bluuebunny/crossref_metadata_2025_split huggingface dataset:
2 Measuring the Impact of Heat Stress Abatement Strategies on Fecal Microbiome in Finishing Steers During the Summer Months in the Southeast United States
Authors: Hernandez Shane R , Stelzleni Alexander M , Sims Wil , Davis Dylan B , Lourenco Jeferson M , Stewart R Lawton 
Published: October 2021

Abstract
This study evaluated the effect of heat stress abatement strategies on the faecal microbiome of finishing beef steers in the Southeastern United States. The study was designed as a completely randomized block using 32 steers (BW 453k 4 kg) stratified by weight and randomly assigned to one of four treatment groups: covered with fans (CWF), covered no fans (CNF), outside with shade (SHADE), and outside without shade (OUT). All steers were housed outside from June to July and were brought up on a common feedlot ration. After-which steers were placed into treatments on d -10 and covered steers were given 10 days to acclimate Calan-gate system. During the acclimation phase, both covered and outside steers were brought up at a similar rate. Steers were weighed and faecal samples were collected on d 0, 50, and 85. Microbial DNA was extracted from the samples using a combination of mechanical and enzymatic processes, and the DNA sequences were analyzed using QIIME v2.0. There was a treatment x day interaction for Actinobacteriota, Bacteroidota, and Patescibacteria phyla. Additionally, there was a day effect for all three phyla (P &lt; 0.001), but there was only a treatment effect (P &lt; 0.001) for Patescibacteria. There was not an interaction for Fibrobacterota, Firmicutes, or Proteobacteria (P &gt; 0.226), but there was a day effect (P &lt; 0.019) for all three phyla. There was a treatment effect (P = 0.036) for Fibrobacterota, however there was not a treatment effect for Firmicutes and Proteobacteria (P &gt; 0.394). Moreover, there was no interaction, treatment, or day effect (P &gt; 0.39) for the Firmicutes to Bacteroidota ratio. These results suggest that stress abatement strategies may influence the fecal microbiome over time, and they might help better understand how stress affects the microbiome and nutrient digestibility in the lower gut.
17 Genome-wide Association Study for Heat Stress Tolerance in Swine Using Weighted Single-step GBLUP
Authors: Dodd Gabriella R , Fragomeni Breno O , Gray Kent A , Huang Yijian 
Published: October 2021

Abstract
The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine as well as analyze the accuracy of prediction. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri. Individuals were a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8,232 animals with 33,581 SNP. The pedigree file contained 553,448 animals. A 78 on the Temperature Humidity Index (THI) was used as a threshold for heat stress. A two-trait analysis was used with the phenotypes heat stress (trait one) and non-heat stress (trait two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for trait one and two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The GEBV of complete data was used as the benchmark, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies, both methods showed a maximum accuracy of 0.32 for trait one and 0.54 for trait two. Manhattan Plots for trait one, trait two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining around 1% of the genetic variance were identified. The only difference between the two traits was a peak at chromosome 14. The genetic correlation suggests different mechanisms for growth under heat stress. The GWAS results show that both traits are highly polygenic, with few genomic regions explaining more than 1% of variance.
18 Genetic and Phenotypic Parameters for Aleutian Disease Tests and Their Correlations with Pelt Quality, Reproductive Performance, Packed-cell Volume, and Harvest Length in Mink
Authors: Hu Guoyu , Ngoc Do Duy , Karimi Karim , Miar Younes 
Published: October 2021

Abstract
Aleutian disease (AD) is an untreatable immune complex disease in mink and brings tremendous economic losses to the mink industry globally. The ineffectiveness of culling, immunoprophylaxis, and medical treatment in controlling AD have urged mink farmers to select AD-resilient mink based on the AD tests. However, the genetic analysis of these tests and their correlations with AD-resilient traits have not been investigated. In this study, data on 5,824 mink were used to estimate the genetic and phenotypic parameters of four AD tests, including two systems of enzyme-linked immunosorbent assay (ELISA), counterimmunoelectrophoresis (CIEP), and iodine agglutination test (IAT), and their genetic and phenotypic correlations with pelt quality, reproductive performance, packed-cell volume (PCV), and harvest length (HL). Significance (P &lt; 0.05) of fixed effects (sex, year, color type, the number of mating, and dam age), covariates (age at blood sampling and age at harvest), and random effects (additive genetic, permanent environmental, and maternal effects) were determined using univariate models. The genetic and phenotypic parameters for all traits were estimated under bivariate models using ASReml 4.1. Estimated heritabilities (±SE) were 0.39±0.05, 0.61±0.07, 0.11±0.07, and 0.26±0.05 for antigen-based ELISA (ELISA-G), virus capsid protein-based ELISA, CIEP, and IAT, respectively. The ELISA-G showed moderate repeatability (0.58±0.04) and significant (P &lt; 0.05) negative genetic correlations (±SE) with reproductive performance traits (from -0.41±0.16 to -0.49±0.12), PCV (-0.53±0.09), and HL (-0.45±0.16). These results indicated that the selection of mink with a lower ELISA-G score could not only decrease the anti-AMDV antibody level and the extent of anemia but also improve the female reproductive performance and the harvest length of mink without causing adverse influences on the pelt quality. Hence, ELISA-G could be applied as an indicator for genetic selection of AD-resilient mink and help mink farmers reduce the adverse effects of AD.
25 Gut Microbiome Information Enables Additional Discovery in Genome-wide Association Studies in Swine
Authors: Tiezzi Francesco , Maltecca Christian 
Published: October 2021

Abstract
Several studies have highlighted the relevance of gut microbiome composition in shaping fat deposition in mammals. In contrast, other studies have highlighted how the host genome can control the abundance of individual species in the gut microbiota’s make-up. There is the need to incorporate the different ‘-omics’ data (host genome, gut microbiome, high-throughput phenotyping) in a model that allows to extract information beyond the simple sum of each component’s contribution. We propose a systematic approach to detect host genomic variants that control the gut microbiome, which in turn contributes to the host fat deposition, when this latter is based on multiple phenotypic measures. Using a dataset that included longitudinal records of fat deposition on 1,180 pigs, we implemented a mediation test to describe how fat deposition in swine (Sus scrofa) is affected by the host genotype and the gut microbiome. The phenotypic outcome was described both by measured and latent variables, taking advantage of structural equation modeling. We also implemented a ‘traditional’ genome-wide association analysis, testing the (total) effect of host genomic variants on the phenotype. Results for all models were validated using both bootstrapping and permutation tests. The models identified several host genomic features having microbiome-mediated effects on fat deposition. Our work demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. The host genomic features identified through the mediation analysis do not entirely overlap the group of features identified by traditional GWAS. Microbiome-mediated analyses can help understand the genetic determination of complex phenotypes. The host genomic features that exert a mediated effect could not be identified by traditional genome-wide association analysis. These can contribute to filling the missing heritability gap and provide further insights into the host genome – gut microbiome interplay.
19 Comparative Evaluation of Human-edible Animal Products Derived from Offspring of Genome Edited and Control Cattle
Authors: Trott Josehine F , Young Amy E , McNabb Bret R , Yang Xiang , Bishop Thomas F , Van Eenennaam Alison L 
Published: October 2021

Abstract
Risk assessments of genetically modified (GM) animals include evaluating whether milk and meat derived from the GM animal is as nutritious to humans and/or animals as traditionally-bred animals. The underlying assumption of this comparative approach is that traditionally-bred animals have a well-established history of safe use. The goal of this project was to provide empirical data on the development and nutritional composition of animal products derived from the offspring of a genome edited dairy bull, homozygous for the dominant PC Celtic POLLED allele. The bull was crossed with horned Hereford (HH) cows (pp) to obtain five male and one female heterozygous hornless (RC) calves. Hereford, Angus and Holstein bulls were also bred to Hereford cows to produce five HH calves, two polled Angus/Hereford (AH) calves, and three horned Holstein/Hereford (HO) calves (with 25% genetic identity to the hornless offspring), respectively as contemporary controls. Weights were recorded at 8 months, 1 year and slaughter (n = 9–16). Following calving, individual quarters of the RC heifer (RC.calf1), one HO heifer (HO.calf2), two horned HH heifers, and one contemporaneous hornless AH cow (3113) were milked at varying timepoints during the first seven weeks of lactation. Meat samples were collected from the 6 hornless RC calves and 3 horned HO comparator offspring after slaughter. Proximate analysis was conducted for meat and milk samples. Cattle that inherited a POLLED allele from the genome-edited bull showed no differences in their overall growth (Figure 1) or meat composition (Figure 2) when compared to contemporary controls. Milk composition varied between animals and by days in milk (DIM), although values were within the normal range reported in peer-reviewed literature (Figure 3). Bovine milk composition is known to be influenced by breed, nutrition, parity, and DIM, making it difficult to obtain appropriate comparators in experiments with a limited number of GM animals.
21 Unravelling Transmission Ratio Distortion Across the Genome of a Crossbreed Beef Cattle Population
Authors: Becker Scalez Daiane C , Id-Lahoucine Samir , Fonseca Pablo A S , Casellas Joaquim , Cánovas Angela 
Published: October 2021

Abstract
Transmission ratio distortion (TRD) is a process when one allele from either parent is preferentially transmitted to the offspring. The identification of genomic regions affected by TRD might help in the detection of lethal alleles or potential genes affecting reproduction. Here, we investigated TRD in crossbreed beef cattle population aiming to identify genomic regions showing altered deviations in segregation that could be affecting reproduction performance. A total of 237 genotyped animals were used including 46 sires, 80 dams, and 111 parent-offspring (trios). The predominant breeds of these animals were Angus (61.83%), Simmental (18.99%), Gelbvieh (6.12%), Charolais (3.65%), Hereford (2.46%) and Limousin (1.57%). After excluding SNPs with minor allele frequency lower than 0.05 and call-rate lower than 0.90, a total of 369,902 autosomal SNPs were retained for further analyses. The SNP-by-SNP analysis was performed within a Bayesian framework using TRDscanv.2.0 software, using 100,000 iterations, with 10,000 iterations being discarded as burn-in. As table 1 shows, 33 SNPs were identified with TRD, considering a Bayes Factor (BF)≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. Among them, 26 SNPs were parent-unspecific and 7 SNPs were parent-specific TRD. For parent-specific TRD, 214 were identified for sire- and 162 for dam-TRD (BF≥100). Among them, 4 SNPs were detected with sire- and dam-TRD in opposite direction of preference of transmission. Preliminary functional and positional analysis was performed using the list of TRD regions with BF≥100 and the approximate empirical null distribution of TRD at 0.01% margin error. For sire-TRD, 14% of the identified QTL (n = 254) were related to non-return rate. For dam-TRD, 21 regions related to conception rate were found (1.5%) and 13 regions related to stillbirth (0.93%). Haplotype analysis is in progress to identify additional candidate regions and alleles with TRD to better understand this phenomenon in a crossbreed beef population.
22 Characterization of tRNA Expression Profiles in Large Offspring Syndrome
Authors: Goldkamp Anna K , Li Yahan , Rivera Rocio M , Hagen Darren 
Published: October 2021

Abstract
Differentially methylated regions (DMRs) have been associated with Large Offspring Syndrome (LOS) in cattle. Some DMRs overlap transfer RNA (tRNA) gene clusters, potentially altering tRNA expression patterns uniquely by treatment group or tissue type. tRNAs are classified as adapter molecules, serving a key role in the translational machinery implementing genetic code. Variation in tRNA expression has been identified in several disease pathways suggesting an important role in the regulation of biological processes. tRNAs also serve as a source of small non-coding RNAs. To better understand the role of tRNA expression in LOS, total RNA was extracted from skeletal muscle and liver of 105-day fetuses and the tRNAs sequenced. Although there are nearly three times the number of tRNA genes in cattle as compared to human (1,659 vs 597), there is a shared occurrence of transcriptionally silent tRNA genes in both species. This study detected expression of 474 and 487 bovine tRNA genes in skeletal muscle and liver, respectively, with the remainder being very lowly expressed or transcriptionally silent. Eleven tRNA isodecoders are transcriptionally silent in both skeletal muscle and liver and another isodecoder is silent in the liver (SerGGA). Further, the highest expressed isodecoders differ by treatment or tissue type with roughly half correlated to codon frequency. While the absence of certain isodecoders may be relieved by wobble base pairing, missing tRNA species could likely increase the likelihood of mistranslation or mRNA degradation. Differential expression of tissue- and treatment-specific tRNA genes may modulate translation during protein homeostasis or cellular stress, altering regulatory products targeting genes associated with overgrowth in skeletal muscle and/or tumor development in the liver of LOS individuals.
30 Calf Performance and Sex Ratios in Second Generation Reciprocal Nellore-angus Crosses
Authors: Mickey Dana , Sanders James , Riley David G , Herring Andy D 
Published: October 2021

Abstract
The objectives of this study were to analyze calf weight and sex distribution differences in reciprocal F2 Nellore-Angus calves (n = 559) born 2010–2015 and calves out of these F2 females through 4 yr age (n = 469) born 2012–2018. The F2 animals were produced by 4 F1 parent type combinations from Nellore-sired (NA) vs. Angus-sired (AN) sires and dams. Weight traits were analyzed using mixed models, and sex distribution was analyzed through PROC GLIMMIX in SAS. Sire type and dam type affected birth weight (P &lt; 0.05) but not weaning weight of the F2 calves. However, the interaction between sire type and dam type accounted for weaning weight differences (P = 0.038). Birth weight averaged 3.5 kg heavier from NA vs. AN sires, and 2.4 kg heavier from NA vs. AN dams. Weaning weight displayed a different pattern than observed in birth weight with the heaviest calves being NAAN (224.5 ± 3.7 kg) and lightest being ANAN (215.9 ± 3.9 kg). Sire type accounted for important variation (P = 0.036) in the calf sex distribution at birth among F2 calves. Percentage males at birth exceeded the expected ratio for NA-sired calves (66.6%, n = 168) but not for AN-sired calves (49.9%, n = 105). The sire and dam type main effects in the F2 females did not influence weight traits or sex distribution in their calves (from predominantly Angus sires). However, the interaction of sire type x dam type x sex showed a trend in birth weight (P = 0.080) and importance for weaning weight (P = 0.010) where only from ANNA and NANA dams were males heavier than females. These results suggest that some of the calf weight reciprocal differences typically observed among F1 Bos indicus-Bos taurus crosses can persist in later generations.
27 Machine Learning Algorithms Based on Haplotype Libraries for Classification of Stillbirth Susceptibility in Holstein Cows
Authors: Fonseca Pablo A S , Tornatore Massimo , Cánovas Angela 
Published: October 2021

Abstract
Reduced fertility is one of the main causes of economic losses in dairy farms. The cost of a stillbirth is estimated in US$ 938 per case in Holstein herds. Machine learning (ML) is gaining popularity in the livestock sector as a mean to identify hidden patterns and due to its potential to address dimensionality problems. Here we investigate the application of ML algorithms for the prediction of cows with higher stillbirth susceptibility in two scenarios: cows with &gt;25% and &gt;33.33% of stillbirths among birth records. These thresholds correspond to percentiles 75 (still_75) and 90 (still_90), respectively. A total of 10,570 cows and 50,541 birth records were collected to perform a haplotype-based genome-wide association study. Five-hundred significant pseudo single nucleotide polymorphisms (pseudo-SNPs) (False-Discovery Rate&lt; 0.05) were used as input features of ML-based predictions to determine if the cow is in the top-75 and top-90 percentiles. Table 1 shows the classification performance of the investigated ML and linear models. The ML models outperformed linear models for both thresholds. In general, still_75 showed higher F1 values compared to still_90, suggesting a lower misclassification ratio when a less stringent threshold is used. We observe that accuracy of the models in our study is higher when compared to ML-based prediction accuracies in other breeds, e.g. compared to the accuracies of 0.46 and 0.67 that were achieved using SNPs for body weight in Brahman and fertility traits in Nellore, respectively. Xgboost algorithm shows the highest balanced accuracy (BA; 0.625), F1-score (0.588) and area under the curve (AUC; 0.688), suggesting that xgboost can achieve the highest predictive performance and the lowest difference in misclassification ratio between classes. The ML applied over haplotype libraries is an interesting approach for the detection of animals with higher susceptibility to stillbirths due to highest predictive accuracy and relatively lower misclassification ratio.
33 Impact of Including the Cause of Missing Records on Genetic Evaluations for Growth in Commercial Pigs
Authors: Hollifield Mary Kate , Lourenco Daniela , Tsuruta Shogo , Bermann Matias , Howard Jeremy T , Misztal Ignacy 
Published: October 2021

Abstract
It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.
34 Dimensionality of Genomic Information and Its Impact on GWA and Variant Selection: A Simulation Study
Authors: Jang Sungbong , Tsuruta Shogo , Leite Natalia , Misztal Ignacy , Lourenco Daniela 
Published: October 2021

Abstract
The ability to identify true-positive variants increases as more genotyped animals are available. Although thousands of animals can be genotyped, the dimensionality of the genomic information is limited. Therefore, there is a certain number of animals that represent all chromosome segments (Me) segregating in the population. The number of Me can be approximated from the eigenvalue decomposition of the genomic relationship matrix (G). Thus, the limited dimensionality may help to identify the number of animals to be used in genome-wide association (GWA). The first objective of this study was to examine different discovery set sizes for GWA, with set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in G. Additionally, we investigated the impact of incorporating variants selected from different set sizes to regular SNP chip used for genomic prediction. Sequence data were simulated that contained 500k SNP and 2k QTL, where the genetic variance was fully explained by QTL. The GWA was conducted using the number of genotyped animals equal to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99 percent of the variance in G. Significant SNP had a p-value lower than 0.05 with Bonferroni correction. Further, SNP with the largest effect size (top10, 100, 500, 1k, 2k, and 4k) were also selected to be incorporated into the 50k regular chip. Genomic predictions using the 50k combined with selected SNP were conducted using single-step GBLUP (ssGBLUP). Using the number of animals corresponding to at least EIG98 enabled the identification of the largest effect size QTL. The greatest accuracy of prediction was obtained when the top 2k SNP was combined to the 50k chip. The dimensionality of genomic information should be taken into account for variant selection in GWAS.