate for rumination time (0.45 ± 0.14), methane production (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy corrected milk (0.28 ± 0.07), while low and non-significant for feed efficiency (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on ECM and MBW. A favorable negative genetic correlation was estimated between rumination time and methane production (-0.53 ± 0.24), while a positive favorable correlation was estimated between rumination time and energy corrected milk (0.49 ± 0.11). The estimated genetic correlation of rumination time with feed efficiency (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13, P = 0.11). These results indicate that rumination time is genetically associated with methane production and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for rumination time, methane production and feed efficiency become available.

Investigating the potential for genetic selection of dairy calf disease traits using management data.

Genetic selection could be a tool to help improve the health and welfare of calves; however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data composed of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single- and multiple-trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the effect of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation, standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software is required. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation.

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows.

Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10-3 BCS units in the 120-d dataset and of 3.63 × 10-3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10-3 BCS units and 1.51 × 10-3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.

Genetic evaluation of heat tolerance in Holsteins using test-day production records and NASA POWER weather data.

Heat stress is a prominent issue in livestock production, even for intensively housed dairy herds in Canada. Production records and meteorological data can be combined to assess heat tolerance in dairy cattle. The overall aim of this study was to evaluate the possibility of genetic evaluation for heat tolerance in Canadian dairy cattle. The 2 specific objectives were (1) to estimate the genetic parameters for milk, fat, and protein yield for Holsteins while accounting for high environmental heat loads, and (2) to determine if a genotype-by-environment interaction causes reranking of top-ranked sires between environments with low and high heat loads. A repeatability test-day model with a heat stress function was used to evaluate the genetic merit for milk, fat, and protein yield under heat stress and at thermal comfort for first parity in 5 regions in Canada. The heat stress function for each trait was defined using a specific temperature-humidity index (THI) threshold. The purpose of this function was to quantify the level of heat stress that was experienced by the dairy cattle. The estimated genetic correlation between the general additive genetic effect and the additive effect on the slope of the change in the trait phenotype for milk, fat, and protein yield ranged from -0.16 to -0.30, -0.20 to -0.44, and -0.28 to -0.42, respectively. These negative correlations imply that there is an antagonistic relationship between sensitivity to heat stress and level of production. The heritabilities for milk, fat, and protein yield at 15 units above the THI threshold ranged from 0.15 to 0.27, 0.11 to 0.15, and 0.11 to 0.15, respectively. Finally, the rank correlations between the breeding values from a repeatability model with no heat stress effect and the breeding values accounting for heat stress for the 100 top-ranked bulls indicated possible interaction between milk production traits and THI, resulting in substantial reranking of the top-ranked sires in Canada, especially for milk yield. This is the first study to implement weather data from the NASA POWER database in a genetic evaluation of heat tolerance in dairy cattle. The NASA POWER database is a novel alternative meteorological resource that is potentially more reliable and consistent and with broader coverage than weather station data increasing the number of animals that could be included in a heat stress evaluation.

The effect of progesterone concentrations during superovulation of Holstein heifers in a randomized trial.

The aim of this study was to evaluate the effect of different progesterone (P4) concentrations during the follicular growth on the intensity of estrous expression, ovarian response to the superovulatory treatment, and embryo production and quality in superovulated heifers. A total of 63 Holstein heifers were randomly assigned into 2 experimental groups: Low P4 (n = 31) and High P4 (n = 32). Animals received a pre-synchronization protocol followed by a protocol of superovulation that included the allocated P4 treatment. Activity was monitored continuously by an automated activity monitor, and estrus characteristics (maximum intensity and duration) were recorded. Embryo collection was performed 7 d post artificial insemination (AI). Embryos were counted and graded from good or excellent (1) to degenerated (4). The outcomes of interest were: number and diameter of follicles at the time of AI, ovulation success (confirmed 7 d post-AI), time to estrus event, maximum intensity and duration of estrus, number and quality of embryos. Data were analyzed according to the type of outcome variable using logistic, linear, or Poisson regression models. A total of 105 embryos (High P4: n = 42; Low P4: n = 63) were graded for quality. Different P4 levels did not affect the maximum intensity (High P4 = 497.8 ± 23.9%; Low P4 = 542.2 ± 23.5%) or the duration (High P4 = 13.5 ± 1.5 h; Low P4 = 14.3 ± 1.4 h) of estrus. Heifers in the High P4 treatment had greater number of follicles at time of AI (High P4 = 16.6 ± 1.6 follicles; Low P4 = 13.9 ± 1.2 follicles), but with smaller diameter (High P4 = 11.3 ± 0.1 mm; Low P4 = 12.0 ± 0.1 mm) compared with Low P4. High P4 heifers tended to have better embryo quality compared with Low P4 heifers (odds ratio = 1.98; 95% CI = 0.90-4.35). High P4 heifers had less embryos than Low P4 heifers, but this was modified by the CIDR (intravaginal implant of P4) removal to estrus interval (interval 0-21 h: mean ratio = 1.15, 95% CI = 0.42-1.87; interval 22-46 h: mean ratio = 0.58, 95% CI = 0.27-0.96). Although estrous expression was not associated with embryo quality, as the duration and the maximum intensity of estrous expression increased, the number of embryos recovered 7 d post-AI increased (duration: mean ratio = 1.04; 95% CI = 1.03-1.05; maximum intensity: mean ratio = 1.50; 95% CI = 1.42-1.58). In conclusion, P4 during the follicular growth, and intensity of estrus, are playing a role in regulating the quality and the number of embryos produced by superovulated heifers. This study was supported by contributions from Resilient Dairy Genome Project and the Natural Sciences and Engineering Research Council.

Phenotypic analysis of heat stress in Holsteins using test-day production records and NASA POWER meteorological data.

Weather station data and test-day production records can be combined to quantify the effects of heat stress on production traits in dairy cattle. However, meteorological data sets that are retrieved from ground-based weather stations can be limited by spatial and temporal data gaps. The National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) database provides meteorological data over regions where surface measurements are sparse or nonexistent. The first aim of this study was to determine whether NASA POWER data are a viable alternative resource of weather data for studying heat stress in Canadian Holsteins. The results showed that average, minima, and maxima ambient temperature and dewpoint temperature as well as 4 different types of temperature-humidity index (THI) values from NASA POWER were highly correlated to the corresponding values from weather stations (regression R2 > 0.80). However, the NASA POWER values for the daily average, minima, and maxima wind speed and relative humidity were poorly correlated to the corresponding weather station values (regression R2 = 0.10 to 0.49). The second aim of this study was to quantify the influence of heat stress on Canadian dairy cattle. This was achieved by determining the THI values at which milk, protein, and fat yield started to decline due to heat stress as well as the rates of decline in these traits after the respective thresholds, using segmented polynomial regression models. This was completed for both primiparous and multiparous cows from 5 regions in Canada (Ontario, Quebec, British Columbia, the Prairies, and the Atlantic Maritime). The results showed that all production traits were negatively affected by heat stress and that the patterns of responses for milk, fat, and protein yields to increasing THI differed from each other. We found 3 THI thresholds for milk yield, 1 for fat yield, and 2 for protein yield. All thresholds marked a change in rate of decrease in production yield per unit THI, except for the first milk yield threshold, which marked a greater rate of increase. The first thresholds for milk yield ranged between 47 and 50, the second thresholds ranged between 61 and 69, and the third thresholds ranged between 72 and 76 THI units. The single THI threshold for fat yield ranged between 48 and 55 THI units. Finally, the first and second thresholds ranged between 58 and 62 THI units and 72 and 73 THI units for protein yield, respectively.

Erratum to "Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle" (JDS Commun. 3:114-119).

[This corrects the article DOI: 10.3168/jdsc.2021-0150.].

Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle.

Although selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015. Feed Saved combines residual feed intake (RFI) genomic EBV and maintenance requirements calculated from mature BW EBV. The FS EBV was designed to enable the selection of cows for reduced energy requirements with similar milk production. In this study, we used a reference population of 3,711 animals in a multivariate analysis including Australian heifers (AUSh), Australian cows (AUSc), and overseas cows (OVEc) to update the Australian EBV for lifetime RFI (i.e., a breeding value that incorporated RFI in growing and lactating cows) and to recalculate the FS EBV in Australian Holstein bulls (AUSb). The estimates of genomic heritabilities using univariate (only AUSc or AUSh) to trivariate (including the OVEc) analyses were similar. Genomic heritabilities for RFI were estimated as 0.18 for AUSc, 0.27 for OVEc, and 0.36 for AUSh. The genomic correlation for RFI between AUSc and AUSh was 0.47 and that between AUSc and OVEc was 0.94, but these estimates were associated with large standard errors (range: 0.18-0.28). The reliability of lifetime RFI (a component of FS) in the trivariate analysis (i.e., including OVEc) increased from 11% to 20% compared with the 2015 model and was greater, by 12%, than in a bivariate analysis in which the reference population included only AUSc and AUSh. By applying the prediction equation of the 2020 model, the average reliability of the FS EBV in 20,816 AUSb that were born between 2010 and 2020 improved from 33% to 43%. Previous selection strategies-that is, using the predecessor of the Balanced Performance Index (Australian Profit Ranking index) that did not include FS-have resulted in an unfavorable genetic trend in FS. However, this unfavorable trend has stabilized since 2015, when FS was included in the Balanced Performance Index, and is expected to move in a favorable direction with selection on Balanced Performance Index or the Health Weighted Index. Doubling the reference population, particularly by incorporating international data for feed efficiency, has improved the reliability of the FS EBV. This could lead to increased genetic gain for feed efficiency in the Australian industry.

Machine learning classification of breeding protocol descriptions from Canadian Holsteins.

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility.

Accuracy of genomic selection for reducing susceptibility to pendulous crop in turkey (Meleagris gallopavo).

Pendulous crop (PC) in the turkey occurs when the crop distends from its normal position, thereby preventing the movement of feed and water from the crop down into the digestive system. This condition negatively impacts the turkey industry at both production and welfare levels. In this study, we estimated the genetic parameters for PC incidence and its genetic correlation with 5 production traits. Additionally, we evaluated the prediction accuracy and bias of breeding values for the selection candidates using pedigree (BLUP) or pedigree-genomic (ssGBLUP) relationships among the animals. A total of 245,783 turkey records were made available by Hybrid Turkeys, Kitchener, Canada. Of these, 6,545 were affected with PC. In addition, the data included 9,634 records for breast meat yield (BMY); 5,592 records for feed conversion ratio (FCR) and residual feed intake (RFI) in males; 170,844 records for body weight (BW) and walking score (WS) between 18 and 20 wk of age for males (71,012) and females (99,832), respectively. Among this population, 36,830 were genotyped using a 65K SNP Illumina Inc. chip. While all animals passed the quality control criteria, only 53,455 SNP markers were retained for subsequent analysis. Heritability for PC was estimated at 0.16 ± 0.00 and 0.17 ± 0.00 using BLUP and ssGBLUP, respectively. The incidence of PC was not genetically correlated with WS or FCR. Low unfavourable genetic correlations with BW (0.12 and 0.14), BMY (0.24 and 0.24) and RFI (-0.33 and -0.28) were obtained using BLUP and ssGBLUP, respectively. Using ssGBLUP showed higher prediction accuracy (0.51) for the breeding values for the selection candidates than the pedigree-based model (0.35). Whereas the bias of the prediction was slightly reduced with ssGBLUP (0.33 ± 0.05) than BLUP (0.30 ± 0.08), both models showed a regression coefficient lower than one, indicating inflation in the predictions. The results of this study suggest that PC is a heritable trait and selection for lower PC incidence rates is feasible. Although further investigation is necessary, selection for BW, BMY, and RFI may increase PC incidence. Incorporating genomic information would lead to higher accuracy in predicting the genetic merit for selection candidates.

Occurrence and greater intensity of estrus in recipient lactating dairy cows improve pregnancy per embryo transfer.

The aim of this study was to determine the association between occurrence and intensity of estrous expression with pregnancy success in recipient lactating dairy cows subjected to embryo transfer (ET). Two observational studies were conducted. Holstein cows were synchronized using the same timed ET protocol, based on estradiol and progesterone in both experiments. At 9 d after the end of the timed ET protocol only animals that had ovulated were implanted with a 7-d embryo [experiment 1 (Exp. 1); n = 1,401 ET events from 1,045 cows, and experiment 2 (Exp. 2); n = 1,147 ET events from 657 cows]. Embryos were produced in vivo (Exp. 1 and Exp. 2) and in vitro (only Exp. 2), then transferred to recipient cows as fresh or frozen-thawed. Pregnancy was confirmed at 29 and 58 d after the end of timed ET protocol. In Exp. 1, animals had their estrous expression monitored through a tail chalk applied on the tail head of the cows and evaluated daily for chalk removal (no estrus: 100% of chalk remaining; estrus: <50% of chalk remaining). In Exp. 2, cows were continuously monitored by a leg-mounted automated activity monitor. Estrous expression was quantified using the relative increase in physical activity at estrus in relation to the days before estrus. Estrous expression was classified as no estrus [<100% relative increase in activity (RI)], weak intensity (100-299% RI), and strong intensity (≥300% RI). Data were analyzed by analysis of variance using mixed linear regression models (GLIMMIX) in SAS (SAS Institute Inc.). A total of 65.2% (914/1,401) and 89.2% (1,019/1,142) of cows from Exp. 1 and Exp. 2, respectively, displayed estrus at the end of the ovulation synchronization protocol. In Exp. 1, cows expressing estrus before to ET had greater pregnancy per ET than those that did not [41.0 ± 2.3% (381/914) vs. 31.5 ± 2.9% (151/487), respectively]. Similarly, in Exp. 2, cows classified in the strong intensity group had greater pregnancy per ET compared with cows in the weak intensity and no estrus groups [41.3 ± 2.2% (213/571) vs. 32.7 ± 2.7% (115/353) vs. 11.3 ± 3.5% (26/218), respectively]. There was no effect of ET type on pregnancy per ET in Exp. 1. However, in Exp. 2, cows that received an in vivo-produced embryo, either fresh or frozen, had greater pregnancy per ET compared with cows that received in vitro-produced embryo. Cows receiving embryos in the early blastocyst and blastocyst stage had greater fertility compared with cows receiving embryos in the morula stage. There was an interaction between the occurrence of estrus and the stage of embryo development on pregnancy per ET, cows which displayed estrus and received a morula or early blastocyst had greater pregnancy per ET than cows that did not display estrus. In conclusion, the occurrence and the intensity of estrous expression improved pregnancy per ET in recipient lactating dairy cows and thus could be used as a tool to assist in the decision making of reproduction strategies in dairy farms.

Effect of synchronized breeding on genetic evaluations of fertility traits in dairy cattle.

Estrus detection has become more difficult over the years due to decreases in the estrus expression of high-producing dairy cows, and increased herd sizes and animal density. Through the use of hormonal synchronization protocols, also known as timed artificial insemination (TAI) protocols, it is possible to alleviate some of the challenges associated with estrus detection. However, TAI masks cows' fertility performance, resulting in an unfair comparison of treated animals and innately fertile animals. Consequently, genetically inferior and superior cows show similar phenotypes, making it difficult to distinguish between them. As genetic programs rely on the collection of accurate phenotypic data, phenotypes collected on treated animals likely add bias to genetic evaluations. In this study, to assess the effect of TAI, the rank correlation of bulls for a given trait using only TAI records were compared with the same trait using only heat detection records. A total of 270,434 records from 192,539 animals split across heifers, first and second parity cows were analyzed for the traits: calving to first service, first service to conception, and days open. Results showed large reranking across all traits and parities between bulls compared based on either having only TAI records or only heat detection records, suggesting that a bias does indeed exist. Large reranking was also observed for both the heat detection and TAI groups among the top 100 bulls in the control group, which included all records. Furthermore, breeding method was added to the model to assess its effect on bull ranking. However, there were only minor changes in the rank correlations between scenario groups. Therefore, more complex methods to account for the apparent bias created by TAI should be investigated; for this, the method by which these data are collected needs to be improved through creating a standardized way of recording breeding codes. Though the results of this study suggest the presence of bias within current fertility evaluations, additional research is required to confirm the findings of this study, including looking at high-reliability bulls specifically, to determine if the levels of reranking remain. Future studies should also aim to understand the potential genetic differences between the fertility traits split via management technology, possibly in a multiple-trait analysis.

Review: Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world.

The massive improvement in food production, as a result of effective genetic selection combined with advancements in farming practices, has been one of the greatest achievements of modern agriculture. For instance, the dairy cattle industry has more than doubled milk production over the past five decades, while the total number of cows has been reduced dramatically. This was achieved mainly through the intensification of production systems, direct genetic selection for milk yield and a limited number of related traits, and the use of modern technologies (e.g., artificial insemination and genomic selection). Despite the great betterment in production efficiency, strong drawbacks have occurred along the way. First, across-breed genetic diversity reduced dramatically, with the worldwide use of few common dairy breeds, as well as a substantial reduction in within-breed genetic diversity. Intensive selection for milk yield has also resulted in unfavorable genetic responses for traits related to fertility, health, longevity, and environmental sensitivity. Moving forward, the dairy industry needs to continue refining the current selection indexes and breeding goals to put greater emphasis on traits related to animal welfare, health, longevity, environmental efficiency (e.g., methane emission and feed efficiency), and overall resilience. This needs to be done through the definition of criteria (traits) that (a) represent well the biological mechanisms underlying the respective phenotypes, (b) are heritable, and (c) can be cost-effectively measured in a large number of animals and as early in life as possible. The long-term sustainability of the dairy cattle industry will also require diversification of production systems, with greater investments in the development of genetic resources that are resilient to perturbations occurring in specific farming systems with lesser control over the environment (e.g., organic, agroecological, and pasture-based, mountain-grazing farming systems). The conservation, genetic improvement, and use of local breeds should be integrated into the modern dairy cattle industry and greater care should be taken to avoid further genetic diversity losses in dairy cattle populations. In this review, we acknowledge the genetic progress achieved in high-yielding dairy cattle, closely related to dairy farm intensification, that reaches its limits. We discuss key points that need to be addressed toward the development of a robust and long-term sustainable dairy industry that maximize animal welfare (fundamental needs of individual animals and positive welfare) and productive efficiency, while also minimizing the environmental footprint, inputs required, and sensitivity to external factors.

Associations between feed efficiency and aspects of lactation curves in primiparous Holstein dairy cattle.

Genetic selection for improved feed efficiency in dairy cattle has received renewed attention over the last decade to address the needs of a growing global population. As milk yield is a critical component of feed efficiency metrics in dairy animals, our objective was to evaluate the associations between feed efficiency in primiparous Holstein cattle and parameters of a mathematical model describing individual lactation curves. The Dijkstra lactation curve model was fit to individual lactation records from 34 Holstein heifers with previously estimated measures of feed efficiency. We found that the optimal fit of the Dijkstra model was achieved using daily milk yield records up to 21 d in milk to capture the rise to peak milk yield and using monthly dairy herd improvement records for the remainder of lactation to accurately characterize lactation persistency. In the period of lactation before peak milk yield, improved feed efficiency was associated with a faster increase in daily milk yield over a shorter period of time at the expense of increased mobilization of body reserves; this serves to reinforce the concept that dairy cattle are primarily capital breeders versus income breeders. Feed efficiency in the period following peak lactation, as measured by gross feed efficiency, return over feed costs, and net energy efficiency of lactation, was positively associated with higher peak milk yield. The findings in early lactation suggest that estimates of feed efficiency could be improved by evaluating feed efficiency relative to conception, rather than parturition and lactation, to better account for the energy stored and released from body reserves in capital breeding.

Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins.

Genetic improvement is a crucial tool to deal with the increasing demand for high quality, sustainably produced dairy. Breeding programs are based on genetic parameters, such as heritability and genetic correlations, for economically important traits in a population. In this study, we estimated population genetic parameters and genetic trends for 67 traits evaluated on heifers and first-lactation Canadian Holstein cows. The data consisted of approximately 500,000 records with pedigree information collected from 1980 to 2019. Genetic parameters were estimated using bivariate linear animal models under a Bayesian approach. Analyses for the 67 traits resulted in 2,211 bivariate combinations, from which the estimated genetic parameters are reported here. The most highly heritable traits were fat percent (0.66) and protein percent (0.69), followed by stature (0.47). Lowest heritabilities (0.01) were observed for disease-related traits, such as lameness and toe ulcer, and calf survival. The genetic correlations between gestation length, calf size, and calving ease measured on both heifer and cows were close to unity. On the other hand, traits such as body condition score and pin width, cystic ovaries and sole ulcer, rear teat placement, and toe ulcer were genetically unrelated. This study reports genetic parameters that have not been previously published for Canadian Holstein cows, and provides updates of those previously estimated. These estimates are useful for building new indexes, updating existing selection indexes, and for predicting correlated responses due to inclusion of novel traits in the breeding programs.

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study.

About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1-S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.

Estimating the environmental impact of dairy cattle breeding programs through emission intensity.

A recently developed methodological approach for determining the greenhouse gas emissions impact of national breeding programs was applied to measure the effects of current and future breeding goals on the emission intensity (EI) of the Canadian dairy industry. Emission intensity is the ratio of greenhouse gas outputted in comparison to the product generated. Traits under investigation affected EI by either decreasing the direct emissions yield (i.e. increasing feed performance), changing herd structure (i.e. prolonging herd life) or through the dilution effect of increased production (i.e. increasing fat yield). The intensity value (IV) of each trait, defined as the change in emissions' intensity per unit change in each trait, was calculated for each of the investigated traits. The IV trend of these traits was compared for the current and prospective selection index, as well as for a system with and without quota (the supply management policy designed to prevent overproduction). The overall EI of the average genetic merit Canadian dairy herd per breeding female was 5.07 kg CO2eq/kg protein equivalent output. The annual reduction in EI due to the improvement of production traits was -0.027, -0.018 and -0.006 for fat, protein and milk other solids, respectively. The functional traits, herd life and mastitis resistance, had more modest effects (-0.008 and -0.001, respectively). These results are consistent with international studies that identified traits related to production, survival, health and fertility as having the largest impact on the environmental footprint of dairy cattle. Overall, the dairy industry is becoming more efficient by reducing its EI through selection of environmentally favorable traits, with a 1% annual reduction of EI in Canada.

Discovering lethal alleles across the turkey genome using a transmission ratio distortion approach.

Deviation from Mendelian inheritance expectations (transmission ratio distortion, TRD) has been observed in several species, including the mouse and humans. In this study, TRD was characterized in the turkey genome using both allelic (specific- and unspecific-parent TRD) and genotypic (additive- and dominance-TRD) parameterizations within a Bayesian framework. In this study, we evaluated TRD for 23 243 genotyped Turkeys across 56 393 autosomal SNPs. The analyses included 500 sires, 2013 dams and 11 047 offspring (trios). Three different haplotype sliding windows of 4, 10 and 20 SNPs were used across the autosomal chromosomes. Based on the genotypic parameterizations, 14 haplotypes showed additive and dominance TRD effects highlighting regions with a recessive TRD pattern. In contrast, the allelic model uncovered 12 haplotype alleles with the allelic TRD pattern which showed an underrepresentation of heterozygous offspring in addition to the absence of homozygous animals. For regions with the allelic pattern, only one particular region showed a parent-specific TRD where the penetrance was high via the dam, but low via the sire. The gene set analysis uncovered several gene ontology functional terms, Reactome pathways and several Medical Subject Headings that showed significant enrichment of genes associated with TRD. Many of these gene ontology functional terms (e.g. mitotic spindle assembly checkpoint, DRM complex and Aneuploidy), Reactome pathways (e.g. Mismatch repair) and Medical Subject Headings (e.g. Adenosine monophosphate) are known to be related to fertility, embryo development and lethality. The results of this study revealed potential novel candidate lethal haplotypes, functional terms and pathways that may enhance breeding programs in Turkeys through reducing mortality and improving reproduction rate.