Genetic parameters for traits derived from measures of electrical conductivity collected by automatic milking systems and their correlations with clinical mastitis in Holstein cattle

Journal of Dairy Science · 2026-04-01

Genetic selection for mastitis resistance in dairy cattle has been conducted through both direct selection on clinical mastitis and indirect selection via correlated traits like SCS. Even with these efforts, mastitis remains a major concern for the dairy industry. Automatic milking systems collect measures of milk electrical conductivity (COND) for individual quarters of the udder at every milking visit, and traits derived from these measures may be good candidates for inclusion in selection indexes as indirect indicators of mastitis resistance. Therefore, the objectives of this study were to 1) derive multiple traits from quarter-level measures of milk COND collected by automatic milking systems; 2) estimate genetic parameters for traits derived from these measures; and 3) estimate (co)variance components between traits derived from measures of milk COND, health traits, and test-day traits in a population of Holstein cattle in the United States. Quarter-level milk COND and visit milk yield were collected at every milking visit on 7,157 Holstein cows milked with automatic milking systems on a farm in north-central Indiana, USA. Additionally, producer-recorded events of clinical ketosis and clinical mastitis and test-day records of fat content, protein content, and SCS were available for an expanded population of cows that were milked in both automatic milking systems and conventional parlors. Eight traits were derived from quarter-level measures of milk COND collected at each milking visit. These visit-level traits were then summarized into daily values, either as the daily average or daily maximum, for subsequent analyses. Two additional traits were derived as the coefficient of variation or variance of all quarter-level measures of milk COND for an individual within a day. Additionally, data were analyzed separately by lactation: 1, 2, or 3+. Pedigree data for 53,186 individuals and genotype data for 29,126 individuals, imputed to 50,886 SNP, were used in the analyses. (Co)variance components were estimated using single-step GBLUP and linear animal models with REML. Estimates of heritability for traits derived from measures of milk COND ranged from 0.01 ± 0.003 (range of COND scores; lactation 3+) to 0.52 ± 0.016 (average of electrical COND; lactation 1). Estimates of genetic correlation between traits derived from measures of milk COND and clinical mastitis ranged from 0.27 ± 0.095 (right-front COND score; lactation 3+) to 0.89 ± 0.054 (coefficient of variation of COND scores; lactation 2), while estimates of genetic correlation between traits derived from measures of milk COND and SCS ranged from -0.05 ± 0.095 (quarter extremes ratio of COND scores; lactation 1) to 0.76 ± 0.032 (variance of COND scores; lactation 3+). Our results show that traits derived from measures of milk COND are heritable, and they have moderate to high genetic correlations with clinical mastitis, while having near-zero to high genetic correlations with SCS. These results suggest that traits derived from measures of milk COND are promising indicators of clinical mastitis and incorporating them into selection indexes in complement to traits already included in indexes for udder health can enhance genetic selection for mastitis resistance.

Genotyping strategies for single-step genomic predictions in a simulated sheep population under different scenarios of pedigree error types

Frontiers in Genetics · 2025-11-10 · 2 citations

Genomic predictions provide more accurate estimated breeding values (EBV) in younger animals. However, sheep reference populations are still small and if the animals included in the reference populations are not chosen carefully, genomic predictions may be biased. In this context, we compared genotyping strategies varying in the proportion of animals genotyped (using a 50K SNP panel) and the extent of pedigree errors (misidentified sires or missing information) on accuracy, bias, and dispersion of genomically-enhanced EBV (GEBV). We simulated a composite sheep population mimicking the formation and flock structure of the Katahdin breed using the AlphaSimR package. Sixteen flocks with an effective population size of 103 were simulated for two traits with heritabilities of 0.35 and 0.10. Breeding values were predicted with Best Linear Unbiased Prediction (BLUP) and Single-step Genomic BLUP (ssGBLUP). Scenarios included combinations of 0%-100% males or females genotyped, 0%-20% pedigree errors, and three genotyping strategies (random, highest EBV, or highest phenotypic values). The final population (18,717 animals) was divided into training and validation sets for calculating validation statistics of GEBV. Genomic prediction accuracy significantly improved with random genotyping, outperforming phenotype and EBV-based strategies by up to 19%. Pedigree errors reduced GEBV accuracy while increasing bias and dispersion. Missing pedigree information impacted results more than misidentified sires. Increasing the proportion of animals genotyped improved GEBV prediction metrics, with random genotyping yielding higher accuracies, lower biases, and dispersion closer to 1 (desirable). Prioritizing the genotyping of males up to 10% of the population before incorporating females enhanced the accuracy of GEBV. Genomic information mitigated some pedigree error effects. However, selective genotyping increased GEBV bias and dispersion, and reduced prediction accuracy. Compared to random genotyping, selective genotyping captured less genomic diversity, limiting the effectiveness of the reference population. Similar conclusions were obtained for both trait heritability levels. These findings highlight the importance of genotyping strategies when implementing genomic selection in sheep and the usefulness of genomic information for minimizing the impact of pedigree errors.

320 Can We define production environments more effectively by combining climate and management data?

Journal of Animal Science · 2025-10-01

Abstract The U.S. sheep industry operates under diverse environments, with differences in climatic and management conditions. A consequence may be expression of genotype-by-environment interactions (G×E), necessitating their incorporation into breeding strategies. Traditional methods for defining production environments may fail to capture the complexity of real-world management and climatic conditions, risking a loss in reliability of genetic evaluations. This study integrated climate and management data to define eco-management clusters, potentially offering a more accurate representation of production environments for G×E analyses in U.S. sheep. Data were collected from 97 flocks representing 5 sheep breeds: Katahdin (50 flocks), Polypay (21 flocks), Suffolk (13 flocks), Rambouillet (8 flocks), and Targhee (5 flocks). Climate data were sourced from NASA POWER based on flock-specific latitude and longitude coordinates and included elevation, seasonal precipitation, and soil moisture. Other meteorological variables (ambient temperature, relative humidity, wind speed, and solar radiation) were combined into a Comprehensive Climate Index and used as another climatic variable. Management data were obtained via a producer survey consisting of 60 questions, covering general husbandry, lambing, feeding, culling practices, and strategies for parasite control and mitigating adverse climatic conditions. Clustering analyses were conducted in R using the climate and management data either separately or together to delineate production environments. Climate clusters were derived using Principal Component Analysis (PCA). Management clusters were generated using Multiple Correspondence Analysis (MCA). Climate and management were combined into eco-management clusters using Factorial Analysis of Mixed Data (FAMD). Additionally, Linear Discriminant Analysis was applied to refine cluster separation. Only the top 5% of variables contributing to differentiation among clusters were retained to enhance interpretability. Clusters were validated using silhouette scores within each clustering method to confirm distinct groupings. Cross-tabulation of cluster memberships revealed a significant overlap between climate-based (PCA), management-based (MCA), and eco-management (FAMD) clusters. Climate-based clusters captured broad environmental differences, while management-based clusters reflected operational diversity. Eco-management clusters provided a more realistic classification, incorporating both factors, as animals thrive in environments shaped by both climatic conditions and management practices. Across all breeds, climate-based clustering explained the largest proportion of total variation (94 to 99%), while eco-management and management-based clusters accounted for less and similar amounts of variation (30 to 55%). Though the degree of improvement varied by breed, eco-management clusters offered a more refined classification of production environments. By improving the characterization of production environments, analyses of G×E may become more reliable improving predictions of genetic merit. Aligning selection for robustness and climatic resilience with flock-specific conditions can lead to more targeted breeding decisions. Future research will evaluate use of eco-management clusters in G×E analyses to identify climate-resilient sheep genotypes.

Estimation of Genetic Parameters and Weighted Single-Step Genome-Wide Association Study for Indicators of Colostrum Quality in Chinese Holstein Cattle

Agriculture · 2025-08-17

Colostrum is the milk harvested during the first few hours after calving, which contains high levels of immunoglobulins, antimicrobial peptides, and growth factors essential for the health of neonates. The primary objective of this study was to investigate the genetic background of colostrum quality traits (based on Brix percentage) in Holstein cows. Using phenotypic records of 58,338 Holstein cows from 37 dairy farms, we identified significant systematic effects influencing colostrum quality measured by digital Brix refractometer, estimated genetic parameters, and performed weighted single-step genome-wide association studies (WssGWAS) to identify genomic regions and candidate genes associated with these traits. The average (±SD) Brix percentage was 23.76 ± 3.25%. With heritability values ranging from 0.21 ± 0.03 (Brix in third parity) to 0.30 ± 0.02 (Brix in second parity), colostrum quality was determined to be moderately heritable. Genetic correlations between colostrum quality across parities ranged from 0.37 ± 0.14 to 0.81 ± 0.13. For colostrum quality from cows in the first, second, and third parities, WssGWAS enabled the identification of 30, 32, and 38 genomic regions explaining 4.18%, 4.42%, and 5.58% of the total additive genetic variance, respectively. Two immune-related genes (CNR1 and ZXDC) were identified as promising candidate genes for colostrum quality traits. In summary, colostrum quality measured in first parity cows should be evaluated as a different trait from measurements in later parities in breeding programs. These findings provide useful information for dairy breeders to genetically improve colostrum quality in dairy cattle populations.

The Law Governing Forum-Selection Clauses in International Commercial Contracts

Yearbook of Private International Law · 2025-11-06

Genotype imputation accuracy of X chromosome variants in Holstein cattle based on different software and imputation strategies

Journal of Dairy Science · 2025-09-19

) with FindHap. In general, all the scenarios had high imputation accuracy of the X chromosome SNPs when using the Minimac software, whereas FindHap showed better accuracy with scenarios S3 and S6. Including both males and females in the reference and validation populations increased the imputation accuracy of X chromosome variants. These findings highlight the importance of the choice of the imputation software and the need for enlarging the reference populations to increase genotype imputation accuracy of the X chromosome variants in Holstein cattle.

Variation in body weight and feed intake trajectories are promising resilience indicators in Texel lambs

Frontiers in Animal Science · 2025-12-09

Introduction Resilient animals are capable of coping with environmental perturbations or quickly returning to unperturbed performance trajectory after facing challenges. More resilient animals tend to have better welfare, health, and productivity under variable conditions. However, trade-offs between production and resilience traits have been reported, indicating the need for further research to enable genetic selection for increased productive efficiency while maintaining or improving general resilience. Methods In this study, data from 76 Texel lambs monitored during a 53-day feed efficiency trial were used to generate 24 indicators of resilience based on variability in daily feed intake (FI), feeding behavior and average daily gain (ADG) and assess their phenotypic relationship with ADG and residual FI (RFI). Some traits evaluated included adgVar (residual variance of ADG), adgLnVar (log-variance of deviation between observed absolute and expected ADG), QRfi (quantile regression of FI), and QRdurfi (quantile regression of duration with effective consumption). Results Strong associations were found between indicators, such as adgVar and adgLnVar (r = 0.81). Productive traits showed two clear patterns, ADG was favorably correlated with QRdur (r = -0.53), QRdurfi (r = -0.65), QRfi (r = -0.65), suggesting that more resilient animals tend to have higher ADG. Conversely, RFI presented unfavorable correlations with resilience, ranging from r = -0.46 for QRfi to r = -0.24 for QRtimesfi indicating that more feed-efficient animals may be less resilient. Discussion These contrasting results highlight two key findings: (1) productivity and resilience can be favorably associated, as shown by ADG-resilience correlations, however, (2) specific feed efficiency indicators (e.g., RFI) may have antagonistic relationships with resilience. Given the relatively small sample size (n = 76) in this exploratory study, findings should be interpretated with caution but can provide some insights into the relation between resilience and production and potential trade-offs warranting further investigation.

Genome-wide association and functional genomic analyses for udder conformation traits derived from data recorded by robotic milking systems in American Holstein cattle

Journal of Dairy Science · 2025-10-10 · 2 citations

The implementation of automatic milking systems (AMS) in modern dairy farming has significantly facilitated genetic evaluation of udder conformation traits in dairy cattle. As AMS-based udder conformation traits are highly heritable in Holstein cattle, this study aimed to identify genomic regions, QTL, and genes significantly associated with 3 udder conformation traits derived from Cartesian coordinates recorded by AMS in American Holstein cows. The traits evaluated included udder depth (UD), udder balance (UB), and distance front-rear (DFR). Phenotypic data consisted of 4,232,026 visit records to 36 Lely Astronaut A5 AMS robots for 4,280 American Holstein cows. A total of 4,118 cows also had genomic information for 57,598 SNPs. The SNP effects, together with their approximate P-values and proportion of the total additive genetic variance explained by them, were back solved from the GEBV using the POSTGSF90 software (version 2024-12-26, BLUPF90 Family of Programs, University of Georgia, Athens, GA). Multiple testing correction was applied using a modified Bonferroni method based on the number of independent chromosomal segments, and functional enrichment analyses were performed for the candidate positional genes and QTL. We identified 5 (BTA4, BTA22, and BTA29), 15 (BTA2, BTA8, BTA9, BTA15, BTA18, BTA23, and BTA26), and 19 (BTA1, BTA4, BTA6, BTA9, BTA10, BTA14, BTA17, BTA18, BTA23, and BTA25) genome-wide SNPs significantly associated with UB, DFR, and UD, respectively. There were no significant SNPs capturing more than 0.5% of the total additive genetic variance, highlighting the highly polygenic nature of these traits. The candidate genes overlapping with these genomic regions were previously reported to have biological functions that included but were not limited to transcription regulation, DNA repair mechanisms, inflammatory responses, epithelial differentiation processes, nutrient transport, extracellular matrix stabilization, cell division, and immune regulation. The strongest candidate genes associated with udder conformation traits are AFM, AFP, CREBBP, NLRP12, SP3, EBF2, B4GALT1, HDAC9, CDK13, and KDM2A. Furthermore, many QTL known to be associated with various milk production, reproduction, and health traits overlapped with the significant SNPs identified in this study. In summary, we identified significant genetic markers, candidate genes, and genomic regions associated with these traits, offering insights into the genetic architecture underlying udder conformation in Holstein cattle and its implications for dairy production.

Effects of microclimate during transport on physiological indicators of market pig welfare: a systematic review with meta-analysis

Frontiers in Veterinary Science · 2025-08-07 · 4 citations

During transportation, microclimatic conditions can fluctuate significantly, affecting pigs’ thermal comfort and leading to compromised welfare and production losses. Although numerous studies have examined the effects of heat stress during transport on pig welfare and meat quality, it remains unclear whether these effects persist across varying transport scenarios and environmental conditions. Therefore, this systematic review and meta-analysis evaluated the effects of microclimate during transport on physiological welfare indicators in market pigs and summarized methodologies for assessing microclimate in commercial settings. Following PRISMA guidelines, 21 studies from three databases were used. Meta-regression analyses assessed microclimatic effects and trip duration on physiological indicators, including ultimate pH (pHu), creatine kinase (U/L), lactate (mmol/L), skin lesion score (0–5), skin temperature (°C), and blood cortisol (ng/mL). The studies retrieved used different equations to determine temperature-humidity index and enthalpy to describe microclimate dynamics. Ambient temperature was significantly associated with trailer temperature ( β = 0.93 ± 0.12; p < 0.01). However, ambient relative humidity showed a lower magnitude association with trailer relative humidity ( β = 0.51 ± 0.00; p < 0.001). Adverse microclimate conditions represented by high enthalpy (H) were associated with increases in creatine kinase ( β = 3,715 ± 94.11; p < 0.001), lactate ( β = 0.45 ± 0.12; p < 0.001), skin temperature ( β = 0.10 ± 0.03; p < 0.01), and blood cortisol ( β = 0.16 ± 0.08; p < 0.05). Short trips (<119 min) increased skin lesion score ( β = 2.58 ± 0.43; p < 0.01), and medium trips (120–420 min) increased skin temperature ( β = 6.36 ± 0.45; p < 0.001) and reduced cortisol levels ( β = –11.36 ± 2.59; p < 0.01). In conclusion, trailer microclimates differ from ambient conditions and are strongly associated with physiological stress indicators in market pigs. Monitoring H may offer a more accurate representation of thermal load during transport, enabling threshold development for risk assessment. These consistent associations across diverse environments underscore the global nature of transport-related heat stress and the need for coordinated international welfare standards. Integrating compartment-level microclimate monitoring into transport protocols will improve welfare evaluation and support predictive risk models.

398 Characterizing the genomic background of climatic resilience in pigs through quantitative epigenetic models, novel traits, and biological validation studies.

Journal of Animal Science · 2025-10-01

Abstract Heat stress negatively impacts the welfare and productive efficiency of pigs at different life stages. However, lactating sows are at a greater risk of deleterious heat-stress effects due to a substantial increase in litter and piglet sizes and, consequently, greater lactation demand and metabolic heat production. Genomic selection has been very effective in speeding up the rates of genetic progress for productive and reproductive traits, but the lack of indicators of heat tolerance in the selection indexes could unfavorably impact the heat stress response of modern sows. The development of a sustainable breeding program requires the identification of novel traits and genomic evaluation strategies to genetically select for improved heat tolerance. We have previously provided evidence of genetic variability for heat stress response, both in gestating sows and growing/finishing pigs, based on routinely-recorded performance data and environmental gradient variables derived from public databases. As the heritability estimates for fertility traits are substantially lower compared to growth traits (and consequently, heat tolerance based on reproductive traits), we have identified novel phenotypes and biomarkers that better represent the behavioral and physiological mechanisms of heat stress response in both growing/finishing pigs and lactating sows raised in climatically-challenging regions. Heat stress and feed efficiency are regulated by biological networks that starts with the host genotype, includes the gut microbiome, and is manifested in behavioral changes, which ultimately affects productivity and animal welfare. In our work we follow an integrated approach to study the biological background of heat stress connecting pig genomic, behavioral measures, epigenomics, and deep phenotyping. We will: 1) provide a comprehensive description of the environmental-gradient variables and critical periods fitted in genomic evaluations of heat tolerance as well as the accuracy of genomic breeding values; 2) describe the genetic background of various indicators of heat tolerance followed by comprehensive biological validations; 3) describe epigenetic variance due to heat stress based on quantitative modeling, and 4) make recommendations for the implementation of genomic selection for improved heat tolerance in pigs.