bluuebunny/crossref_metadata_2025_split huggingface dataset:
28 Optimal Definition of Contemporary Groups for Crossbred Pigs in a Joint Purebred and Crossbred Genetic Evaluation
Authors: Steyn Yvette , Lourenco Daniela , Chen Ching-Yi , Valente Bruno , Holl Justin W , Herring Willian O , Misztal Ignacy 
Published: October 2021

Abstract
In the pig industry, purebred animals are raised in nucleus herds and selected to produce crossbred progeny for commercial environments. Crossbred and purebred performances are different, correlated traits. All purebreds in a pen are assessed together at the end of a performance test. However, only selected crossbreds are removed (based on visual inspection) and measured at different times, creating many small contemporary groups (CG). This may reduce EBV prediction accuracies. Considering this sequential recording of crossbreds, the objective was to investigate the impact of different CG definitions on genetic parameters and EBV prediction accuracy for crossbred traits. Growth rate (GP) and ultrasound backfat (BFP) records were available for purebreds. Lifetime growth (GX) and backfat (BFX) were recorded on crossbreds. Different CG were tested: CG_all included farm, sex, birth year and birth week, CG_week added slaughter week, and CG_day used slaughter day instead of week. Data of 124,709 crossbreds were used. The purebred phenotypes (62,274 animals) included 3 generations of purebred ancestors of these crossbreds and their CG mates. Variance components for 4-trait models with different CG definitions were estimated with AIREML. Purebred traits’ variance components remained stable across CG definitions and varied slightly for BFX. Additive genetic variances (and heritabilities) for GX fluctuated more: 812±36 (0.28±0.01), 257±15 (0.17±0.01) and 204±13 (0.15±0.01) for CG_all, CG_week, and CG_day, respectively. The predictive ability, linear regression (LR) accuracy, bias, and dispersion of crossbred traits in crossbreds favored CG_day, but correlations with unadjusted phenotypes favored CG_all. In purebreds, CG_all showed the best LR accuracy, while showing small relative differences in bias and dispersion. Different GC scenarios showed no relevant impact on BFX EBV. This study shows that different CG definitions may affect evaluation stability and ranking. Results suggest that ignoring slaughter dates in CG is more appropriate for estimating crossbred trait EBV for purebred animals.
35 Detecting Effective Starting Point of Genomic Selection by Divergent Trends from BLUP and ssGBLUP
Authors: Abdollahi-Arpanahi Rostam , Lourenco Daniela , Misztal Ignacy 
Published: October 2021

Abstract
Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the point of divergence of genetic trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP) may indicate the start date of genomic selection. This study aimed to find the start date of genomic selection for a set of economically important traits in pigs and Angus cattle by comparing genetic and Realized Mendelian sampling (RMS) trends obtained using BLUP and ssGBLUP. In pigs, the genetic trends from ssGBLUP and BLUP diverged for all traits with different intensity. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In pigs, the RMS trends from ssGBLUP and BLUP diverged strongly for one trait and weakly for another trait. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little trend for birth weight. Divergence of the genetic trends from ssGBLUP and BLUP indicate onset of the genomic selection. Presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the two species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.
48 Genomic Studies of Feed Efficiency and Component Traits in American Mink
Authors: Davoudi Pourya , Ngoc Do Duy , Hu Guoyu , Ardestani Siavash Salek , Miar Younes 
Published: October 2021

Abstract
Feed costs are the largest expense in mink production systems. Therefore, improvement of feed efficiency (FE) is the best way to use limited resources efficiently and increase the mink industry’s sustainability. The objectives of this project are to 1) identify the genetic relationships among different FE measures and their component traits, and 2) discover the genetic architecture of FE and implement genomic selection for FE traits to increase the genetic gain in American mink. Final body weight (FBW), final body length (FBL), daily feed intake (DFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG) and Kleiber ratio (KR) traits were measured based on the phenotypic records on 1,088 American mink from the Canadian Center for Fur Animal Research (Nova Scotia, Canada). Univariate models were applied to test the significance of sex, color type, age, and nested Row(Year) as fixed effects and random maternal effect. Genetic parameters were estimated via bivariate models using ASReml-R 4. Estimated heritabilities (±SE) were 0.38±0.10, 0.36±0.10, 0.25±0.10, 0.34±0.09, 0.38±0.08, 0.37±0.07, 0.29±0.10, 0.32±0.10 and 0.34±0.10 for FBW, FBL, DFI, ADG, FCR, RFI, RG, RIG and KR, respectively. RFI showed non-significant (P &gt;0.05) genetic correlations with component traits such as FBW (0.00±0.17) and FBL (0.30±0.16) but significant (P &lt; 0.05) high genetic correlation with DFI (0.74±0.09), indicating that selection based on RFI will reduce the feed intake without any negative effects on the size and growth. The estimated genetic parameters for FE traits suggested the possibility to implement genetic/genomic selection to improve the FE in American mink. Consequently, the ongoing project on genetic mapping and genomic selection will enhance the knowledge of FE and improve the efficacy of selection for more feed-efficient mink.
501 Late-Breaking: Search for Genomic Associations with Cryo-resistance of Sperm in Animals
Authors: Nikitkina Elena , Dementieva Natalia , Shcherbakov Yuyriy , Krutikova Anna , Musidray Artem , Pleshanov Nikolay , Samoylov Oleg 
Published: October 2021

Abstract
Genetic resistance to sperm cryopreservation is an important factor in the sires selection. In our study, we studied the cryoresistance of the sperm in Gallus gallus, Bos taurus, Equus ferus caballus. Sperm quality was assessed using computer-assisted semen analysis (CASA). Genetic studies were performed using Affymetrix Axiom®600k Array chips for cocks, Affymetrix Axiom®600k Array for stallions and BovineSNP50 for bulls. For the analysis of genotypes, the GWAS (Genome-Wide Association Studies) was used. The calculations were carried out using the PLINK and EMMAX programs. The candidate genes found as a result of our work were compared with the functions of orthologous genes, according to the publications of other researchers. As a result of comparing different animal species, a common candidate gene was found, localized on chromosome 3 (Gallus gallus) and on BTA9 (Bos taurus) BCKDHB gene encodes the beta subunit E1 of branched chain keto acid dehydrogenase, which is a multienzyme complex associated with the inner membrane of mitochondria. In roosters, it was associated with the preservation of sperm motility after cryopreservation, and it was found in the analysis of the integrity of the sperm head in bulls. The functioning of genes associated with meiosis and sperm maturation is an important condition for maintaining its quality after freezing. The work of genes responsible for the formation of membranes and various cellular structures associated with the functions of spermatozoa is also an important factor in cryoresistance. Another important condition for the functioning of the sperm is the expression of genes that prepare the sperm for fertilization. These are calcium metabolism genes involved in preparation for capacitation. Changes in the structure of proteins can lead to premature capacitation and disruption of sperm integrity. The found molecular genetic markers will be used to select sires whose sperm are suitable for freezing. Project of RSF № 18-16-00071.
43 Single and Multiple-breed Genomic Predictions for Conformation Traits of Canadian Dairy Goats
Authors: Massender Erin , Brito Luiz F , Maignel Laurence , Oliveira Hinayah R , Jafarikia Mohsen , Baes Christine F , Sullivan Brian , Schenkel Flavio S 
Published: October 2021

Abstract
The use of multiple-breed models can increase the accuracy of estimated breeding values (EBV) when few phenotypes are available for a trait. However, pooling breeds is not always beneficial for genomic evaluations due to the low consistency of gametic phase between individual breeds. The objective of this study was to compare the expected gain in accuracy of single-step genomic breeding values (GEBV) for conformation traits of Canadian Alpine and Saanen goats predicted using single and multiple-breed models. The traits considered were body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats, all recorded by trained classifiers, using a 1 to 9 scale. The full datasets included a total of 7,500 phenotypes for each trait (5,158 Alpine and 2,342 Saanen) and 1,707 50K genotypes (833 Alpine, 874 Saanen). Standard errors of prediction (SEP) were obtained for EBV and GEBV predicted using single-trait animal models on full or validation datasets. Breed difference was accounted for as a fixed effect in the multiple-breed models. Average theoretical accuracies were calculated from the SEP. For Saanen, with fewer records, expected accuracies of EBV and GEBV for the validation animals (selection candidates) were consistently higher for the multiple-breed models. Trait specific gains in theoretical accuracy of GEBV relative to EBV for the selection candidates ranged from 30 to 48% for Alpine and 41 to 61% for Saanen. Averaged across all traits, GEBV predicted from the full dataset were 32 to 38% more accurate than EBV for genotyped animals and the largest gains were found for does without conformation records (49 to 55%) and bucks without daughter records (56 to 82%). Overall, the implementation of genomic selection would substantially increase selection accuracy for young breeding candidates and, consequently, the rate of genetic improvement for conformation traits in Canadian dairy goats.
53 Awardee Talk: Implications of the Gut Microbiome for Genetic Improvement of Swine
Authors: Maltecca Christian , Tiezzi Francesco 
Published: October 2021

Abstract
With the universal adoption of genomic selection as a breeding standard, livestock farming is about to enter a new chapter in which deep phenotyping and multi-omics technologies will enhance the use of molecular data in the selection process. Microbiome composition represents a promising tool in this arena, serving simultaneously as a benchmark of environmental challenge, a predictor of animal physiological status, and a direct target for host selection. Our group has been researching the application of the gut microbiome in swine production, with a particular focus on growth and feed efficiency. We investigated how the microbiome is shaped in different production systems by comparing the microbiome composition of growing/finishing pigs raised in bio-secure nucleus farms and commercial facilities. Results suggested a strong impact of gut microbiome composition on pork production efficiency and a consistent effect of several microbial features across different systems. We found that microbial features associated with animal growth are heritable and identified host genomic markers contributing to their relative abundance. We have found that the microbiota composition is different across breeds but stable within breeds (Duroc, Large White, Landrace). Taxa differently represented among the breeds are also associated with feed efficiency and behavior. We estimated the ‘microbiability’ of meat and carcass quality traits. We found that the gut microbiome composition leaves an identifiable ‘footprint’ on the animal tissue deposition. Following these findings, we elucidated the complex relationship between genomic and microbial variation using structural equation modeling to test a causal path between the host genotype, the gut microbiome composition, and fat deposition. We identified direct and mediated (through microbiome) genomic variants affecting host trait expression, highlighting the potential for the ‘second genome’ to contribute to the recovery of genetic variance.
51 Opportunities to Apply and Learn from Deep Phenotyping in Dairy Cattle
Authors: Koltes James E 
Published: October 2021

Abstract
Data from automated systems including sensors, cameras and high-throughput assays, such as milk testing, generate huge amounts of valuable data for research and on-farm applications. The potential to use data collected from automated systems in real-time for predictive analytics or as indicator traits for genetic selection is an exciting opportunity for the dairy industry to address difficult phenotypes. While high frequency recording of data provides high-resolution views of animal behavior, it also creates logistical challenges for determining what data are most informative, when or if to use repeated measures, and how to integrate these repeated measurements with static or less frequently measured traits of interest. Measuring the precision and accuracy of data from automated systems is also a continued challenge. As feed is the largest cost on commercial dairy farms and intake is difficult to measure, our lab is investigating the use of different wearable and stationary sensors to monitor cow and environmental level measurements to determine their utility as potential indicators of variation in feed intake. Associations of sensor measures with feed intake and relationships with health events have been identified. Ongoing research is evaluating the ability to predict trait phenotypes with various sensor data. Beyond the potential to learning more about trait relationships and underlaying genetics of behavior, there is also the potential to develop precision management tools from sensors for producers. Integrating automated systems measurements with research data (e.g. omics) may help to fuel new discoveries and tool development. Future advances in sensing technologies will require transdisciplinary research to develop new types of sensor measures and data analytics to identify hidden information within the data that may lead to new actionable applications on farm.
66 Blood MicroRNAomes Revealed Signatures of Lameness Phenotypes in Feedlot Cattle
Authors: Li Wentao , O’Hara Eóin , Sun Hui-Zeng , Schwartzkopf-Genswein Karen S , Guan Leluo 
Published: October 2021

Abstract
Lameness is a significant health issue in Canadian feedlots resulting in substantial economic losses. However, the high frequency of misdiagnosis of lameness using traditional methods leads to ineffective treatment, suggesting a new diagnostic method is needed. Growing evidence indicates that microRNAs (miRNAs) can be used as biomarkers for identifying the animals’ physiological status and the diagnosis of certain diseases, but this approach has not been utilized in beef cattle. The objective of this study was to compare blood miRNA profiles between lame and healthy cattle to investigate the relationship between miRNA expression patterns and specific lameness phenotypes. Blood samples were collected from 156 feedlot cattle at 0, 1, 2 and 3 weeks after being diagnosed with either digital dermatitis (DD; n=62), toe tip necrosis syndrome (TTNS; n = 40), or footrot (FR; n = 40) and healthy controls (HC; n = 12) for miRNA libraries construction and sequencing. A total of 314 expressed miRNAs were identified in 89 blood samples collected at week 0 across all groups, with TTN having the largest number of expressed miRNAs (291, P &lt; 0.01) compared to all other groups (HC=276, DD=281, FR=278). Although miRNA profiles did not differ among the lameness types, type-specific miRNAs were identified; 6 in DD, 10 in TTN, 5 in FR and 7 in HC cattle. In addition, 3, 6 and 7 DE miRNAs were detected in DD, TTNS and FR when compared with HC cattle. Most of the DE and group-specific miRNAs are related to inflammation and skin diseases. The DE miRNAs were different between week 0 and all other weeks, indicating miRNA profiles may differ over time and with disease progression and recovery. These findings provide an initial understanding of the relationship between the cattle blood miRNAome and lameness and suggest that miRNA expression holds promise in the discovery of novel biomarkers for identifying lameness.
85 Integrating Mechanistic Models with AI for Precision Feeding of Sows
Authors: Gaillard Charlotte , Gauthier Raphaël , Dourmad Jean-Yves 
Published: October 2021

Abstract
Conventional feeding for sows is usually based on the average herd’s nutrient requirements. Thus, sows can be under- or over- fed leading to extra feed costs and environmental losses. New technologies, as sensors and AI, bring opportunities to measure and integrate individual variability into nutrient requirements estimations. The objective is therefore to go towards precision feeding (PF) combining on-farm data as input for a dynamic nutritional model with smart feeders to provide individual and daily-adjusted rations. As a first step, a mechanistic model (InraPorc) was upgraded and applied to databases to calculate daily nutrient requirements at the individual scale for sows. For lactating sows, it highlighted that milk production and appetite influenced the amount and composition of the optimal ration to be fed to each sow. For gestating sows, it showed that parity, gestation stage, and activity level influenced nutrient requirements. The second step was to develop algorithms to predict the parameters of interest defined in the first step and not measured daily on-farm. For lactating sows, feed intake and litter weight at weaning (as proxy for milk production) were accurately predicted using supervised methods: respectively, clustering k-shape and a linear regression. For gestating sows, an algorithm is being developed to identify individual activities via video recordings. The third step is to test on farm the decision support systems (DSS) composed of the models and algorithms. An interface allows the link between the DSS and the feeders, and another allows the farmers to enter observational data. During on-farm trials, nitrogen and phosphorus excretions as well as feed costs were reduced for sows fed with PF compared to sows fed a conventional diet. To conclude, AI allows mechanistic models and algorithms to be integrated on farm for sows for an on real-time individual adjustment of the nutrient supply.
80 A Brief Overview, Comparison and Practical Applications of Machine Learning Models
Authors: Tulpan Dan 
Published: October 2021

Abstract
This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this workshop is to provide the audience with a way to formulate a problem such that it will be solvable by machine learning techniques and apply an exploratory analysis of various machine learning algorithms on different datasets. The workshop is structured in a hands-on format and includes a brief overview of basic notions about machine learning, a description of relevant models and evaluation metrics followed by a practical session. The practical session requires each attendee to bring their own laptop and have already installed the Waikato Environment for Knowledge Analysis (Weka) workbench for machine learning available from https://www.cs.waikato.ac.nz/ml/weka/ and all freely available machine learning models. The Weka installation of freely available machine learning models can be achieved by using the Weka Package Manager available from the Tools menu in the main application. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2021_mlworkshop/
91 Assessing Variability of Herd Sire Economic Value for Commercial Operations
Authors: Dodd Logan , Anderson David , Riley David G , Johnson Barton , Herring Andy D 
Published: October 2021

Abstract
Number of calves (n = 596) and weaning calf value (n = 574) from natural service, multi-sire breeding groups were evaluated in a crossbred research herd. Sires and dams that were F1 crosses of Angus (A) and Nellore (N) were mated annually during 60 to 90-day breeding seasons to produce spring-born calves in 2009–2015. Numbers of sires used annually varied from 5 to 9; numbers of females exposed per bull ranged from 14 to 22 across years. Bulls were pastured together throughout the year as well as breeding seasons. Sires were identified based on calf DNA genotyping. Calf number, birth date, birth weight, weaning weight, and economic value were determined per bull annually. Calf value was based on weaning weight and reported prices for respective weaning dates. Calf prices were calculated separately for steers and heifers relative to 22.7 kg (50 lb) increments for corresponding weight class and year. Mixed model analyses were conducted that included fixed categorical effects of calf birth year, type of F1 sire (A-sired vs. N-sired), sire nested within type, calf sex, and the interaction of F1 sire type with calf sex. Covariates of Julian birth date and calf weaning age were included for birth weight and weaning weight, respectively. Large differences in calf numbers and performance (P &lt; 0.05) were observed. Weaned calves produced annually per sire ranged from 0 to 48. Average annual calf performance per sire ranged from 28.4 to 50.8 kg for birth weight and 146.5 to 249.0 kg for weaning weight. Annual economic value per sire ranged from $0 to $30,870 when considering half of each calf’s value as attributed to the sire. Commercial producers should consider potential sire variability for calf numbers, birth date distribution, and ratio of female-to-male calves in combination with calf performance for economic assessments.