Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency.

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.

Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite.

Large data sets allow estimating feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and body weight change during 28- and 42-d feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the energy-corrected milk formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, while the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kg DMI / kg body weight/lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by NASEM (2021) based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with body weight and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Ovarian stimulation with excessive FSH doses causes cumulus cell and oocyte dysfunction in small ovarian reserve heifers.

Excessive FSH doses during ovarian stimulation in the small ovarian reserve heifer (SORH) cause premature cumulus expansion and follicular hyperstimulation dysgenesis (FHD) in nearly all ovulatory-size follicles with predicted disruptions in cell-signaling pathways in cumulus cells and oocytes (before ovulatory hCG stimulation). These observations support the hypothesis that excessive FSH dysregulates cumulus cell function and oocyte maturation. To test this hypothesis, we determined whether excessive FSH-induced differentially expressed genes (DEGs) in cumulus cells identified in our previously published transcriptome analysis were altered independent of extreme phenotypic differences observed amongst ovulatory-size follicles, and assessed predicted roles of these DEGs in cumulus and oocyte biology. We also determined if excessive FSH alters cumulus cell morphology, and oocyte nuclear maturation before (premature) or after an ovulatory hCG stimulus or during IVM. Excessive FSH doses increased expression of 17 cumulus DEGs with known roles in cumulus cell and oocyte functions (responsiveness to gonadotrophins, survival, expansion, and oocyte maturation). Excessive FSH also induced premature cumulus expansion and oocyte maturation but inhibited cumulus expansion and oocyte maturation post-hCG and diminished the ability of oocytes with prematurely expanded cumulus cells to undergo IVF or nuclear maturation during IVM. Ovarian stimulation with excessive FSH is concluded to disrupt cumulus cell and oocyte functions by inducing premature cumulus expansion and dysregulating oocyte maturation without an ovulatory hCG stimulus yielding poor-quality cumulus-oocyte complexes that may be incorrectly judged morphologically as suitable for IVF during ART.

The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows.

Accurate early diagnosis of pregnancy is important for timely reproductive management of dairy farms. Fourier-transform mid-infrared (FT-MIR) milk spectral data are routinely used for determining milk components such as fat and protein, whereas milk composition is known to change with advancing stages of pregnancy. The objectives of this study were to compare partial least squares discriminant analysis (PLS-DA) and a Bayesian variable selection regression model (BayesC) for the diagnosis of pregnancy status (PS) from milk FT-MIR data and to infer any spectral regions that might be highly associated with PS at various stages of pregnancy. Conception dates on confirmed pregnant cows were obtained from Holstein cows within 123 herds in Michigan, Ohio, and Indiana during 2018 and 2019. Milk samples from these pregnant cows at 7 different stages of pregnancy were case-control matched to open contemporary herd mates to be within the same stage (±10 d for days in milk) of lactation for the same milk sample test date. The FT-MIR data were obtained for all of these milk samples. Ten-fold herd-independent cross-validation was used to compare PLS-DA versus BayesC using the area under the receiver operating characteristic curve (AUC). The BayesC model demonstrated higher mean AUC compared with PLS-DA at all stages exceeding 60 d of pregnancy. The mean BayesC AUC at stage 1 (1-30 d) was 0.58 ± 0.02, which was superior to a random guess (AUC = 0.50) yet too low to be of practical use. The mean BayesC AUC at stage 7 (≥180 d) was 0.13 greater compared with that of stage 1 (1-30 d) and 0.07 to 0.10 greater compared with stages 2, 3, 4, 5, and 6 (31-180 d in 30-d increments). The mean AUC of stages 2 to 6 were 0.03 to 0.06 greater compared with stage 1 yet again too low to be of practical use. Because of high multicollinearity between many adjacent wavenumbers, a spatially constrained clustering algorithm was used to adaptively partition wavenumbers into 68 windows before inferring associations of spectral regions with pregnancy. Pregnancy status was highly associated with wavenumber windows 1,063 to 1,134 cm-1, 1,201 to 1,257 cm-1, and 1,260 to 1,432 cm-1 based on an estimated BayesC posterior probability of association (PPA) approaching 100% for each of these windows at all pregnancy stages. Other windows ranging from 1,730 to 1,764 cm-1, 1,775 to 1,992 cm-1, 1,995 to 2,163 cm-1, and 2,167 to 2,316 cm-1 had varying medium to high PPA (30% to 100%) across stages. The estimated PPA in wavenumber regions from 1,477 to 1,507 cm-1, and 1,510 to 1,574 cm-1 was weaker in stages 1 and 2 compared with later stages, whereas for the regions 2,984 to 3,077 cm-1 and 3,081 to 3,133 cm-1 the effect of pregnancy was greater for stage 1 compared with other stages. Despite our conclusion that milk FT-MIR data poorly diagnose PS, our study provides new insights into spectral regions that are strongly associated with PS and warrant greater attention.

Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows.

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Negative impact of high doses of follicle-stimulating hormone during superovulation on the ovulatory follicle function in small ovarian reserve dairy heifers†.

When women with small ovarian reserves are subjected to assisted reproductive technologies, high doses of gonadotropins are linked to high oocyte and embryo wastage and low live birth rates. We hypothesized that excessive follicle-stimulating hormone (FSH) doses during superovulation are detrimental to ovulatory follicle function in individuals with a small ovarian reserve. To test this hypothesis, heifers with small ovarian reserves were injected twice daily for 4 days, beginning on Day 1 of the estrous cycle with 35, 70, 140, or 210 IU doses of Folltropin-V (FSH). Each heifer (n = 8) was superovulated using a Williams Latin Square Design. During each superovulation regimen, three prostaglandin F2α injections were given at 12-h interval, starting at the seventh FSH injection to regress the newly formed corpus luteum (CL). Human chorionic gonadotropin was injected 12 h after the last (8th) FSH injection to induce ovulation. Daily ultrasonography and blood sampling were used to determine the number and size of follicles and corpora lutea, uterine thickness, and circulating concentrations of estradiol, progesterone, and anti-Müllerian hormone (AMH). The highest doses of FSH did not increase AMH, progesterone, number of ovulatory-size follicles, uterine thickness, or number of CL. However, estradiol production and ovulation rate were lower for heifers given high FSH doses compared to lower doses, indicating detrimental effects on ovulatory follicle function.

Effects of Hatchery Rearing Density, Handling, and Nutrition on Renibacterium salmoninarum Infection Prevalence in Juvenile Chinook Salmon.

Bacterial kidney disease, caused by Renibacterium salmoninarum (RS), is a chronic and often fatal disease of salmonid species, and can be particularly harmful to hatchery-reared Chinook Salmon Oncorhynchus tshawytscha. A considerable amount of research has focused on the prevention of vertical and horizontal transmission; however, a comparatively little amount has investigated factors that increase the prevalence of RS infection in captive environments. We evaluated the effects of three common hatchery conditions (handling, nutrition level, and rearing density) on RS infection prevalence. Fish were sampled at 30-d and 60-d postexposure to RS. Of 577 juveniles examined, 65 (11.27%) had anterior kidneys infected with RS. Using a logistic mixed model analysis, we found effects of nutrition level (P = 0.018), handling (P = 0.010), and sampling period (P = 0.003) on the prevalence of RS. The interactions of nutrition and handling (P = 0.008) and nutrition and time (P < 0.001) were also significant. When fed a standard-nutrition diet, proportionately fewer fish were infected with RS when not handled (7.16% versus 0.04%; P = 0.003). Fish in the standard-nutrition group also had a lower prevalence of RS during the second sampling period (4.08% versus 0.08%, respectively; P < 0.001). When not handled, rearing with standard nutrition (11.50% versus 0.04%; P = 0.004) resulted in a reduction in prevalence of RS infection. Additionally, nonhandled fish had a much lower prevalence of RS infection during the second sampling period (2.66% versus 0.21%; P = 0.009). While density did not affect the prevalence of RS infection (P = 0.145), fish reared at a higher density had lower RS infection when not handled (16.48% versus 0.84%, P = 0.004). For fish at a higher density, the RS prevalence was lower during the second sampling period (10.57% versus 1.40%; P = 0.002). Our results suggest that hatchery managers can reduce RS infection prevalence by maintaining an adequate nutritional regime as recommended by the manufacturer. Additionally, the prevalence of RS may be reduced if managers decrease handling of hatchery-reared Chinook Salmon if exposed to RS.

Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle.

BACKGROUND: Copy number variation (CNV) is an important type of genetic variation contributing to phenotypic differences among mammals and may serve as an alternative molecular marker to single nucleotide polymorphism (SNP) for genome-wide association study (GWAS). Recently, GWAS analysis using CNV has been applied in livestock, although few studies have focused on Holstein cattle. RESULTS: We describe 191 CNV detected using intensity data from over 700,000 SNP genotypes generated with the BovineHD Genotyping BeadChip (Illumina, San Diego, CA) in 528 Holstein cows. The CNV were used for GWAS analysis of 10 important production traits of 473 cattle related to feed intake, milk quality, and female fertility, as well as 2 composite traits of net merit and productive life. In total, we detected 57 CNV associated (P < 0.05 after false discovery rate correction) with at least one of the 10 phenotypes. Focusing on feed efficiency and intake-related phenotypes of residual feed intake and dry matter intake, we detected a single CNV associated with both traits which overlaps a predicted olfactory receptor gene OR2A2 (LOC787786). Additionally, 2 CNV within the RXFP4 (relaxin/insulin like family peptide receptor 4) and 2 additional olfactory receptor gene regions, respectively, were associated with residual feed intake. The RXFP4 gene encodes a receptor for an orexigenic peptide, insulin-like peptide 5 produced by intestinal L cells, which is expressed by enteric neurons. Olfactory receptors are critical for transmitting the effects of odorants, contributing to the sense of smell, and have been implicated in participating in appetite regulation. CONCLUSIONS: Our results identify CNV for genomic evaluation in Holstein cattle, and provide candidate genes, such as RXFP4, contributing to variation in feed efficiency and feed intake-related traits. These results indicate potential novel targets for manipulating feed intake-related traits of livestock.

Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows.

The relationship of the estrous cycle to milk composition and milk physical properties was assessed on Holstein (n = 10,696), Brown Swiss (n = 20,501), Simmental (n = 17,837), and Alpine Grey (n = 8,595) cows reared in northeastern Italy. The first insemination after calving for each cow was chosen to be the day of estrus and insemination. Test days surrounding the insemination date (from 10 d before to 10 d after the day of the estrus) were selected and categorized in phases relative to estrus as diestrus high-progesterone, proestrus, estrus, metestrus, and diestrus increasing-progesterone phases. Milk components and physical properties were predicted on the basis of Fourier-transform infrared spectra of milk samples and were analyzed using a linear mixed model, which included the random effects of herd, the fixed classification effects of year-month, parity number, breed, estrous cycle phase, day nested within the estrous cycle phase, conception, partial regressions on linear and quadratic effects of days in milk nested within parity number, as well as the interactions between conception outcome with estrous cycle phase and breed with estrous cycle phase. Milk composition, particularly fat, protein, and lactose, showed clear differences among the estrous cycle phases. Fat increased by 0.14% from diestrus high-progesterone to estrous phase, whereas protein concomitantly decreased by 0.03%. Lactose appeared to remain relatively constant over diestrus high-progesterone, rising 1 d before the day of estrus followed by a gradual reduction over the subsequent phases. Specific fatty acids were also affected across the estrous cycle phases: C14:0 and C16:0 decreased (-0.34 and -0.48%) from proestrus to estrus with a concomitant increase in C18:0 and C18:1 cis-9 (0.40 and 0.73%). More general categories of fatty acids showed a similar behavior; that is, unsaturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, trans fatty acids, and long-chain fatty acids increased, whereas the saturated fatty acids, medium-chain fatty acids, and short-chain fatty acids decreased during the estrous phase. Finally, urea, somatic cell score, freezing point, pH, and homogenization index were also affected indicating variation associated with the hormonal and behavioral changes of cows in standing estrus. Hence, the variation in milk profiles of cows showing estrus should potentially be taken into account for precision dairy farming management.

Diagnosing pregnancy status using infrared spectra and milk composition in dairy cows.

Data on Holstein (16,890), Brown Swiss (31,441), Simmental (25,845), and Alpine Grey (12,535) cows reared in northeastern Italy were used to assess the ability of milk components (fat, protein, casein, and lactose) and Fourier transform infrared (FTIR) spectral data to diagnose pregnancy. Pregnancy status was defined as whether a pregnancy was confirmed by a subsequent calving and no other subsequent inseminations within 90 d of the breeding of specific interest. Milk samples were analyzed for components and FTIR full-spectrum data using a MilkoScan FT+ 6000 (Foss Electric, Hillerød, Denmark). The spectrum covered 1,060 wavenumbers (wn) from 5,010 to 925 cm-1. Pregnancy status was predicted using generalized linear models with fat, protein, lactose, casein, and individual FTIR spectral bands or wavelengths as predictors. We also fitted a generalized linear model as a simultaneous function of all wavelengths (1,060 wn) with a Bayesian variable selection model using the BGLR R-package (https://r-forge.r-project.org/projects/bglr/). Prediction accuracy was determined using the area under a receiver operating characteristic curve based on a 10-fold cross-validation (CV-AUC) assessment based on sensitivities and specificities of phenotypic predictions. Overall, the best prediction accuracies were obtained for the model that included the complete FTIR spectral data. We observed similar patterns across breeds with small differences in prediction accuracy. The highest CV-AUC value was obtained for Alpine Grey cows (CV-AUC = 0.645), whereas Brown Swiss and Simmental cows had similar performance (CV-AUC = 0.630 and 0.628, respectively), followed by Holsteins (CV-AUC = 0.607). For single-wavelength analyses, important peaks were detected at wn 2,973 to 2,872 cm-1 where Fat-B (C-H stretch) is usually filtered, wn 1,773 cm-1 where Fat-A (C=O stretch) is filtered, wn 1,546 cm-1 where protein is filtered, wn 1,468 cm-1 associated with urea and fat, wn 1,399 and 1,245 cm-1 associated with acetone, and wn 1,025 to 1,013 cm-1 where lactose is filtered. In conclusion, this research provides new insight into alternative strategies for pregnancy screening of dairy cows.

Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models.

BACKGROUND: The cattle tick is a parasite that adversely affects livestock performance in tropical areas. Although countries such as Australia and Brazil have developed genetic evaluations for tick resistance, these evaluations have not considered genotype by environment (G*E) interactions. Genetic gains could be adversely affected, since breedstock comparisons are environmentally dependent on the presence of G*E interactions, particularly if residual variability is also heterogeneous across environments. The objective of this study was to infer upon the existence of G*E interactions for tick resistance of cattle based on various models with different assumptions of genetic and residual variability. METHODS: Data were collected by the Delta G Connection Improvement program and included 10,673 records of tick counts on 4363 animals. Twelve models, including three traditional animal models (AM) and nine different hierarchical Bayesian reaction norm models (HBRNM), were investigated. One-step models that jointly estimate environmental covariates and reaction norms and two-step models based on previously estimated environmental covariates were used to infer upon G*E interactions. Model choice was based on the deviance criterion information. RESULTS: The best-fitting model specified heterogeneous residual variances across 10 subclasses that were bounded by every decile of the contemporary group (CG) estimates of tick count effects. One-step models generally had the highest estimated genetic variances. Heritability estimates were normally higher for HBRNM than for AM. One-step models based on heterogeneous residual variances also usually led to higher heritability estimates. Estimates of repeatability varied along the environmental gradient (ranging from 0.18 to 0.45), which implies that the relative importance of additive and permanent environmental effects for tick resistance is influenced by the environment. Estimated genetic correlations decreased as the tick infestation level increased, with negative correlations between extreme environmental levels, i.e., between more favorable (low infestation) and harsh environments (high infestation). CONCLUSIONS: HBRNM can be used to describe the presence of G*E interactions for tick resistance in Hereford and Braford beef cattle. The preferred model for the genetic evaluation of this population for tick counts in Brazilian climates was a one-step model that considered heteroscedastic residual variance. Reaction norm models are a powerful tool to identify and quantify G*E interactions and represent a promising alternative for genetic evaluation of tick resistance, since they are expected to lead to greater selection efficiency and genetic progress.

Short communication: On recognizing the proper experimental unit in animal studies in the dairy sciences.

Sound design of experiments combined with proper implementation of appropriate statistical methods for data analysis are critical for producing meaningful scientific results that are both replicable and reproducible. This communication addresses specific aspects of design and analysis of experiments relevant to the dairy sciences and, in so doing, responds to recent concerns raised in a letter to the editor of the Journal of Dairy Science regarding journal policy for research publications on pen-based animal studies. We further elaborate on points raised, rectify interpretation of important concepts, and show how aspects of statistical inference and elicitation of research conclusions are affected.

Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models.

BACKGROUND: The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that is `loosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. The remaining markers are specified to be random draws from a Student t distribution with key hyperparameters being degrees of freedom v and scale s(2). METHODS: We consider three alternative Markov chain Monte Carlo (MCMC) approaches based on the use of Metropolis-Hastings (MH) to estimate these key hyperparameters. The first approach, termed DFMH, is based on a previously published strategy for which s(2) is drawn by a Gibbs step and v is drawn by a MH step. The second strategy, termed UNIMH, substitutes MH for Gibbs when drawing s(2) and further collapses or marginalizes the full conditional density of v. The third strategy, termed BIVMH, is based on jointly drawing the two hyperparameters in a bivariate MH step. We also tested the effect of misspecification of s(2) for its effect on accuracy of genomic estimated breeding values (GEBV), yet allowing for inference on the other hyperparameters. RESULTS: The UNIMH and BIVMH strategies had significantly greater (P < 0.05) computational efficiencies for estimating v and s(2) than DFMH in BayesA (π = 1) and BayesB implementations. We drew similar conclusions based on an analysis of the public domain heterogeneous stock mice data. We also determined significant drops (P < 0.01) in accuracies of GEBV under BayesA by overspecifying s(2), whereas BayesB was more robust to such misspecifications. However, understating s(2) was compensated by counterbalancing inferences on v in BayesA and BayesB, and on π in BayesB. CONCLUSIONS: Sampling strategies based solely on MH updates of v and s(2), and collapsed representations of full conditional densities can improve the computational efficiency of MCMC relative to the use of Gibbs updates. We believe that proper inferences on s(2), v and π are vital to ensure that the accuracy of GEBV is maximized when using parametric WGP models.

Variation in litter occupancy and dust bathing patterns among layer strains following periods of litter restriction.

Producers are moving toward cage-free systems to house laying hens. These include aviary styles with multilevel wire enclosures and litter areas on the floor. In aviaries with doors hens can be confined within the tiered enclosure, which can be done to promote oviposition in nests and prevent hens from laying eggs in litter. However, there are multiple genetic strains of laying hen used in the egg industry, and some show different temporal patterns for key behaviors that could affect when they want to be on litter. For example, though dust bathing by laying hens is typically considered to peak in early afternoon, there may be variation in timing of motivation to dust bathe among strains. Differences in hens' temporal patterns, coupled with aviary configurations or management practices, may restricts birds' ability to perform important behaviors, such as dust bathing (DB), when they would most prefer to do them. Our objective was to determine if there were strain differences in the temporal pattern of DB. We examined the timing of DB in 4 strains of laying hen (Hy-Line Brown [HB], Bovans Brown [BB], DeKalb White [DW], and Hy-Line W36 [W36]) housed in aviaries using 144 hens of each strain per aviary unit (4 units/strain). We recorded the number of hens DB and on litter using instantaneous scan sampling every 5 min using video collected at 26 and 28 wk of age beginning at 11:35 (when litter access began each day) to 20:00 (lights off). Brown strains acclimated to litter access more slowly than white strains. Hens of all strains DB most often soon after gaining access to litter, and more white hens (DW and W36) DB simultaneously and in the presence of more conspecifics. Further examination of diurnal rhythm of behaviors, such as dust bathing, under unconstrained conditions by a range of genetic strains of laying hens is needed to design management practices and aviary styles that best meet hens' needs.

Impact of parity differences on residual feed intake estimation in Holstein cows.

Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. In lactating dairy cattle, RFI is typically obtained as the difference between dry matter intake observations and predictions from regression on known energy sinks, and effects of parity, days in milk, and cohort. The impact of parity (lactation number) on the estimation of RFI is not well understood, so the objectives of this study were to (1) evaluate alternative RFI models in which the energy sinks (metabolic body weight, body weight change, and secreted milk energy) were nested or not nested within parity, and (2) estimate variance components and genetic correlations for RFI across parities. Data consisted of 72,474 weekly RFI records of 5,813 lactating Holstein cows collected from 2007 to 2022 in 5 research stations across the United States. Estimates of heritability, repeatability, and genetic correlations between weekly RFI for parities 1, 2, and 3 were obtained using bivariate repeatability animal models. The nested RFI model showed better goodness of fit than the nonnested model, and some partial regression coefficients of dry matter intake on energy sinks were heterogeneous between parities. However, the Spearman's rank correlation between RFI values calculated from nested and nonnested models was equal to 0.99. Similarly, Spearman's rank correlation between the RFI breeding values from these 2 models was equal to 0.98. Heritability estimates for RFI were equal to 0.16 for parity 1, 0.19 for parity 2, and 0.22 for parity 3. Repeatability estimates for RFI across weeks within parities were high, ranging from 0.51 to 0.57. Spearman's rank correlations of sires' breeding values were 0.99 between parities 1 and 2, 0.91 between parities 1 and 3, and 0.92 between parities 2 and 3. We conclude that nesting energy sinks within parity when computing RFI improves model goodness of fit, but the impact on the estimated breading values appears to be minimal.

Hierarchical Bayesian modeling of heterogeneous cluster- and subject-level associations between continuous and binary outcomes in dairy production.

The augmentation of categorical outcomes with underlying Gaussian variables in bivariate generalized mixed effects models has facilitated the joint modeling of continuous and binary response variables. These models typically assume that random effects and residual effects (co)variances are homogeneous across all clusters and subjects, respectively. Motivated by conflicting evidence about the association between performance outcomes in dairy production systems, we consider the situation where these (co)variance parameters may themselves be functions of systematic and/or random effects. We present a hierarchical Bayesian extension of bivariate generalized linear models whereby functions of the (co)variance matrices are specified as linear combinations of fixed and random effects following a square-root-free Cholesky reparameterization that ensures necessary positive semidefinite constraints. We test the proposed model by simulation and apply it to the analysis of a dairy cattle data set in which the random herd-level and residual cow-level effects (co)variances between a continuous production trait and binary reproduction trait are modeled as functions of fixed management effects and random cluster effects.

Limitations in use of ovarian reserve biomarkers to predict the superovulation response in small ovarian reserve heifers.

High FSH doses during superovulation of heifers with a small ovarian reserve increase the number of dysfunctional ovulatory-size follicles that do not ovulate in response to human chorionic gonadotropin (hCG). Thus, anti-Müllerian hormone (AMH) and antral follicle count (AFC), two well-established biomarkers of responsiveness of individuals to superovulation, are hypothesized to be positively linked to number of dysfunctional ovulatory-size follicles developing in response to superovulation with high FSH doses. To test this hypothesis, heifers with a small ovarian reserve were stimulated beginning on Day 1 of the estrous cycle with twice daily treatments for 4 days with each of four Folltropin-V (FSH) doses (35 IU, 70 IU (industry standard), 140 IU, or 210 IU) followed by prostaglandin F2α to regress corpora lutea (CL) from the previous estrous cycle and hCG to induce ovulation. Ovulatory-size follicles were classified as functional or dysfunctional based on whether they ovulated and formed CL in response to hCG. FSH dose did not impact the relationship between AMH, AFC and the number of functional or dysfunctional ovulatory-size follicles developing in response to superovulation. Thus, data from the four superovulations were averaged for each heifer. AMH and AFC were positively associated with the subsequent number of functional and dysfunctional ovulatory-size follicles and the proportion of ovulatory-size follicles that are dysfunctional after superovulation. Because measurements of AMH concentration and AFC predict the number but not functionality of ovulatory-size follicles, which may also impact oocyte quality, these ovarian reserve biomarkers are concluded to be unlikely useful to improve IVF or embryo transfer outcomes in heifers with a small ovarian reserve.

Effects of a multi-strain Bacillus subtilis-based direct-fed microbial on immunity markers and intestinal morphology in diets fed to weanling pigs.

The objective of this experiment was to evaluate the effects of a multi-strain Bacillus subtilis-based direct-fed microbial (DFM) on nursery pig health as indicated by intestinal mucosal and blood plasma immunological markers and intestinal morphology. Eighty pigs, of equal number of barrows and gilts (initial BW: 7.0 ±â€…0.60 kg), weaned at 21 ±â€…1 d of age were randomly allotted to sixteen pens, with five pigs per pen. Two dietary treatments were implemented, a basal control (CON) and a basal control plus DFM (CDFM). Both diets were corn, soybean meal, and distillers dried grains based and were formulated to meet or exceed all nutritional requirements (NRC, 2012) and manufactured on site. Diets were fed for 42 d. On d 21 and 42 of the experiment, one pig per pen was randomly selected and euthanized, with equal number of males and females represented. Blood samples were collected prior to euthanasia for assessment of plasma concentrations of immunoglobulin A (IgA) and intestinal fatty acid binding protein. Segments of the gastrointestinal tract including duodenum, jejunum, ileum, ascending and distal colon were removed for analysis of intestinal morphology, and levels of interleukin 6, interleukin 10 (IL-10), and tumor necrosis factor alpha. Jejunal villus height was greater in the CDFM pigs as compared with CON pigs (P = 0.02) and ascending colon crypt depth tended to be greater on d 21 (P = 0.10). Compared to CON, CDFM significantly increased overall plasma IgA (P = 0.03) (0.58 vs. 0.73 0.05 mg/mL, respectively), while it tended to increase plasma IgA (P = 0.06) on d 21 (0.34 vs. 0.54 ±â€…0.07 mg/mL, respectively) and tended to increase overall IL-10 (P = 0.10) in the jejunum (113 vs. 195 ±â€…35 pg/mL, respectively). Addition of a multi-strain Bacillus subtilis-based DFM may have an early benefit to nursery pig health status, observed through specific changes in morphology and both systemic and localized immunological markers.

Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows.

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197-0.333) under A + A×A model and was 0.231 (with range of 0.188-0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02-14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.