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Body conformation traits are directly associated with longevity, fertility, health, and workability in dairy cows and have been under direct genetic selection for many decades in various countries worldwide. The main objectives of this study were to perform genome-wide association studies and functional enrichment analyses for fourteen body conformation traits using imputed high-density single nucleotide polymorphism (SNP) genotypes. The traits analyzed include body condition score (BCS), body depth (BD), bone quality (BQ), chest width (CW), dairy capacity (DC), foot angle (FAN), front legs view (FLV), heel depth (HDe), height at front end (HFE), locomotion (LOC), rear legs rear view (RLRV), rear legs side view (RLSV), stature (ST), and a composite feet and legs score index (FL) of Holstein cows scored in Canada. De-regressed estimated breeding values from a dataset of 39,135 North American Holstein animals were used as pseudo-phenotypes in the genome-wide association analyses. A mixed linear model was used to estimate the SNP effects, which ranged from 239,533 to 242,747 markers depending on the trait analyzed. Genes and quantitative trait loci (QTL) located up to 100 Kb upstream or downstream of the significant SNPs previously cited in the Animal QTLdb were detected, and functional enrichment analyses were performed for the candidate genes identified for each trait. A total of 20, 60, 13, 17, 27, 8, 7, 19, 4, 10, 13, 15, 7, and 13 genome-wide statistically significant SNPs for Bonferroni correction based on independent chromosomal segments were identified for BCS, BD, BQ, CW, DC, FAN, FLV, HDe, HFE, LOC, RLRV, RLSV, ST, and FL, respectively. The significant SNPs were located across the whole genome, except on chromosomes BTA24, BTA27, and BTA29. Four markers (for BCS, BD, HDe, and RLRV) were statistically significant when considering a much stricter threshold for the Bonferroni correction for multiple tests. Moreover, the genomic regions identified overlap with various QTL previously reported for the trait groups of exterior, health, meat and carcass, milk, production, and reproduction. The functional enrichment analyses revealed 27 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously reported as linked to bone development, metabolism, as well as infectious and immunological diseases. [ABSTRACT FROM AUTHOR]

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

For a more sustainable dairy industry it is crucial to select for feed-efficient cattle. Although feed efficiency research has primarily focused on lactating animals, genetic selection for feed efficiency in calves has continued to gain research interest. Previously estimated genetic parameters for pre-weaned calf feed efficiency suggest residual metabolizable energy intake (RMEI) as a potential trait for selection. However, the trait definition of RMEI needs further evaluation. In previous studies only the intake of colostrum, fed in the first hours of the lif of a calf, was included to define RMEI. The inclusion of colostrum quality ([grams of immunoglobulin (g IgG)] might better represent the transfer of passive immunity to calves and enhance the indicator trait for feed efficiency. Therefore, the objective of this study was to evaluate the impact of colostrum characteristics on estimating RMEI as a measure of calf feed efficiency in Canadian Holstein calves. Records from 330 pre-weaned Canadian Holstein calves between 0 and 65 d of age, born between 2016 and 2021 were provided by The Ontario Dairy Research Centre (Elora, ON, Canada). Data included calf health (incidence of scours, 1 = event, 2 = no event), feed intake (kg), body weight (BW; kg), and colostrum quality (g IgG) ranging between 17.41 and 151.69 g IgG. RMEI was estimated by four linear regression models using metabolizable energy intake (MEI) for two time periods (RMEI1 = first month of age calves, RMEI2 = second month of age calves) according to previous studies. For both time periods, colostrum quality was then included in the linear regression model, in addition to scours incidence, year and season, trial, and regression on average daily gain and metabolic BW to estimate RMEI corrected for colostrum quality (i.e., RMEIc1 and RMEIc2). Colostrum quality was fitted as a fixed effect with four classes (≤72 g IgG, 73-76 g IgG, 77-96 g IgG, ≥97 g IgG), divided according to the quartiles of its distribution. The inclusion of colostrum quality had a significant effect on MEI in the first month of age (P ± 0.001) and a trend for significance in the second month of age (P = 0.095). RMEIc model had an adjusted R2 value of 0.86 with the inclusion of colostrum quality in the first time-period. As a next step, genetic parameters need to be estimated to determine any possible gain using RMEIc over RMEI. Improving the definition of RMEI will further the potential to select for feed efficiency in younger animals to achieve sustainability goals. [ABSTRACT FROM AUTHOR]

The goal of infectious disease control is often local eradication, but this is theoretically impossible to achieve based on classical quantitative genetic theory. Current methods focus on the susceptibility of individuals to disease and assume that exposure to a pathogen is 1) constant over time, 2) equal among individuals, and (3) due entirely to the environment. For this reason, it is likely that conventional genetic methods are capturing only a fraction of genetic variation in disease occurrence. The incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in infectious disease traits. This stems from the ability to include the positive feed-back dynamics of infectious disease transmission. From an epidemiolocal perspective, both susceptibility and infectivity also have indirect genetic effects (IGE), because the genotype of one individual impacts the risk of infection of other individuals, and this can drastically affect the rate and direction of response to selection. Epidemiological models unravel the genetic heterogeneity in both susceptibility and infectivity traits, and account for the impact each animal has on its contemporaries compared with conventional quantitative genetic approaches. From a calf health perspective, there is a shift towards group housing dairy calves, primarily due to welfare and social acceptability. In turn, this will lead to increased animal interactions from a young age. Therefore, the aim of this study was to investigate the potential of incorporating IGE into current quantitative approaches to determine its impact on selection potential. Our study looked at two common infectious diseases in calves on dairy farms: respiratory problems (RESP) and diarrhea (DIAR). Producer-recorded data comprised of 19,445 records collected on 34 herds that group housed calves between 2007 and 2020. Calves were allocated into pen groups based on birth dates and herd specific housing practices. Original phenotypes were split into 10 records, each representing a week of life, to determine when exactly calves became sick and infective. Several scenarios were investigated with respect to the time an animal was infective for following a disease case (1 or 2 wk), and the maximum difference in weeks between animal birth dates to be included within the same pen (2 to 5 wk). Variance components were estimated using a generalized linear mixed model fitting a complementary-log-log link function with offset for exposure. Initial heritability estimates for susceptibility on the observed scale for DIAR ranged from 0.03 to 0.07, and from 0.02 to 0.03 for RESP across scenarios. While heritability estimates for infectivity are currently being investigated, the results for susceptibility highlight the potential for incorporating IGE into genetic evaluation of disease traits and provide an introduction towards incorporating epidemiological theory into classical quantitative approaches to better respect the transmission dynamics of infections. [ABSTRACT FROM AUTHOR]

Abstract The effects of additive, dominance, additive by dominance, additive by additive and dominance by dominance genetic effects on age at first service, non-return rates and interval from calving to first service were estimated. Practical considerations of computing additive and dominance relationships using the genomic relationship matrix are discussed. The final strategy utilized several groups of 1000 animals (heifers or cows) in which all animals had a non-zero dominance relationship with at least one other animal in the group. Direct inversion of relationship matrices was possible within the 1000 animal subsets. Estimates of variances were obtained using Bayesian methodology via Gibbs sampling. Estimated non-additive genetic variances were generally as large as or larger than the additive genetic variance in most cases, except for non-return rates and interval from calving to first service for cows. Non-additive genetic effects appear to be of sizeable magnitude for fertility traits and should be included in models intended for estimating additive genetic merit. However, computing additive and dominance relationships for all possible pairs of individuals is very time consuming in populations of more than 200 000 animals.

The decision of premature culling cows directly impacts the profitability of dairy farms. A comprehensive characterization of the primary causes of culling reasons would greatly improve both management and selection objectives in dairy cattle breeding programs. Therefore, this study aimed to analyze the temporal frequencies of 34 culling reasons in Canadian Holstein cows. After data editing and quality control, records from 3,096,872 cows culled from 9,683 herds spread across Canada were used for the analyses covering the periods from 1996 to 2020. Reproductive issues were the main culling reason accounting for 23.02%, followed by milk production (20.82%), health (20.39%), conformation problems (13.69%), economic factors (13.10%), accidents (5.67%), age-related causes (1.67%), and workability (1.63%). Nearly fifty-eight percent of cows were culled after 47 months of age. The observed frequencies of culling due to economic factors were lower than expected from 1996 to 2014 and higher than expected between 2015 and 2020. Reproduction issues had the highest culling frequencies during fall (24.54%), winter (24.02%), and spring (22.51%), while health issues were the most frequent (22.51%) culling reason in the summer season. Health issues (25.50%) and milk production (27.71%) were the most frequent culling reasons in the provinces of Quebec and Ontario, respectively. Reproductive issues showed the highest frequency across climates based on the Köppen climate classification, except for Csb (Dry-summer subtropical or Mediterranean climate) and Bsk (Middle latitude steppe climate), which correspond to small regions in Canada, where production was the most frequent culling reason (29.42% and 21.56%, respectively). Reproductive and milk performance issues were the two main culling reasons in most ecozones, except in Boreal Shield and Atlantic Marine, where health issues had the highest frequencies (25.12 and 23.75%, respectively). These results will contribute to improving management practices and selective decisions to reduce involuntary culling of Holstein cows. [ABSTRACT FROM AUTHOR]

Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows. [ABSTRACT FROM AUTHOR]

Implementing breeding strategies for a more sustainable food system is one of the most pressing topics for livestock industries. Although different species face different challenges, large-scale research projects can pave the way for future opportunities in these key sectors. Herein, we present two examples (dairy and poultry) of how research projects can result in meaningful changes to commercial breeding programs. The first example is an international large-scale dairy project, which will deliver a roadmap for greenhouse gas (GHG) management using genomics and nutrition. The project aims to facilitate a 55% reduction in GHG emissions from Canadian dairy at an estimated value of $338M CAD. This systems-level approach will leverage resources developed through previous and current large-scale projects to produce accurate multi-level emission estimates and identify opportunities to mitigate enteric GHG emissions. The results of this project will provide accurate, reliable, and robust data for industry stakeholders, national policy, and GHG inventories. The second example is a research collaboration with a commercial turkey breeding company that will enable breeding strategies for long-term and sustainable genetic improvement in health traits. This project aims to reduce preslaughter mortality by 5% and condemnations due to myopathies and health issues. This reduction alone is expected to result in 7.5M kg more turkey meat at a value of $19.8M CAD. Furthermore, there is also an expected reduction of 2.3M kg of CO2 equivalent per year for each 1% livability improvement due to production efficiencies and reduced inputs wasted for more sustainable and ethical meat production. Overall, ongoing and future research initiatives will continue to deliver solutions for key Canadian livestock sectors to improve animal health and welfare as well as efficiency and sustainability. The strides made in these ongoing collaborative projects are imperative for enhancing the sustainability of animal agriculture to feed the growing population. [ABSTRACT FROM AUTHOR]