bluuebunny/crossref_metadata_2025_split huggingface dataset:
83 Genotype-by-environment interaction with high-dimensional environmental data: an example in pigs
Authors: Bussiman Fernando , Lourenco Daniela , Chen Ching-Yi , Holl Justin W , Misztal Ignacy , Vitezica Zulma G 
Published: September 2024

Abstract
This study investigated using environmental covariates to model correlated herd effects and genotype-by-environment interaction (GE), as well as their impact on the prediction accuracy of genomic evaluations in pigs. We used air, dew/frost, wet-bulb, and earth-skin temperatures, relative humidity, rainfall, wind speed and direction. Average daily gain (ADG) and backfat thickness (BFT) from a terminal pig line were analyzed by 6 different models: MG, which included herd as an uncorrelated random effect; ME30, which considered 30 d of daily weather information to correlate herd effects; and ME100, which used 100 d of daily climate covariates; MGE30, same as ME30 but also considering the GE as the Hadamard product of the genomic relationship matrix and the environmental covariates matrix; MGE100, same as ME100 but including the GE; and MTM, a traditional multiple-trait model with each herd as a different trait. All animals were genotyped and phenotyped; therefore, the statistical model of choice was GBLUP. Validation was based on LR method, and focal animals were those born in the last year. Accuracy, bias, and dispersion did not change among models, except for MTM. Comparing all models against MTM, the accuracy increased by 38% for BFT, whereas only slightly increased for ADG. Among all 11 herds, only 4 showed a genetic correlation stronger than 0.80 for BFT, while this number was much greater for ADG. When the model has a variable accounting for environmental effects like herd in the case of MG, using weather information to correlate environments or adding the GE term has little benefit for genomic predictions.
87 Estimation of genetic parameters for activity levels in North American Holstein cattle
Authors: Moulder Caitlin N , Maskal Jacob M , Kalbaugh Kirby , Pacheco Hendyel , Boerman Jacquelyn , Brito Luiz F 
Published: September 2024

Abstract
High-producing dairy cows face various challenges during their productive life, including metabolic and infectious diseases, heat stress, and intensive management practices. Therefore, dairy cattle breeding programs have begun to focus on novel traits related to the overall resilience of the cows. Resilience has been defined as the ability of an animal to be minimally affected or to recover quickly from environmental disturbances while maintaining a certain level of productivity. In dairy cattle, resilience can be evaluated using measurements of longitudinal indicator traits, such as activity level, which are repeatedly measured throughout the lifetime of the animal. Variability in activity (defined in this study as average steps per hour and daily steps taken) might be a key indicator of resilience, due to activity of contemporary group animals deviating from normal behavior in less resilient cattle. Before resilience indicators are defined based on variability in certain longitudinal traits, there is a need for evaluating the genetic background of the source variable. Therefore, the primary objective of this study was to estimate variance components and genetic parameters of activity traits in lactating Holstein cows. The data was collected from a large dairy farm located in Indiana, using milking systems. Records from 6,332 Holstein cows for the traits of average activity per hour and number of daily steps were used in the analyses. The models used for the estimation of variance components included the significant (P &lt; 0.05) fixed effects of contemporary group defined based on the concatenation of calving year and season, parity number (1, 2, 3+), pregnancy group (0 mo, 1-3 mo, 4-6 mo, and 7-9 mo), and days in milk (2-wkclasses), and the random effects of direct additive genetic, permanent environment, and residual effects. Genetic parameters were estimated for average activity per hour and daily steps for all parities together. Heritability estimates were calculated based on single-trait repeatability models and the (AI) REML method implemented in the BLUPF90+ software. Daily steps taken had a heritability (± SE) of 0.05 (± 0.02) and a repeatability of 0.29 (± 0.02). Activity per hour had a heritability estimate of 0.12 (± 0.04) and a repeatability of 0.57 (± 0.01). These results indicate that activity level is heritable in North American Holstein cattle and can be added to selection indexes if proven to be a key auxiliary trait for improving dairy cattle sustainability. As a next step, we will investigate the genetic relationship of activity traits with other relevant traits and derive resilience indicators based on variability in activity level.
115 Confidence intervals for validation statistics with data truncation in genomic prediction
Authors: Bermann Matias , Legarra Andres , Munera Alejandra Alvarez , Misztal Ignacy , Lourenco Daniela 
Published: September 2024

Abstract
Validation by data truncation is a common practice in genetic evaluations because of the interest in predicting the genetic merit of a set of young selection candidates. Two of the most used validation methods in genetic evaluations are predictivity (correlation between pre-adjusted phenotypes and EBV) divided by the square root of the heritability) and the linear regression (LR) method. Both methods compare predictions with the whole and partial datasets obtained by removing the information related to a set of validation individuals. Confidence intervals (CI) for predictivity and the LR method can be obtained by k-fold validation or bootstrapping. Analytical or frequentist CI are unavailable for predictivity and the LR method and would be beneficial to avoid running several validations. The analytical CI can also help test the quality of bootstrap intervals. This study aimed to derive analytical CI for predictivity, and statistics included in the LR method (bias, dispersion, ratio of accuracies, and reliability). The CI for the bias, dispersion, and reliability depends on the (co)variances of the EBV across the individuals in the validation set. The CI for the ratio of accuracies and predictivity were obtained through the Fisher transformation. We showed the adequacy of the analytical CI using simulation. The analytical CI were closer to the simulated ones. Bootstrap CI tend to be narrower than the simulated ones. Estimating the sampling variation of predictivity and the statistics in the LR method without replication or bootstrap is possible for any dataset with the method proposed in this study. All the formulas derived in this study were implemented in a new program belonging to the BLUPF90 suite called Validationf90.
116 Contribution of X-chromosome to the genetic variance of litter size and stillbirth in pigs
Authors: Veroneze Renata , Chen Ching-Yi , Holl Justin W , Misztal Ignacy , Bermann Matias , Lourenco Daniela 
Published: September 2024

Abstract
Genes with important roles in reproduction traits have been described in pig X-chromosome (SSCX), which may contribute considerably to genetic variance and could impact the accuracy of genomic prediction. The phenotypic data set includes information on 82,580 litter size records from 34,346 sows. Additionally, there were 108,006 in the pedigree, and genotypes for 41,492 SNPs, of which 1,435 in SSCX, were available for 30,000 animals. Three different approaches were used to estimate variance components for the total number born and number of stillborn. In the first approach, only SNPs in the autosomes were used and the model included the additive genetic (u), permanent environment (p), and residual (e) random effects. In the second approach, the same model was adopted; however, the data included SNPs in the autosomes and in the X chromosome. For both models, it was assumed that u~N(0,H⊗Vu), e~N(0,I⊗Ve) and p~N(0,I⊗Vp), wherein H is a relationship matrix that combines pedigree and genomic information; and, Vu, Vp, and Ve are the (co)variance matrices for the random additive genetic, permanent environment, and residual effects, respectively. In the third approach, the random additive genetic effects associated with the X-chromosome (s) were included in the model described before. Then, it was assumed that s~N(0,Hx⊗Vs), where Hx is the genomic relationship matrix for the X-chromosome and Vs is the additive genetic variance associated with the SSCX. All analyses were carried out using GIBBSF90+. Table 1 shows variance components for the three approaches. For both traits, additive genetic variance and heritabilities were similar across the approaches. The additive genetic variance for the SSCX was small, accounting for approximately two percent of the total additive genetic variance. Including the X chromosome as a separate effect and using Hx as a covariance matrix captured only a small part of the additive genetic variance. The additive variance across approaches did not differ. For total number born and number of stillborn, using the SSCX SNPs to build Hx did not provide extra benefit compared with fitting those SNP in the regular H matrix.
198 Discovery of key functional SNP markers associated with feed efficiency in beef cattle
Authors: Lam Stephanie , Guan Leluo , Plastow Graham , Cánovas Ángela 
Published: September 2024

Abstract
Beef production contributes to approximately 2.4% of the total Greenhouse Gas emissions in Canada. Improving beef cattle feed efficiency (FE) may lead to improved energy partitioning and resource use, thereby improving the sustainability of the beef industry. Our objective was to identify differentially expressed key regulatory genes and associated novel functional SNP markers linked to FE in Canadian beef cattle using RNA-Sequencing (RNA-Seq) of rumen tissue collected from 48 beef cattle [n = 16 Angus, n = 16 Charolais, n = 16 Kinsella (Composite Hybrid including Angus, Charolais, Galloway, Hereford, Holstein, Brown Swiss, and Simmental)] selected for extreme FE phenotypes. In total, 11 key regulatory genes (MYH1, MYL2, MYLPF, TNNC2, EIF4B, RHOD, TCEANC, CKM, ENSBTAG00000040518, SERPINB2, and USP43) were significantly differentially expressed (DE) between extreme Residual Feed Intake (RFI) groups (low-RFI n = 8, high-RFI n = 8 per breed) using CLC Genomics Workbench (FDR &lt; 0.05; |FC| &gt;2). Using an optimized RNA-Seq variant calling pipeline using STAR and BCFtools, a total of [total (unique to low-RFI, unique to high-RFI)] 75 (36, 39), 78 (42, 36), and 53 (35, 18) uniquely fixed functional SNPs were located within coding regions of these 11 functional candidate genes, in low- and high-RFI animals in the Angus, Charolais, and Kinsella breeds, respectively. Considering all functional SNPs uniquely identified in low- or high-RFI groups for all breed comparisons, the majority of SNPs were identified in MYH1, EIF4B, and SERPINB2 genes, which function together in metabolically demanding biological processes (P &lt; 0.05) related to muscle contraction, muscle system, and muscle filament function processes. Additionally, EIF4B and SERPINB2 genes were found to have a role in signaling pathways that coordinate cell growth and immune function, respectively. Using RNA-Seq to identify key regulatory genes and associated functional SNPs linked to FE may uncover important genetic markers that influence the regulation of FE in beef cattle.
204 Changes in DNA methylation 5 days after exposure to acute heat stress in beef cattle skeletal muscle
Authors: Reith Rachel R , Gibbs Rachel , White Melanie R , Parrish Brooke L , Fuller Anna M , Schmidt Ty B , Yates Dustin T , Petersen Jessica L 
Published: September 2024

Abstract
The objective of this study was to investigate the effects of acute heat stress on the skeletal muscle epigenome using reduced representation bisulfite sequencing (RRBS). March-born crossbred (5/8 Red Angus, 3/8 Simmental) steers (n = 14; average body weight = 308.7 kg) were exposed to heat stress (HS) conditions of 35°C + 35% relative humidity (Temperature Humidity Index = 81) for 12h/d for 2 d (48 h). Longissimus dorsi samples were collected via biopsy and flash-frozen in liquid nitrogen immediately following the HS period at 48 h and from the contralateral longissimus dorsi 5 d later. DNA was isolated and underwent RRBS using 150 bp paired-end reads at an achieved average depth of 15.3 million reads/sample. Reads were trimmed and mapped to a bisulfite-converted bovine genome (ARSUCD1.2). Methylation calls were extracted and used to identify differentially methylated regions (DMRs) with methylkit in R comparing samples collected 5 d post-HS to those taken immediately post-48 h of HS. Differentially methylated regions (1,000 bp) had q &lt; 0.05 and at least a 15% difference in methylation. There were 1,015 DMRs in genes. Hypomethylated DMRs 5 d post-HS included IGF1R, IGF2, IGF2BP1, IGFBP4, IGF2R, IKBKB, and IL34. Hypermethylated DMRs 5 d post-HS included FGF5, FGFR1, and FGFR3. There were 81 DMRs in putative promoter regions (2000 bp 5’ of a gene). MYLK2, SIGIRR, and 25 other genes had hypomethylated promoter regions. Genes that contained DMRs were used as input for DAVID to explore KEGG pathways. Enriched pathways (P &lt; 0.1) included multiple hormone pathways, MAPK, PI3K-Akt, HIF-1, and VEGF. Hypomethylation of IGF-related genes and hypermethylation of FGF-related genes suggests impaired growth capacity post-HS, as does the enrichment of MAPK and PI3K-Akt pathways. Hypomethylation of SIGIRR’s promoter region, IL34 and IKBKB, indicates enhanced anti-inflammatory activity given their known regulation of inflammatory cytokine production. This, coupled with enrichment of the HIF-1 and VEGF pathways, suggests regulatory dampening of HS-induced oxidative stress. These findings reveal key regulatory changes in muscle growth, inflammatory response, and oxidative stress activity for cattle recovering from acute HS. Previous cattle skeletal muscle RNA studies predicted that HS altered metabolism, inflammation, and oxidative stress pathways, similar to the methylation results of this study. Overall, evidence suggests growth dysregulation, which may help explain observed HS-induced poor growth due to HS, as well as subsequent anti-inflammatory activity during recovery from HS.
341 Impact of genomic selection for growth on feet and leg structure in Angus cattle
Authors: Trujano Zuleica , Hidalgo Jorge , Retallick-Riley Kelli J , Garcia Andre , Lourenco Daniela , Misztal Ignacy 
Published: September 2024

Abstract
Historically fitness and production traits have shown an antagonistic relationship at the genetic level. Genetic selection mainly focused on growth traits (GT) might have influenced feet and leg structure traits (FT; recently added trait), causing changes over time. We aimed to examine genetic parameter changes in FT and GT over time in Angus cattle. Currently, genetic parameters are estimated using Variance Components Estimation (VCE), which has limitations in handling large datasets and is time demanding. To address these limitations, we explored the use of predictivity-based formulas to estimate heritability and genetic correlations in GT. The analysis was conducted using the blupf90+ family of programs. To estimate variance components (VCE), sampled data (2011-2022) from the American Angus population included 311K GT records [birth (BW), weaning weight (WW), and post-weaning gain (PWG)], ~70K FT records [foot claw set (CS) and foot angle (FA)], and 142K genotypes. FT are taken on the worst foot of the animal and can be scored multiple times throughout the life of the animal, but in this study, we did not include repeated records. The dataset was split into 5-yr intervals. Since FT ranged from 5 to 9, and 5 is ideal, a negative correlation is desired between FT and GT. The correlation FA-PWG, changed in an undesirable way over time, as it changed from -0.10 to 0.02 in the last interval. The correlation FA-WW is becoming more favorable over time, shifting from 0.05 to -0.15. The GT selection process shifted the correlations between CS and GT in the desired direction, changing from positive (~0.1) to almost zero in the more recent two intervals. To test the predictivity-based formulas, Genomic Estimated Breeding Values (GEBV) were estimated using GT data from 6.3M animals born from 2000 to 2022, and 900K genotyped animals. The data were divided into four slices, with a 2-yr validation period. Over time, heritabilities remained constant: ~0.3 for BW and WW, and ~0.2 for PWG. Correlations between GT also remained constant, approximately at 0.45 for BW-WW, 0.30 for BW-PWG, and 0.75 for WW-PWG. Although the formulas show promising results for heritabilities, interpreting the outcomes of genetic correlation estimation remains unclear, needing further tests. While certain correlations between FT and GT suggest a positive impact of genetic selection, some undesirable outcomes are realized. Probably due to the early FT genetic selection implementation (2019). This emphasizes the need for a multi-trait genomic selection approach to mitigate genetic antagonism. Adopting predictivity-based formulas reduces the time for genetic parameter estimation and allows the use of complete data instead of sampled data, as currently done in VCE.
309 Synergistic optimization: Utilizing genomic insights to enhance strategies for joint selective (deep) phenotyping and recursive creation of genomic reference populations - Sponsored by EAAP
Authors: Gengler Nicolas , Markey Alice , Lemal Pauline , Chen Yansen , Wilmot Hélène , Schroyen Martine 
Published: September 2024

Abstract
Synergistic optimization is a process combining different elements achieving better results than would be possible by optimizing each element individually. Our objective will be to design systems where genotyping and phenotyping complement each other to achieve superior performance. Deep phenotyping can be defined as a comprehensive and detailed approach to characterize traits of animals, including those based on the study of intermediate phenotypes representing not only the phenome, but also e.g., the metabolome, the proteome, and the transcriptome (i.e., -omics phenotypes). Often these phenotypes are also called high-throughput but this should not be considered being a synonym for being cheap. Interplay between genotyping and (deep) phenotyping has a major impact on the acquisition of genetic knowledge but could also be used in the context of genomic evaluations (i.e., as intermediate correlated features such as high-throughput or -omics phenotypes). While past studies have largely focused on ways to integrate deep phenotyping to gain new knowledge but also to support genetic and genomic evaluations, phenotyping and especially deep phenotyping currently requires massive investment, limiting its effective and widespread use under field, but also in experimental conditions. This problem extends to complex phenotype-taking as feed intake, methane emissions or in general use of expensive sensors. Even with all the possible investments, many phenotyping efforts are limited by the poor choice of animals. This presentation will address and introduce the topic of genome-enabled optimization of (deep) phenotyping taking into account the facts that genotypes are in many cases much cheaper than phenotyping, can be performed at early stages, and can help organize animal sampling in an efficient manner. The last point is also linked to validation techniques comparing predicted to observed phenotypes and closely related to recursive genomic reference population building issues. Techniques used in other fields such as chemometrics (i.e., infrared spectra prediction building) will be proposed to optimize covering genetic variability. In the context of recursively performed genomic evaluations (i.e., increasing reference population at each round) population accuracy and bias of prediction will be addressed. Moreover, long term genetic contribution and population structure issues will have to be considered. Based on literature and practical examples systematically organized sampling techniques will be presented to discuss basic principles as covering observed genomic and expected phenotypic diversity and long-term effects of recursively created genomic reference populations.
311 Effect of feeding butyric acid on growth performance and metabolic indicators of dairy calves before weaning: A meta-analysis
Authors: Kihal Hacib , Puyalto Monica , Mallo Juan Jose 
Published: September 2024

Abstract
This meta-analysis aimed to evaluate the effect of feeding butyric acid (BA) on growth performance and metabolic indicators in dairy calves. A literature search was conducted to identify in vivo research studies carried out on BA supplementation in dairy calves. Inclusion criteria were in vivo studies, dairy calves, weaning, BA, and inclusion doses of BA. Twenty-three papers with 80 data and 1,132 calves were selected. Data extracted included the number of calves in control and treatment groups and measures of variance of responses (standard error or standard deviation). The response variables were production performance including body weight (BW), average daily gain (ADG) feed efficiency (FE), and metabolic indicators including blood glucose and beta-hydroxybutyrate (BHB) concentrations. A random effect of model was used to examine the raw mean difference and standardized mean difference between BA supplementation and the control group. The analysis was conducted using the metafor package of Rstudio. Butyric acid moderately improved the growth performance and metabolic indicators responses of calves with substantial heterogeneity (59 &lt; I2 &lt; 73%, P &lt; 0.01). Final BW and ADG were increased by 2.7 kg and 48.1 g/day, or &gt; 0.6 and &gt; 0.4 standard deviation units (P &lt; 0.05), respectively. However, FE was increased by BA supplementation but not significantly different. For metabolic indicators, glucose concentration was greater with BA supplementation with &gt; 0.4 standard deviation unit, (P &lt; 0.05). However, BA supplementation did not affect the blood concentration of BHB (P &gt; 0.05). In conclusion, the meta-analysis results demonstrated that BA constitutes an important energy source for dairy calves. Butyric acid is effective in promoting growth performance and intestinal health and, yet, increasing production profits.
5 Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data
Authors: Tulpan Dan 
Published: September 2024

Abstract
This hands-on workshop aims to offer examples of computational pipelines that apply traditional predictive machine learning modelling techniques to animal science datasets with the purpose of solving classification and/or regression problems. The objective of this workshop is to provide the attendants with fully functional and customizable computational pipelines developed in Python. The workshop is structured in a hands-on format and includes a combination of basic notions about machine learning and relevant algorithms, evaluation measures, evaluation strategies and Python code examples. To avoid technical problems related to installing a Python environment on personal computers, we recommend the registrants to acquire access on the Repl.it platform (https://replit.com/) by creating a free account prior to attending the workshop. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2024/