bluuebunny/crossref_metadata_2025_split huggingface dataset:
141 Genomic selection for heat stress resilience in pigs: Advancements, applications, and challenges
Authors: Johnson Jay S , Brito Luiz F 
Published: September 2024

Abstract
Heat stress is a critical challenge to swine productivity and welfare that causes economic losses and endangers food security. As temperatures continue to increase in most pig producing regions, the effects of heat stress on pig production will become more apparent. The detrimental effects of postnatal heat stress on pig performance and welfare are well-defined. Furthermore, data on the effects of in utero heat stress in pigs describes lifelong production and welfare deficits that may be transgenerational. Despite its economic implications and transgenerational effects, heat stress mitigation strategies remain underexplored. Genomic selection offers a promising avenue for addressing swine heat stress, targeting resilient swine phenotypes to minimize adverse outcomes. However, challenges persist in integrating thermotolerance traits with traditional performance metrics, as improved thermotolerance is often unfavorably correlated with production traits. Therefore, effective selection strategies require balancing productive efficiency with climatic resilience and animal welfare breeding goals. As climate change continues to intensify the frequency and severity of heat stress, innovative approaches that prioritize resilience and adaptability are needed to safeguard swine productivity and welfare.
88 The impact of trait measurement error on quantitative genetic analysis
Authors: Bi Ye , Huang Yijian , Morota Gota 
Published: September 2024

Abstract
The accuracy of trait measurements greatly affects the quality of downstream genetic analyses. With the advancement of three-dimensional sensor cameras and computer vision techniques, there is a growing trend to adopt these new tools for on-farm data collection as they can reduce labor and facilitate the collection of large phenotypic data sets across diverse populations. Pig body weight (BW) values estimated 1) manually using a digital scale, and 2) from videos using computer vision can be considered as two different measurements of the same trait because the source of phenotyping error is different. In other words, BW is controlled by the same set of causal loci, but by different phenotyping errors, and thus has different heritabilities. Previous research has shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, may be influenced, at least in part, by genetic factors. This suggests that trait measurement error is systematic rather than random, and thus, more likely to lead to misleading downstream results when used in quantitative genetic analysis. Therefore, in this study, we investigated the impact of trait measurement error on the genetic analysis of pig BW. Specifically, the objectives of this study were to 1) estimate trait measurement error from manually collected and image-derived pig BW, and 2) estimate genomic heritability for trait measurement error. Ground-truth BW data were collected manually using a digital weighing system for 540 pigs at five different time points over 3 mo. In addition, image-derived BW were estimated by training models on video data captured by a depth camera. A total of four image analysis machine learning and deep learning models were used, including adaptive thresholding segmentation coupled with either ordinary least squares or random forests, and deep regression based on ResNet50 and Xception. Coefficients of determination for image-derived and ground truth BW were at least 0.95 across time points. Genomic heritability estimates for manual and image-derived BW were mostly identical across time points (ranging from 0.23 to 0.54), indicating that manually measured and image-derived phenotypes yield the same genetic results. In addition, genomic heritability estimates for measurement error were consistently negligible, indicating that trait measurement errors in pig BW do not contain systematic errors that could bias downstream genetic analysis.
119 Awardee Talk: Decoding genomic predictions and genome-wide association in large populations
Authors: Lourenco Daniela , Bermann Matias , Cesarani Alberto , Tsuruta Shogo , Misztal Ignacy 
Published: September 2024

Abstract
Data recording is an integral part of breeding programs. Some breed associations and breeding companies keep historical data that may go back to the 1950s. While pruning old data can help decrease computing costs without sacrificing accuracy, this practice is avoided by most livestock industries. Besides phenotypes and pedigree, genomic information has been accumulating very fast, and dealing with that has become the biggest challenge in the genomic selection era. There are currently more than 8.5 million genotyped dairy cattle and 1.5 million Angus cattle in the US. We recently ran genomic evaluations for almost 4 million genotyped animals, 30 million animals in the pedigree, and 45 million phenotypes for five different breeds, and this talk will describe how these large-scale genomic evaluations are possible under single-step GBLUP (ssGBLUP). In ssGBLUP, a genomic relationship matrix (G) is needed, which may take around 116 TB to keep in memory (RAM) for 4 million genotyped animals. Although millions of animals can be genotyped, the dimensionality of the genomic information is limited, meaning there is a limited set of genotyped individuals representing the independent chromosome segments in the population (i.e., the non-redundant information). The dimensionality is around 6 k in pigs and chickens and 15 k in cattle, which are considered the “core” animals. This reduced rank is exploited to reduce computing costs by creating a generalized, sparse inverse of G with an approximately linear cost in computing and storage. In multi-breed evaluations, the dimensionality should be assessed within each breed to maximize the GEBV prediction accuracy. The model-based accuracy of GEBV is also an important piece of information to help selection but cannot be easily obtained in large, genotyped populations. Our recently developed algorithm also considers the dimensionality of genomic information and uses block sparse inversion to approximate prediction error (co)variance (PEC) for genotyped animals. With PEC for core animals, computing PEC for SNP is trivial, and the latter is used to obtain p-values for SNP effects in genome-wide association studies (GWAS). Using large amounts of information in GWAS may improve the resolution and discovery. Exploiting the limited dimensionality of genomic information helped lift computing limitations for ssGBLUP genomic evaluations and GWAS in large, genotyped populations. Because of that, ssGBLUP became a popular method for genomic selection in livestock. Although this method is quite efficient now, the ever-increasing number of genotyped animals will pose future challenges that will be addressed by constantly changing algorithms and evolving computing power.
121 Copy number variation: From mapping to variance components in Holsteins
Authors: Ladeira Giovanni C , Pinedo Pablo J , Santos Jose E P , Thatcher William W , Rezende Fernanda M 
Published: September 2024

Abstract
Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.
118 DHGLMF90: A software tool using double hierarchical generalized linear models for estimating the genetic heterogeneity of residual variance
Authors: Munera Alejandra Alvarez , Bermann Matias , Escriche Noelia Ibáñez , Rebollo Cristina Casto , Misztal Ignacy , Lourenco Daniela 
Published: September 2024

Abstract
In animal breeding, there is a growing interest in selecting animals that exhibit uniform responses to environmental conditions, aiming for product consistency as a desirable breeding objective. This could be achieved by modeling heteroskedastic residuals when estimating breeding values through a hierarchical single-trait model that includes a mean and dispersion part. The latter could be influenced by genetic and non-genetic features. Bayesian methods can estimate heteroskedastic residuals using the Metropolis-Hastings algorithm, which is a slow and inefficient approach. Double hierarchical generalized linear models (DHGLM) provide a faster alternative to Bayesian methods. This study aimed to develop software named DHGLMF90 to implement DHGLM using a highly efficient algorithm to accurately estimate heterogeneous residual variances and breeding values, particularly suited for handling large datasets. We improved the reweighted least squares algorithm (IRWLS) to enhance convergence properties compared with prior implementations. IRWLS iteratively computes variance components and leverages them for a bivariate model, covering both the mean and dispersion part of the original model. In DHGLMF90, variance component estimation is performed using REML through BLUPF90+. The software was tested using both simulated and real datasets, the latter from previous studies. Additionally, the simulations were replicated to ensure a comprehensive evaluation of the DHGLMF90 performance. Results indicated that, in most cases, there were no significant differences in estimation compared with results obtained from previous studies. DHGLMF90 converged in order of magnitude faster than Bayesian methods. Future developments will include analyzing datasets with genomic information and multiple-trait models.
202 Longitudinal comparative transcriptomics of dairy cattle at different altitudes reveals unique mechanisms underlying plateau hypoxia adaptation
Authors: Fang Qianhai , Zhang Hailiang , Wang Yachun 
Published: September 2024

Abstract
The adaptation of livestock to high altitude hypoxic environment (plateau hypoxia adaptation) is important as they have a key role in the socio-economic conditions of the local residents. However, the underlying molecular mechanisms of plateau hypoxia adaptation of livestock are poorly understood. This study was objective to 1) construct the longitudinal transcriptional profiles of dairy cattle across five development stages; 2) reveal the transcriptomic changes of peripheral blood under different altitudes environment; and 3) identify key genes involved in plateau hypoxia adaptation in bovine. In this study, 109 peripheral blood samples from 70 Holstein cattle were performed whole transcriptome sequencing, including 56 at high altitude (&gt; 3600 m) at five development stages (1, 2, 6, 12 and 18 mo) from 56 individuals, and 53 at low altitude (~1000 m) at three development stages (1, 2 and 6 mo) from 14 individuals. The peripheral transcriptional profile was obtained for high altitude cattle at five development stages, and for low altitude cattle for three development stages. By the transcriptome profile analysis in plateau cattle across five development stages, it was found that there were significant differences on mRNA transcriptome at different stages, and a noticeable shift occurred between 2 mo and 6 mo. By a comparative analysis of the transcription levels between high and low altitude cattle, 795, 900 and 880 differentially expressed genes (DEGs) were identified at the age of 1, 2 and 6 mo, respectively. The DEGs at three stages were enriched in several cellular communication, hypoxia response, and regulation of blood pressure, and immune-related signaling pathways. Furthermore, there were 64 DEGs shared across three comparative analyses at the three stages. By functional annotation analysis, we observed the shared DEGs of the three stages were enriched in the Notch signaling involved in heart development, regulation of phosphorylation, and positive regulation of MAP kinase activity. In summary, this study revealed the development-related transcriptomic shifts from birth to 18 mo of age in dairy cattle, and preliminarily elucidated the important signaling pathways involved in bovine plateau hypoxia adaptation by comparative analysis between high and low altitude cattle. The results of the current study will drive a better understanding of plateau hypoxia adaptation and can provide molecular targets for future breeding of plateau-adapted dairy cattle.
200 Transcriptomics analyses of mink spleen infected by Aleutian Disease
Authors: Bishop Gregory , Farid Hossain , Do Duy Ngoc , Miar Younes 
Published: September 2024

Abstract
Aleutian disease (AD) is one of the most challenging mink diseases that causes high mortality and affects several economically important traits. Aleutian mink disease virus (AMDV) targets multiple organs and among them, the spleen is one of the major targeted organs. Transcriptomics has been widely used to reveal genes and biological pathways and identify biomarkers for early detection or prevention of diseases. This study aimed to identify genes and pathways related to the host response to AD infection in the spleen via transcriptomic study. Twelve (3 sets of 4 full-sibs) AMDV-free black male mink were housed in the clean section of the animal housing facility at the AD Research Centre (Truro, NS, Canada) between 4 to 7 d before inoculation. Before inoculation, sampling, and euthanasia, animals were anesthetized. The viral inoculum was a 10% (W/V) passage 2 of AMDV prepared from the spleens of mink infected with a local strain and stored at − 80 °C. Animals were intranasally inoculated under sedation with 60 µL of the viral homogenate, corresponding to approximately 300 to 700 ID50. Infected mink were humanely euthanized and the spleen tissues were collected for RNA isolation at 24h (d 1), 48h (d 2), and d 7. Libraries were prepared from total RNA using the Illumina TruSeqTM RNA kit, and were sequenced using the HiScanSQ platform, producing 101 bp reads from each end. The raw RNA-Seq data were cleaned using Cutadapt V1.4.2 and after the cleaning process was completed, 942,803,540 paired-end reads remained to be used in downstream analysis. Differential gene expression analyses revealed most (168) significant differentially expressed (DE) genes between d 1 and d 0 (Control or ADMV-free mink) while fewer DE genes (23) were between d 7 and d 0 (Table 1; Figure 1). A total of 19 DE genes are identified between three pair comparisons and seven of them are directly involved in immune response (FGL2, TLF8, LRP1, SERPINB9, MSR1, C3, PLA2R1, and XCR1). Gene enrichment analyses revealed pathways related to innate immune and fat metabolism which are important for the host mechanism to fight against infections. One of the important pathways is neutrophil degranulation which functions as aiding in the elimination of pathogens and the initiation of the inflammatory process. In conclusion, the current study provides insight into the transcriptomic profiles of the spleen in mink infected with AD. Identified candidate genes might be used for functional studies or as prior information for markers or genomic selection against AD in mink. Further studies in other tissues or single-cell RNA sequencing might deliver more comprehensive pictures of host responses to AD infections.
203 Genome Wide Association Study for reduced dry period in dairy goats
Authors: Galindo Bruno A , Massender Erin , Hermisdorff Isis C , Schenkel Flávio S 
Published: September 2024

Abstract
Canada’s goat herd is about 243,000 animals, with national yearly milk production of approximately 62.6 million L. However, the ongoing import of 182,000 kg of goat cheese highlights the potential for further expansion in milk production within the country. The period between two lactations when females are not producing milk is called the “dry period”, which ranges between 50 to 60 d in Ontario goats. Shortening the dry period without compromising animal health or production would be advantageous for producers. In Ontario, records have revealed a herd where a substantial percentage of animals experience a dry period between lactations as short as one day. A genomic study of this herd could provide an understanding regarding the genetic background of these short dry periods (= 1 d). The goal of this research was to perform a Genome Wide Association Study (GWAS) aiming to identify genes associated with dry period length. A total of 209 Saanen dairy goats with usual dry periods (coded as 0) and 54 with short dry periods (coded as 1) were analyzed. All animals were genotyped using GoatSNP50 Bead Chip from Illumina Inc. and, following genotyping quality control, 44,692 variants were retained for analysis. A GWAS using a mixed linear model implemented in GCTA software version v1.94.1 was performed, employing the “leave-one-chromosome-out” approach, excluding the chromosome containing the candidate SNP from the genetic relationship matrix (GRM) calculation. P-values were adjusted for multiple comparisons using the Benjamini & Hochberg method. Additionally, a qq-plot was used to assess possible inflation of the test statistic, but no inflation (λ=1.03) was observed. Four significant SNPs (FDR &lt; 0.05) associated with short dry periods were identified, all located on chromosome 20. Subsequently, within a 60 k base pair range upstream and downstream of these markers, two candidate genes (OSMR and GHR) and one QTL associated with cannon bone circumference were identified. Of particular interest is the OSMR gene, previously linked to apoptosis in goat mammary gland mRNA profiles and showing increased mRNA and protein levels post-weaning in mice. Additionally, GHR gene has been associated with lactation persistency and various milk production traits in cows. The gene network analysis also revealed interactions between OSMR and GHR. This region on chromosome 20 seems to harbor genes influencing the dry period in dairy goats as well as the milk production traits. Further investigation of this genetic region could help increase the knowledge about the genetic background of the observed short dry periods.
349 The relationship of heart score with hot carcass weight and fat depth in Angus cattle
Authors: Torres-Quijada Isaac F , Speidel Scott E , Bryant Tony C , Retallick-Riley Kelli J , Szasz Josh I , Enns R Mark 
Published: September 2024

Abstract
Congestive Heart Failure (CHF) is a cardiopulmonary condition observed in cattle raised in feedlots situated at elevations above 800 m. This condition leads to right-sided heart failure caused by increased pulmonary vessel pressures and cardiopulmonary remodeling, known as pulmonary hypertension (PH). In cattle experiencing PH, pulmonary arterial pressure (PAP) becomes abnormally high, increasing heart muscle load. This load stretches muscle fibers and enlarges the right side of the heart which can lead to heart failure and death. To assess the severity of PH, animals are assigned a heart score (HS) on a scale ranging from 1 to 5 at harvest. A score of 1 represents a normal heart, while a score of 5 indicates extreme heart remodeling. Understanding the relationship between hot carcass weight (HCW), fat depth (FD), and HS in Angus cattle will provide useful information on the impacts of this condition on feedlot performance and profitability. The objective of this study was to determine if a phenotypic relationship between HCW and FD with HS in Angus-influenced cattle. For this experiment, a total of 798 black, Angus-influenced cattle were used (245 steers, 553 heifers) that were fed near Greeley, CO. The HCW, FD, and HS data from each animal were recorded at harvest. Three general linear models were used to estimate the effects of HS on HCW and FD. Mean HCW and FD were 393.1 kg, (SD = 90.9) and 0.66 mm, (SD = 0.18), respectively, with an average HS of 2.08 (SD = 0.77). For all models, HCW and FD were fit as dependent variables, while lot and HS were included as fixed effects. Model 1 included all HS categories independently. Model 2 divided HS into 2 groups of normal (HS = 1 or 2) or remodeled (HS = 3+). For model 3 HS 3 was removed leaving only HS of 4 or 5 in the remodeled group. For both HCW and FD in Model 1, the overall effect of HS was not significant (P = 0.82 and P = 0.40, respectively). However, when comparing individual scores, animals with a HS = 4 were 15.0 kg lighter for HCW than individuals with a HS = 1 (P &lt; 0.05). Conversely for FD, a non-significant difference (P = 0.74) of -0.33 mm was observed between animals with a HS = 4 versus a HS = 1. Grouping HS into 2 categories in Model 2 did change the outcome, yielding non-significant HCW and FD differences. However, when eliminating the HS = 3 data, a significant reduction in HCW of 16.9 kg (P = 0.05) was observed between the 2 HS groups, whereas non-significant effects on FD remained. These data suggest that at extreme levels of heart remodeling HCW may be extremely impacted negatively.