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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.

Objective Our objectives were to identify optimal dietary inclusion for Sweet Bran (SB; Cargill Inc.), a branded, modified wet corn gluten feed product, and determine whether there are other important factors to consider when feeding SB to lactating dairy cattle. Materials and Methods Experiments where at least 2 SB concentrations were investigated (n = 12; 50 treatment means) were compiled into a database for meta-regression analysis. Experiments were screened to ensure they reported nutrient and ingredient composition of treatments and milk yield and milk component responses. The meta-regression was conducted using lmer and step functions of the lmerTest package within R (v 4.0.2). Results and Discussion Dietary SB interacted with NDF concentration such that greater SB inclusion was associated with increased DMI, but the magnitude of this effect declined with increasing dietary NDF concentration. Milk fat percentage and protein yield were affected by an interaction between dietary forage NDF (fNDF) and SB; increasing fNDF when feeding greater SB inclusions had positive effects on milk fat concentration but negative effects on milk protein yield. Milk fat and protein yield were optimized at 26 and 11% SB inclusion, respectively. Implications and Applications Sweet Bran supports increased DMI and milk fat yield when included at 20 to 40% of diet DM, but total diet NDF must be controlled. Results also suggest that moderate fNDF when feeding SB is important. Too little fNDF reduces milk fat percentage, whereas too much reduces protein yield. Finally, feeding greater SB concentrations reduced milk protein yield, suggesting the need to focus on AA balancing.

Understanding factors affecting social interactions among animals is important for applied animal behavior research. Thus, there is a need to elicit statistical models to analyze data collected from pairwise behavioral interactions. In this study, we propose treating social interaction data as dyadic observations and propose a statistical model for their analysis. We performed posterior predictive checks of the model through different validation strategies: stratified 5-fold random cross-validation, block-by-social-group cross-validation, and block-by-focal-animals validation. The proposed model was applied to a pig behavior dataset collected from 797 growing pigs freshly remixed into 59 social groups that resulted in 10,032 records of directional dyadic interactions. The response variable was the duration in seconds that each animal spent delivering attacks on another group mate. Generalized linear mixed models were fitted. Fixed effects included sex, individual weight, prior nursery mate experience, and prior littermate experience of the two pigs in the dyad. Random effects included aggression giver, aggression receiver, dyad, and social group. A Bayesian framework was utilized for parameter estimation and posterior predictive model checking. Prior nursery mate experience was the only significant fixed effect. In addition, a weak but significant correlation between the random giver effect and the random receiver effect was obtained when analyzing the attacking duration. The predictive performance of the model varied depending on the validation strategy, with substantially lower performance from the block-by-social-group strategy than other validation strategies. Collectively, this paper demonstrates a statistical model to analyze interactive animal behaviors, particularly dyadic interactions.

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 ± 0.60 kg), weaned at 21 ± 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 ± 0.07 mg/mL, respectively) and tended to increase overall IL-10 (P = 0.10) in the jejunum (113 vs. 195 ± 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.

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 F(2α) 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.

Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.

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

A study was conducted to evaluate the effects of a multi-strain Bacillus subtilis-based direct-fed microbial (DFM) on growth performance and apparent nutrient digestibility of nursery pigs. Eighty pigs, of equal number of barrows and gilts (initial body weight: 7.0 ± 0.60 kg), were weaned at 21 ± 1 d and randomly allotted to 1 of the 16 pens, with 5 pigs per pen. Two dietary treatments were implemented, a basal control (CON) and a control plus DFM (CDFM). Both diets were corn, soybean meal, and distillers dried grains based. Diets were fed for 42 d and growth performance measures were recorded weekly. On days 21 and 42 of the experiment, one pig per pen, with equal number of males and females, was randomly selected and euthanized. Digestibility of nitrogen (N), amino acids (AA), and energy were evaluated within the duodenum, jejunum, ileum, and ascending and distal colon. Relative to CON, CDFM tended to increase ADG during week 2 (P = 0.08) and significantly increased ADFI during week 2 (P = 0.04) and week 3 (P = 0.02). In addition, CDFM decreased the gain to feed ratio (G:F) during week 6 relative to CON (P = 0.04). Within the jejunum, pigs fed the DFM had greater digestibility of tryptophan (P = 0.04) and cysteine (P = 0.04) and tended to have greater digestibility of lysine (P = 0.07), methionine (P = 0.06), and threonine (P = 0.08), relative to CON. The content pH in the ascending colon did not differ between CDFM and CON. Compared with CON, apparent total tract digestibility (ATTD) of energy did not differ from CDFM, whereas ATTD of nitrogen of CDFM was lower (P = 0.05). The addition of a multi-strain B. subtilis-based DFM appears to impact growth performance, AA, and N digestibility depending upon the location in the gastrointestinal tract, with primary AA differences occurring within the mid-jejunum.

Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.

The objective of this experiment was to evaluate the effects of a multi-strain Bacillus subtilis-based direct-fed microbial (DFM) on intestinal mucosal and blood plasma immunological markers, intestinal morphology, and microbiota of nursery pigs as indicators of health status. Eighty pigs, of equal number of barrows and gilts (initial BW: 6.99 ± 1.67 kg), weaned at 21 ± 1 days of age were randomly allotted to sixteen pens, with five pigs per pen. Two dietary treatments were implemented, a basal control (CON) and a control plus DFM (DFM). Both diets were corn, soybean meal, and distillers dried grains based, formulated to meet all or exceed all nutritional requirements, and manufactured on site. Diets were fed for 42 days. 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 levels of immunoglobin 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, microbial communities, and levels of interleukin 6, interleukin 10 (IL-10), and tumor necrosis factor alpha. Jejunal villus height was greater (P = 0.02) in DFM compared with CON (422 vs. 385 ± 10 µm, respectively) and ascending colon crypt depth tended to be greater (P = 0.10) on d 21 (373 vs. 337 ± 14 µm, respectively). Compared to CON, DFM tended to increase IgA (P = 0.06) on d 21 (0.34 vs. 0.54 ± 0.07 mg/mL, respectively) and tended to increase IL-10 (P = 0.12) on d 42 (133 vs. 237 ± 49 pg/mL, respectively). Addition of a multi-strain Bacillus subtilis-based DFM may have a potential early benefit to nursery pig health status.

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, Hillerod, 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.

Despite single nucleotide polymorphism (SNP) availability and frequent cost reduction has allowed genome-wide association studies even in complex traits as tick resistance, the use of this information source in SNP by environment interaction context is unknown for many economically important traits in cattle. We aimed at identifying putative genomic regions explaining differences in tick resistance in Hereford and Braford cattle under SNP by environment point of view as well as to identify candidate genes derived from outliers/significant markers. The environment was defined as contemporary group means of tick counts, since they seemed to be the most appropriate entities to describe the environmental gradient in beef cattle. A total of 4363 animals having tick counts (n=10 673) originated from 197 sires and 3966 dams were used. Genotypes were acquired on 3591 of these cattle. From top 1% SNPs (410) having the greatest effects in each environment, 75 were consistently relevant in all environments, which indicated SNP by environment interaction. The outliers/significant SNPs were mapped on chromosomes 1, 2, 5, 6, 7, 9, 11, 13, 14, 15, 16, 18, 21, 23, 24, 26 and 28, and potential candidate genes were detected across environments. The presence of SNP by environment interaction for tick resistance indicates that genetic expression of resistance depends upon tick burden. Markers with major portion of genetic variance explained across environments appeared to be close to genes with different direct or indirect functions related to immune system, inflammatory process and mechanisms of tissue destruction/repair, such as energy metabolism and cell differentiation.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain β-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.

A study was conducted to evaluate the effects of a multi-strain Bacillus subtilis-based direct-fed microbial (DFM) on apparent digestibility and colonic pH of nursery pigs. Eighty pigs, of equal number of barrows and gilts (initial BW: 6.99 ± 1.67 kg), were weaned at 21 ± 1 d and randomly allotted to sixteen pens, with five pigs per pen. Two dietary treatments were implemented, a basal control (CON) and a control plus DFM (DFM). Both diets were corn, soybean meal, and distillers dried grains based, formulated to meet all or exceed all nutritional requirements, and manufactured on site. Diets were fed for 42 days. Performance measures were recorded weekly. 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. Digestibility of specific nutrients was evaluated within the duodenum, jejunum, ileum, ascending and distal colon. There were no overall differences in growth performance. Overall means ± SD were 0.51 ± 0.05 kg/d, 0.79 ± 0.05 kg/d and 0.66 ± 0.05 for ADG, ADFI, and G:F, respectively. Digestibility of tryptophan within the jejunum tended (P = 0.06) to increase with addition of DFM, as did cysteine (P = 0.12) and methionine (P = 0.10). The analysis also suggested that the impact of the DFM on the digestibility of amino acids may be early in the nursery phase. The pH of contents in ascending colon, a possible indicator of varied fiber digestion, did not differ. Likewise, no differences were observed between treatment in apparent total tract nitrogen and energy digestibility (analysis of distal colon contents). The addition of a multi-strain Bacillus subtilis-based DFM appears to impact digestibility of select amino acids depending upon location in the gastrointestinal tract.

Allocation of treatments to experimental units (EUs) is done at random. In the presence of a concomitant variable (e.g., initial body weight; iBW), one strategy is to block WUs into iBW groups. However, in some scientific manuscripts, EUs are sorted by iBW and then allocated to treatments (e.g., treatments “A” and “B”) based on iBW, such that the lightest EU receives “A,” the second and third lightest receive “B,” etc. Although this strategy guarantees similar iBW between treatments, this ignores the random process required for statistical analysis of the data. We aimed to quantify the impact of lack of randomization on the statistical power and type I error of completely randomized designs (CRD). Data were simulated for ADG using two treatments (“A” having 50 g/d more than “B,” and MSE=1250 g2/d2). Data were simulated for different replicates per treatment (RepsPerTreat; from 3 to 18, every 3). We used two scenarios for the correlations between iBW and ADG (ρ ADG,iBW): 0 and 0.5. Treatments were allocated to EUs at random (CRD) or according to the order of EUs based on iBW (completely non-randomized design; CNRD). The model included the fixed-effects of intercept and treatment. For ρ ADG,iBW=0, results showed that CRD had greater statistical power (POW) than CNRD for RepsPerTreat from 3 to 9, whereas CNRD had greater from 12 to 18. For ρ ADG,iBW=0.5, CNRD had an even greater POW than CRD starting at 9 RepsPerTreat. Although the type I error (ERROR) of CRD were close to 5% across all scenarios with different RepsPerTreat, CNRD had consistently greater and lower ERROR than CRD with =0 and 0.5, respectively. Having ERROR deviating from 5% is not expected. Visual inspection of the F-values of these models when the null hypothesis was true showed that a distribution other than the theoretical F-distribution, indicating that the statistical test is not valid. Sorting EUs by iBW does not guarantee greater statistical power but results in invalid F-tests.

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations.

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.

A greater number of dairy economic selection indexes are incorporating a measure of feed efficiency (FE) as a key trait. Definitions of FE traits have ranged from dry matter intake (DMI) to residual feed intake (RFI), noting that RFI is effectively DMI adjusted for various energy sink traits such as body weight (BW) and milk energy (MilkE). Other definitions of FE fall between these 2 extremes such as feed saved (FS), which combines RFI and the portion of DMI required to maintain BW. The choice between different FE traits can create confusion as to how to meaningfully compare their heritabilities, estimated breeding values (EBV) and their corresponding reliabilities, and how to differentially incorporate these EBV into selection indexes. If RFI and FS are merely linear functions of DMI, BW, and MilkE with known genetic variances and covariances between these 3 traits, there may be no need to directly compute RFI or FS phenotypes to determine their heritabilities, genetic correlations, EBV, and respective reliabilities for individual animals. We demonstrate how the estimated total genetic merit is invariant to the specification of a FE trait within a selection index. That is, economic weights for a selection index involving one particular FE trait readily convert into the economic weights for a selection index involving a different linear function of that FE trait. We use these different specifications of FE to provide insight as to the effect of the degree of missingness (e.g., paucity of DMI relative to milk yield records) on the EBV accuracies of the various derivative FE traits. We particularly highlight that the generally observed higher EBV accuracies for DMI, then for FS, and lastly for RFI are partly driven by the greater genetic correlations of DMI with BW and MilkE and of FS with BW. Finally, we advocate a genetic regression approach to deriving FS and RFI recognizing that genetic versus residual relationships between FE component traits may differ substantially from each other.

Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.

Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold -means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased.

Feed efficiency, as defined by the fraction of feed energy or dry matter captured in products, has more than doubled for the US dairy industry in the past 100 yr. This increased feed efficiency was the result of increased milk production per cow achieved through genetic selection, nutrition, and management with the desired goal being greater profitability. With increased milk production per cow, more feed is consumed per cow, but a greater portion of the feed is partitioned toward milk instead of maintenance and body growth. This dilution of maintenance has been the overwhelming driver of enhanced feed efficiency in the past, but its effect diminishes with each successive increment in production relative to body size and therefore will be less important in the future. Instead, we must also focus on new ways to enhance digestive and metabolic efficiency. One way to examine variation in efficiency among animals is residual feed intake (RFI), a measure of efficiency that is independent of the dilution of maintenance. Cows that convert feed gross energy to net energy more efficiently or have lower maintenance requirements than expected based on body weight use less feed than expected and thus have negative RFI. Cows with low RFI likely digest and metabolize nutrients more efficiently and should have overall greater efficiency and profitability if they are also healthy, fertile, and produce at a high multiple of maintenance. Genomic technologies will help to identify these animals for selection programs. Nutrition and management also will continue to play a major role in farm-level feed efficiency. Management practices such as grouping and total mixed ration feeding have improved rumen function and therefore efficiency, but they have also decreased our attention on individual cow needs. Nutritional grouping is key to helping each cow reach its genetic potential. Perhaps new computer-driven technologies, combined with genomics, will enable us to optimize management for each individual cow within a herd, or to optimize animal selection to match management environments. In the future, availability of feed resources may shift as competition for land increases. New approaches combining genetic, nutrition, and other management practices will help optimize feed efficiency, profitability, and environmental sustainability.

To include feed-intake-related traits in the breeding goal, accurate estimates of genetic parameters of feed intake, and its correlations with other related traits (i.e., production, conformation) are required to compare different options. However, the correlations between feed intake and conformation traits can vary depending on the population. Therefore, the objective was to estimate genetic correlations between 6 feed-intake-related traits and 7 conformation traits within dairy cattle from 2 countries, the Netherlands (NL) and the United States (US). The feed-intake-related traits were dry matter intake (DMI), residual feed intake (RFI), milk energy output (MilkE), milk yield (MY), body weight (BW), and metabolic body weight (MBW). The conformation traits were stature (ST), chest width (CW), body depth (BD), angularity (ANG), rump angle (RA), rump width (RW), and body condition score (BCS). Feed intake data were available for 1,665 cows in NL and for 1,920 cows in US, from 83 nutritional experiments (48 in NL and 35 in US) conducted between 1991 and 2011 in NL and between 2007 and 2013 in US. Additional conformation records from relatives of the animals with DMI records were added to the database, giving a total of 37,241 cows in NL and 28,809 in US with conformation trait information. Genetic parameters were estimated using bivariate animal model analyses. The model included the following fixed effects for feed-intake-related traits: location by experiment-ration, age of cow at calving modeled with a second order polynomial by parity class, location by year-season, and days in milk, and these fixed effects for the conformation traits: herd by classification date, age of cow at classification, and lactation stage at classification. Both models included additive genetic and residual random effects. The highest estimated genetic correlations involving DMI were with CW in both countries (NL = 0.45 and US = 0.61), followed by ST (NL = 0.33 and US = 0.57), BD (NL = 0.26 and US = 0.49), and BCS (NL = 0.24 and US = 0.46). The MilkE and MY were moderately correlated with ANG in both countries (0.33 and 0.47 in NL, and 0.36 and 0.48 in US). Finally, BW was highly correlated with CW (0.77 in NL and 0.84 in US) and with BCS (0.83 in NL and 0.85 in US). Feed-intake-related traits were moderately to highly genetically correlated with conformation traits (ST, CW, BD, and BCS) in both countries, making them potentially useful as predictors of DMI.

Many laying hen companies in the United States are pledging to move away from intensive conventional cages to extensive housing systems. Enriched colony cages (ECC) are a practical alternative to conventional cage systems. Scientific research is limited on the effects of ECC on hen production and welfare. Therefore, the objective of this study was to evaluate the effects of stocking density on welfare and performance with the overall outcome to provide guidance on stocking density for ECC. At 16 wk, W-36 pullets were placed into 2 commercial ECC housing systems. Within each ECC enclosure, hens were allocated into 1 of 6 stocking densities: A) 465 to 484 cm2/bird, B) 581 to 606 cm2/bird, C) 652 to 677 cm2/bird, D) 754 to 780 cm2/bird, E) 799 to 832 cm2/bird, and F) 923 to 955 cm2/bird. Body weight, egg production, mortality, and Welfare Quality data were collected each 28 d period from 17 to 68 wk. The 6 ECC stocking densities had several transient effects on production measures within age periods with no difference in hen-day production (P > 0.05). Body weight was affected by stocking density (P < 0.05) where hens raised at stocking density A (465 to 484 cm2/bird) weighed at least 25 g less than hens from other stocking densities. Stocking density differences for Welfare Quality assessments were only apparent for feather coverage. Hens raised at stocking density A (465 to 484 cm2) consistently had the poorest (P < 0.05) crop, keel, belly, back, and rump feather coverage. The keel, neck, and back body regions had poorer feather coverage when hens were raised at stocking densities B (581 to 606 cm2) and C (652 to 677 cm2) compared to hens from lower stocking densities (P < 0.05). Therefore, the minimum area per hen housed in commercial ECC systems should be 754 cm2 per bird for greater feather coverage.

Anti-Mullerian hormone (AMH) is an ovarian growth factor that plays an important role in regulation of ovarian follicle growth. The objectives of this study were to estimate the genomic heritability of AMH and identify genomic regions associated with AMH production in a genome-wide association (GWA) analysis. Concentrations of AMH were determined in 2,905 dairy Holstein heifers genotyped using the Zoetis medium density panel (Zoetis Inclusions, Kalamazoo, MI) with 54,519 single nucleotide polymorphism (SNP) markers remaining after standard genotype quality control edits. A linear mixed model was used to model the random effects of sampling day and genomics on the logarithm of AMH. The genomic heritability (± standard error of the mean) of AMH was estimated to be 0.36 ± 0.03. Our GWA analysis inferred significant associations between AMH and 11 SNP markers on chromosome 11 and 1 SNP marker on chromosome 20. Annotated genes with significant associations were identified using the Ensembl genome database (version 88) of the cow genome (version UMD 3.1; https://www.ensembl.org/biomart). Gene set enrichment analysis revealed that 2 gene ontology (GO) terms were significantly enriched in the list of candidate genes: G-protein coupled receptor signaling pathway (GO:0007186) and the detection of chemical stimulus involved in sensory perception (GO:0050907). The estimated high heritability and previously established associations between AMH and ovarian follicular reserve, fertility, longevity, and superovulatory response in cattle implies that AMH could be used as a biomarker for genetic improvement of reproductive potential.

More than 90% of the commercial egg production in the United States is pledged to be in cage-free systems by 2025. Management practices like induced molting and litter area management have come under scrutiny because of the housing system change. The aim of this study was to determine the welfare and production implications of different litter substrates and also evaluate induced molting of hens in a cage-free system. Bovan White hens were housed in a multi-tier aviary system with daily access to open litter area of either Astroturf (AT), wood shavings (SH), or straw (ST) and bare concrete floor (CO) serving as control. At 68 wk of age, molt was induced in half of the hens whereas the other half continued without molting to 116 wk. Production and welfare parameters were measured periodically throughout first and second cycles. Litter substrate did not influence hen-day production and case-weight measurements. However, CO had the lowest total number of eggs produced during the first cycle (P < 0.05). Hen-day percentage was approximately 14% greater in molted hens during the second cycle with egg case weight being heavier in non-molt hens toward the end of second cycle (P < 0.05). The only welfare parameter influenced by litter substrate during the first cycle was a greater crop feather loss in AT than ST at mid-lay (P < 0.05). Keel deformations increased with age irrespective of the litter substrate with 91.5% of palpated hens having keel deformations at the end of first cycle (P < 0.05). Molting did not influence the keel palpation and footpad scores whereas frequency of moderate comb wound was greater in molt hens during molt (P < 0.05). Severe feather loss was seen in non-molt hens during the second cycle (P < 0.05). Litter substrate does not affect production and physical parameters of welfare of hens in a multi-tier aviary system. Additionally, induced molting can be successfully carried out in the multi-tier cage-free system.

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.

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.

Whole genome prediction (WGP) based on high density SNP marker panels is known to improve the accuracy of breeding value (BV) prediction in livestock. However, these accuracies can be compromised when genotype by environment interaction (G×E) exists but is not accounted for. Reaction norm (RN) and random regression (RR) models have proven to be useful in accounting for G×E in pre-WGP evaluations by modeling BV as linear or higher order functions of environmental or temporal covariates. We extend these RR/RN models based on several alternative specifications for SNP-specific intercepts and linear slopes on environmental covariates. One specification is based on bivariate normality (BVN) of SNP-specific intercepts and slopes, whereas 2 others, IW-BayesA and based on inverted Wishart (IW) extensions IW-BayesB, are, respectively, bivariate Student t extensions of currently popular models without (BayesA) or with (BayesB) variable selection. We highlight alternative specifications based on the square root free Cholesky decomposition (CD) of SNP-specific variance-covariance (VCV) matrices in an attempt to better differentially model environmentally sensitive from environmentally robust QTL. Two CD specifications were considered with (CD-BayesB) or without (CD-BayesA) any variable selection on intercept and slope effects. We compared each of the 5 models based on an RN simulation study. Six scenarios were considered based on differences in overall genetic correlations between SNP-specific intercept and slope effects as well as on heritabilities and numbers of environmentally robust versus sensitive QTL. In most scenarios, IW-BayesA had the greatest accuracy, whereas CD-BayesB exhibited the greatest accuracy in low complexity architectures (i.e., low number of QTL). In an RR application of a Duroc × Pietrain resource population at Michigan State University, 5,271 SNP markers and 928 F2 animals with known pedigree were analyzed for backfat thickness at wk 10, 13, 16, 19, and 22. SNP-based RR methods had a 2.5% greater (P < 0.0001) cross-validation accuracy for predicting phenotypes than the SNP-based conventional BayesA/BayesB and/or pedigree based RR BLUP; however, none of the proposed RR models had performances that were different from each other.

Our objective was to determine the effects of dry matter intake (DMI), body weight (BW), and diet characteristics on total tract digestibilities of dry matter, neutral detergent fiber, and starch (DMD, NDFD, and StarchD, respectively) in high-producing dairy cows. Our database was composed of 1,942 observations from 662 cows in 54 studies from Michigan, Ohio, and Georgia. On average, cows ate 23 ± 4.5 kg of dry matter/d, weighed 669 ± 79 kg, and produced 38 ± 10 kg of milk/d. Diets were 31 ± 5% neutral detergent fiber, 27 ± 6% starch, 2.6 ± 1.2% fatty acids, and 17 ± 1.4% crude protein. Digestibility means were 66 ± 6, 42 ± 11, and 93 ± 5% for DMD, NDFD, and StarchD, respectively. Forage sources included corn silage, alfalfa, and grasses. Corn source was classified by its ruminal fermentability. Data were analyzed using a mixed effects model, including diet chemical composition, forage source, and corn source, all expressed as percentage of dry matter, except for DMI, which was expressed as percentage of BW (DMI%BW); location and 2-way interactions were fixed effects. Cow, block, period, treatment, and study were included as random effects. Best fitting candidate models were generated using backward and stepwise regression methods. Additionally, the simplest model was generated using only DMI and location as fixed effects and all random effects. Candidate models were cross-validated across studies, and the resulting predictive correlation coefficients across studies (PC) and root mean square error of prediction (RMSEP) were compared by t-test. For each nutrient, the digestibility model that resulted in the highest PC and lowest RMSEP was determined to be the best fitting model. We observed heterogeneous coefficients among the different locations, suggesting that specific location factors influenced digestibilities. The overall location-averaged best fitting prediction equations were: DMD = 69 - 0.83 × DMI%BW (PC = 0.22, RMSEP = 5.39); NDFD = 53 + 0.26 × grass %DM - 0.59 × starch %DM + 3.06 × DMI%BW - 0.46 × DMI%BW2 (PC = 0.53, RMSEP = 9.70); and StarchD = 96 + 0.19 × HFERM%DM - 0.12 × starch %DM - 1.13 × DMI%BW (PC = 0.34, RMSEP = 4.77); where HFERM%DM is highly-fermentable corn source as percentage of DM. Our results confirm that digestibility is reduced as DMI increases, albeit at a lower rate than that reported in National Research Council. Furthermore, dietary starch depresses NDFD. Whereas DMD can be predicted based on DMI only, the best predictions for NDFD and StarchD require diet characteristics in addition to DMI.

peer-reviewed Previous studies have documented that ovarian antral follicle count (AFC) is positively correlated with number of healthy follicles and oocytes in ovaries (ovarian reserve), as well as ovarian function and fertility in cattle. However, environmental factors (e.g., nutrition, steroids) during pregnancy in cattle and sheep can reduce AFC in offspring. The role that genetic and environmental factors play in influencing the variability in AFC and, correspondingly, the size of the ovarian reserve, ovarian function, and fertility, are, however, poorly understood. The present study tests the hypothesis that variability in AFC in offspring is influenced not only by genetic merit but also by the dam age and lactation status (lactating cows vs. nonlactating heifers) and milk production during pregnancy. Antral follicle count was assessed by ultrasonography in 445 Irish Holstein-Friesian dairy cows and 522 US Holstein-Friesian dairy heifers. Heritability estimates for AFC (± standard error) were 0.31 ± 0.14 and 0.25 ± 0.13 in dairy cows and heifers, respectively. Association analysis between both genotypic sire data and phenotypic dam data with AFC in their daughters was performed using regression and generalized linear models. Antral follicle count was negatively associated with genetic merit for milk fat concentration. Also, AFC was greater in offspring of dams that were lactating (n = 255) compared with nonlactating dams (n = 89) during pregnancy and was positively associated with dam milk fat concentration and milk fat-to-protein ratio. In conclusion, AFC in dairy cattle is a moderately heritable genetic trait affected by age or lactation status and milk quality but not by level of dam’s milk production during pregnancy.

In response to high consumer demand, turkeys have been intensively selected for rapid growth rate and breast muscle mass and conformation. The success in breeding selection has coincided with an increasing incidence of pale, soft, and exudative (PSE) meat defect, especially in response to heat stress. We hypothesized that the underlying mechanism responsible for the development of PSE meat arises from differences in expression of several critical genes. The objective of this study was to determine differential gene expression between normal and PSE turkey meat using a 6K turkey skeletal muscle long oligonucleotide microarray. Breast meat samples were collected from Randombred Control Line 2 turkeys at 22 wk of age, and classified as normal or PSE primarily based on marinade uptake (high = normal, low = PSE). Total RNA was isolated from meat samples with the highest (normal, n = 6) and the lowest (PSE, n = 6) marinade uptake. Microarray data confirmation was conducted using quantitative real-time PCR. Selection of differentially expressed genes for pathway analysis was performed using a combination of fold change (FC) ranking (FC-1.66, FC1.66) and false discovery rate (0.35) as criteria. The calcium signaling pathway was highlighted as the top canonical pathway associated with differential gene expression between normal and PSE turkey. Dramatic downregulation of fast-twitch myosin heavy chain coupled with upregulation of slow-twitch myosin and troponin C suggested a switch of skeletal muscle isoforms, which may alter muscle fiber arrangement and formation of actin-myosin complexes. Changes in expression of genes in the actin cytoskeleton signaling pathway also suggest altered structures of actin filaments that may affect cell motility as well as strength and flexibility of muscle cells. Substantial downregulation of pyruvate dehydrogenase kinase, isozyme 4 was observed in PSE samples, suggesting altered regulation of the aerobic metabolic pathway in the birds that developed PSE meat defect.

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker x environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across environment models. Agriculture and Food Research Initiative Competitive Grants from the USDA National Institute of Food and Agriculture (Washington, DC) [2008-35205-18711, 2011-68004-30340]; Hatch grant from the Wisconsin Agricultural Experiment Station (Madison, WI) [MSN139239]; National Association of Animal Breeders (Columbia, MO) This project was supported by Agriculture and Food Research Initiative Competitive Grants no. 2008-35205-18711 and 2011-68004-30340 from the USDA National Institute of Food and Agriculture (Washington, DC). Support from Hatch grant no. MSN139239 from the Wisconsin Agricultural Experiment Station (Madison, WI) is acknowledged, and K. A. Weigel acknowledges partial financial support from the National Association of Animal Breeders (Columbia, MO). Public domain – authored by a U.S. government employee

The objectives of this work were to assess alternative linear reaction norm (RN) models for genetic evaluation of Angus cattle in Brazil. That is, we investigated the interaction between genotypes and continuous descriptors of the environmental variation to examine evidence of genotype by environment interaction (G×E) in post-weaning BW gain (PWG) and to compare the environmental sensitivity of national and imported Angus sires. Data were collected by the Brazilian Angus Improvement Program from 1974 to 2005 and consisted of 63,098 records and a pedigree file with 95,896 animals. Six models were implemented using Bayesian inference and compared using the Deviance Information Criterion (DIC). The simplest model was M1, a traditional animal model, which showed the largest DIC and hence the poorest fit when compared with the 4 alternative RN specifications accounting for G×E. In M2, a 2-step procedure was implemented using the contemporary group posterior means of M1 as the environmental gradient, ranging from −92.6 to +265.5 kg. Moreover, the benefits of jointly estimating all parameters in a 1-step approach were demonstrated by M3. Additionally, we extended M3 to allow for residual heteroskedasticity using an exponential function (M4) and the best fitting (smallest DIC) environmental classification model (M5) specification. Finally, M6 added just heteroskedastic residual variance to M1. Heritabilities were less at harsh environments and increased with the improvement of production conditions for all RN models. Rank correlations among genetic merit predictions obtained by M1 and by the best fitting RN models M3 (homoskedastic) and M5 (heteroskedastic) at different environmental levels ranged from 0.79 and 0.81, suggesting biological importance of G×E in Brazilian Angus PWG. These results suggest that selection progress could be optimized by adopting environment-specific genetic merit predictions. The PWG environmental sensitivity of imported North American origin bulls (0.046 ± 0.009) was significantly larger (P < 0.05) than that of local sires (0.012 ± 0.013). Moreover, PWG of progeny of imported sires exceeded that of native sires in medium and superior production levels. On the other hand, Angus cattle locally selected in Brazil tended to be more robust to environmental changes and hence be more suitable when production environments for potential progeny is uncertain.

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 s2. 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 s2 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 s2 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 s2 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

Summary Skeletal muscle development is a complex process involving the coordinated expression of thousands of genes. The aim of this study was to identify differentially expressed genes in longissimus dorsi (LD) muscle of pigs at 40 and 70 days (d) of gestation (developmental stages encompassing primary and secondary fibre formation) in Yorkshire–Landrace (YL) cross-bred pigs and Piau pigs (a naturalized Brazilian breed), which are two breed types that differ in muscularity. Foetuses were obtained from gilts at each gestational age (n = 3 YL; n = 4 Piau), and transcriptional profiling was performed using the Pigoligoarray microarray containing 20 400 oligonucleotides. A total of 486 oligonucleotides were differentially expressed (fold change (FC) ≥ 1.5; false discovery rate (FDR) ≤ 0.05) between 40 and 70 d gestation in either YL or Piau pigs, and a total of 1300 oligonucleotides were differentially expressed (FC ≥ 1.5; FDR ≤ 0.05) between YL and Piau pigs at either age. Gene ontology annotation and pathway analyses determined functional classifications for differentially expressed genes and revealed breed type–specific developmental expression patterns. Thirteen genes were selected for confirmation by qRT-PCR analyses, and expression patterns for most of these genes were confirmed, providing further insight into the roles of these genes in pig muscle development. This study revealed both developmental and breed type–specific patterns of gene expression in foetal pig skeletal muscle, including genes not previously associated with myogenesis. This information will contribute to future pig genetic improvement efforts.

Summary Consumer demand for lean, inexpensive meat products has driven the domestic turkey (Meleagris gallopavo) industry to unprecedented production; however, this has coincided with an increase in growth-induced myopathies and meat quality defects. With the aim of developing a new tool for the study of turkey growth and development at the muscle transcriptome level, a 6K oligonucleotide microarray was constructed, the Turkey Skeletal Muscle Long Oligo (TSKMLO) microarray. Skeletal muscle samples were collected at three critical stages in muscle development: 18-day embryo (hyperplasia), 1-day post-hatch (hypertrophy), and 16-week (market age) from two genetic lines of turkeys: RBC2, a line maintained without selection pressure, and F, a line selected from the RBC2 line for increased 16-week body weight. Oligonucleotides were designed from sequences obtained from skeletal muscle cDNA libraries from the three developmental stages. Several unique controls, including mismatch and distance controls and scrambled sequences, were designed for 30 genes. Quality control hybridizations were completed, confirming the validity and repeatability of the array. Control features were evaluated across two larger experiments comparing developmental stage within genetic line or genetic line within each developmental stage, totaling 70 arrays. Mismatch and scrambled sequences appeared to be useful controls of specific hybridization for most genes. In addition, quantitative real-time RT-PCR confirmed microarray results. This creation and assessment of the TSKMLO array provides a valuable community resource for the study of gene expression changes related to turkey muscle growth and development.

Genetic evaluations should become more accurate with the advent of whole genome selection (WGS) based on high density SNP panels. The use of WGS should then accelerate genetic gains for production traits given likely decreases in generation interval due to the greater intent to select more animals based just on their genotypes rather than phenotypes. However, past and current genetic evaluations may not generally connect well to the intended scope of inference. For example, estimating haplotype effects from the data of a single reference population does not bode well for the use of WGS in other diverse environments since the scope of inference is too narrow; conversely, WGS based on estimates, for example, derived from daughter yield deviations of dairy bulls may be too broad to infer upon genetic merit under any one particular environment. The treatment of contemporary group effects as random rather than as fixed, heterogeneous variances, genotype by environment interaction, and multiple trait analyses are all important scope of inference issues that are discussed in this review. Management systems and environments have and will continue to change; hence, it is vital that genetic evaluations are as robust and scope-appropriate as is possible in order to optimize animal adaptation to these changes.

Milk allergy is the most common type of food allergy in humans with the potential for fatality. An adjuvant-free mouse model would be highly desirable as a preclinical research tool to develop novel hypoallergenic or nonallergenic milk products. Here we describe an adjuvant-free mouse model of milk allergy that uses transdermal sensitization followed by oral challenge with milk protein. Groups of BALB/c mice were exposed to milk whey protein via a transdermal route, without adjuvant. Systemic IgG1 and IgE antibody responses to transdermal exposure as well as systemic anaphylaxis and hypothermia response to oral protein challenge were studied. Transdermal exposure resulted in a time- and dose-dependent induction of significant IgE and IgG1 antibody responses. Furthermore, oral challenge of sensitized mice resulted in significant clinical symptoms of systemic anaphylaxis within 1 h and significant hypothermia at 30 min postchallenge. To study the underlying mechanism, we examined allergen-driven spleen cell T-helper 2 cytokine (IL-4) responses. There was a robust dose- and time-dependent activation of memory IL-4 responses in allergic mice but not in healthy control mice. These data demonstrate for the first time a novel transdermal sensitization followed by oral challenge mouse model of milk allergy that does not use adjuvant. It is expected that this model may be used not only to study mechanisms of milk allergy, but also to evaluate novel milk products for allergenic potential and aid in the production of hypo- or nonallergenic milk products.

Dados extremos influenciados por fatores não considerados no modelo estatístico, podem enviesar as estimativas dos parâmetros e valores genéticos. Além disso, diversas características de importância econômica não seguem uma distribuição normal ou apresentam dados censurados. O objetivo deste trabalho é descrever e ilustrar a aplicação de modelos hierárquicos bayesianos para a detecção e mitigação de dados extremos e para análise de dados censurados. Primeiro, é apresentada a especificação tradicional do modelo animal em estágios hierárquicos sob o enfoque bayesiano, para dados não censurados com distribuição Normal. A seguir, esse modelo é generalizado pela introdução de uma variável de ponderação independente, que permite a especificação de densidades residuais de caudas longas da família de distribuições Normal/independente. Finalmente, para contemplar a análise de dados censurados, o modelo básico é ampliado pela inclusão de uma variável com distribuição normal truncada no limite inferior do valor observado da característica no momento da avaliação, para aqueles animais que ainda não completaram sua vida reprodutiva no momento da avaliação.

Because of increasing constraints placed on conducting large studies at universities, more research is being conducted on commercial dairies, thereby raising some implications for experimental designs and data analysis. For example, experimental units are often specified to be pens of animals in on-farm studies, thereby requiring that at least 2 pens be used per treatment group in a single-dairy study. Even when treatments are compared within pens, the precision of inference on treatment differences is still primarily limited by the number of pens in the study, rather than the number of cows per treatment in each pen. Other challenges with on-farm studies include proper blocking and randomization of cows or pens to treatments. On the other hand, multiple farm studies are attractive, because they facilitate a broader scope of inference on treatment effects across a wider range of management or climatic conditions and genetic backgrounds compared with single-site university studies. Furthermore, studies based on multiple farms or multiple pens within a single large farm can facilitate greater power for treatment comparisons on binary reproduction or health responses than can be achieved at a smaller research herd. Because quantitative geneticists have been analyzing commercial dairy data for decades, they have developed useful data analysis techniques that should be harnessed to facilitate even greater statistical scope and power for on-farm studies.

The objectives of these experiments were to compare differentiation of bovine stromal-vascular (S-V) cells isolated from i.m. and s.c. adipose tissues in response to a glucocorticoid and a peroxisome proliferator-activated receptor gamma agonist. Stromal-vascular cells were isolated from i.m. and s.c. fat depots of 3 Angus steers and propagated in culture. Cells were exposed to differentiation media containing 0.25 microM dexamethasone (DEX), a glucocorticoid analog, and 40 microM troglitazone (TRO), a peroxisome proliferator-activated receptor gamma agonist, or both. Cells treated with DEX and TRO had greater (P 0.59) or depot differences (P = 0.41) in glycerol-3-phosphate dehydrogenase activity were found. Morphological assessment of adipogenic colonies showed that DEX induced a 1.8-fold increase in the percentage of adipogenic colonies (P = 0.03), whereas TRO increased the proportion of adipogenic colonies by 1.9-fold (P = 0.02) compared with those not treated with DEX or TRO, respectively. Depots had a similar percentage of adipogenic colonies (P = 0.18); however, the percentage of differentiated cells within adipogenic colonies was found to be 6.4-fold greater in s.c. isolates compared with i.m. (P < 0.001). Addition of TRO increased the proportion of differentiated cells within colonies by 10-fold compared with those of nontreated colonies (P < 0.001), whereas the percentage of differentiated cells within adipogenic colonies only tended to be increased by DEX (P = 0.10). These data indicate that bovine i.m. and s.c. S-V cells are capable of enhanced differentiation in response to DEX and TRO, and these effects were additive. Most importantly, inherent differences in the capacity to differentiate exist between adipogenic bovine i.m. and s.c. S-V cells.

Pigs from the F(2) generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting growth and composition traits. Body weight and ultrasound estimates of 10th-rib backfat, last-rib backfat, and LM area were serially measured throughout development. Estimates of fat-free total lean, total body fat, empty body protein, empty body lipid, and ADG from 10 to 22 wk of age were calculated, and random regression analyses were performed to estimate individual animal phenotypes representing intercept and linear rates of increase in these serial traits. A total of 510 F(2) animals were genotyped for 124 micro-satellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression, interval mapping methods using sex and litter as fixed effects. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined at the chromosome- and genome-wise levels by permutation tests. A total of 43 QTL for 22 of the 29 measured traits were found to be significant at the 5% chromosome-wise level. Of these 43 QTL, 20 were significant at the 1% chromosome-wise significance threshold, 14 of these 20 were also significant at the 5% genome-wise significance threshold, and 10 of these 14 were also significant at the 1% genome-wise significance threshold. A total of 22 QTL for the animal random regression terms were found to be significant at the 5% chromosome-wise level. Of these 22 QTL, 6 were significant at the 1% chromosome-wise significance threshold, 4 of these 6 were also significant at the 5% genome-wise significance threshold, and 3 of these 4 were also significant at the 1% genome-wise significance threshold. Putative QTL were discovered for 10th-rib and last-rib backfat on SSC 6, body composition traits on SSC 9, backfat and lipid composition traits on SSC 11, 10th-rib backfat and total body fat tissue on SSC 12, and linear regression of last-rib backfat and total body fat tissue on SSC 8. These results will facilitate fine-mapping efforts to identify genes controlling growth and body composition of pigs that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations.

Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A widely accepted yet occasionally contested measure of FE in dairy cattle is residual feed intake (RFI). The use of RFI is limiting for several reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI, and potential differences in genetic versus nongenetic relationships between dry matter intake (DMI) and FE component traits. Hence, analyses focusing on DMI as the response are often preferred. We propose an alternative multiple-trait (MT) modeling strategy that exploits the Cholesky decomposition to provide a potentially more robust measure of FE. We demonstrate that our proposed FE measure is identical to RFI provided that genetic and nongenetic relationships between DMI and component traits of FE are identical. We assessed both approaches (MT and RFI) by simulation as well as by application to 26,383 weekly records from 50 to 200 d in milk on 2,470 cows from a dairy FE consortium study involving 7 institutions. Although the proposed MT model fared better than the RFI model when simulated genetic and nongenetic associations between DMI and FE component traits were substantially different from each other, no meaningful differences were found in predictive performance between the 2 models when applied to the consortium data.

Mixing litters of pigs often leads to short- or long-lasting aggression. As social recognition (SR) is important for the development and maintenance of stable social groups, a lack or disruption of SR could prolong agonistic interactions and reduce welfare. Investigations of SR abilities are therefore of considerable importance. However, experimental assessment of SR generally involves moving subjects to unfamiliar environments, or excessive handling, or aggression during the familiarisation period (in which subjects become acquainted prior to testing their ability to recognise each other), which in turn may change the outcome of the test. In this study, we tested a novel method for assessing SR in young pigs aiming to minimise these problems. For the familiarisation procedure, flexible netting was placed between farrowing pens allowing physical contact between litters but preventing unwanted mixing and fighting. We also investigated whether recognition of a familiar individual is affected by relocation to a novel environment. Forty-eight piglets from 12 litters (4 piglets/litter) were tested in a split plot design with litters as the experimental units for comparing two familiarity (familiar/unfamiliar) and three relocation (no relocation/relocation to pens with the same spatial orientation/relocation to pens with a different spatial orientation) treatments. Results indicated that piglets exposed to unfamiliar animals spent more time on social investigation than those exposed to familiar ones, suggesting that SR can be successfully tested using this novel approach. The relocation treatment did not influence the amount of time spent on social investigation, but did affect duration on exploration of the arena. Piglets meeting familiar individuals were more susceptible to this effect after relocation than piglets meeting unfamiliar conspecifics.

Accurate evaluations of growth and composition traits enable better management decisions regarding genetic merit, feeding, and marketing. Sires from Duroc and Pietrain populations were used to produce crossbred pigs, which were evaluated for growth and composition traits. All parents were normal for the ryanodine receptor gene. Boars from each breed were mated to either Yorkshire or F1 Yorkshire-Landrace females with 307 offspring evaluated from birth through 26 wk of age. No significant differences between sire breeds were seen for pig BW from birth through 10 wk of age. Body weight, 10th rib backfat (BF10), last rib backfat (LRF), and loin muscle area (LMA) were serially measured at 10, 13, 16, 19, 22, 24, and 26 wk of age. At 26 wk of age, Duroc-sired progeny were heavier (143.4 vs. 132.7 kg, P < 0.001), had more BF10 (27.1 vs. 23.7 mm, P < 0.001) and LRF (21.2 vs. 19.2 mm, P < 0.001), but had similar LMA (46.4 vs. 47.1 cm2) compared with Pietrain-sired progeny. Mean feed efficiency did not differ between breed of sire in any period of the study. Duroc progeny had a greater ADG (980.1 vs. 892.3 g/d, P < 0.001) from 10 to 26 wk of age than Pietrain-sired pigs. Composition traits of fat-free total lean (FFTOLN), total fat tissue (TOFAT), empty body protein (EBPRO), and empty body lipid (EBLIPID) were calculated. Random regression animal models with polynomial regression on week on-test were fitted to BW, BF10, LRF, LMA, FFTOLN, TOFAT, EBPRO, and EBLIPID from 10 to 26 wk of age. Duroc-sired barrows tended to grow faster but with more fat tissue, and Pietrain-sired gilts were slower growing but leaner, whereas Duroc-sired gilts and Pietrain-sired barrows were intermediate for growth and backfat measures. Serial heritability estimates generally increased from 10 to 26 wk of age with ranges as follows: BW (0.05 to 0.39), BF10 (0.13 to 0.76), LRF (0.11 to 0.79), LMA (0.05 to 0.73), FFTOLN (0.07 to 0.16), TOFAT (0.19 to 0.45), EBPRO (0.02 to 0.55), and EBLIPID (0.12 to 0.60). Pigs sired by Duroc and Pietrain boars had similar lean tissue growth but achieved it through different mechanisms.

The objective of this experiment was to compare the effects of dietary mannan oligosaccharide (MOS) and a feed-grade antimicrobial (AM) on growth performance of nursery pigs reared on three different farms (A and B were large-scale commercial farms, and C was located at Michigan State University). On all farms, production was continuous flow by building, but all-in/all-out by room. Within each nursery facility, all pigs on the experiment were in one room. Pigs (Farm A, n = 771, weaning age = 18.4 d; Farm B, n = 576, weaning age = 19.0 d; Farm C, n = 96, weaning age = 20.6 d) were blocked (within farm) by BW and sex and allotted randomly to dietary treatments arranged in a 2 x 2 factorial. The two factors were 1) with and without MOS (0.3% in Phase I, 0.2% in Phases II, III, and IV; as-fed basis) and 2) with and without AM (110 mg of tylosin and 110 mg of sulfamethazine/kg of diet in all phases; as-fed basis). The four nursery phases were 4, 7, 14, and 17 d, respectively. With 35, 20, and 4 pigs per pen on Farms A, B, and C, respectively, space allowances per pig were 0.29, 0.26, and 0.56 m2. Across all farms, the addition of AM and MOS plus AM increased (P < 0.05) ADG (368, 406, and 410 g/d for control, AM, and MOS plus AM, respectively and increased ADFI (661, 703, and 710 g/d for control, AM, and MOS plus AM, respectively) for the entire 42-d experiment. The addition of MOS also increased ADG (P < 0.05) from d 0 to 42 of the experiment (394 g/d). Performance differed depending on farm (P < 0.01). Antimicrobial did not affect growth performance on Farm B, but it increased (P < 0.05) ADG on Farms A and C, ADFI on Farm A, and G:F on Farm C. Growth improvements with MOS on Farms A and B were not significant; however, pigs on Farm C fed MOS had greater (P < 0.05) ADG, ADFI, and G:F than controls. The results of this study suggest that MOS may be an alternative to tylosin and sulfa-methazine as a growth promotant in nursery diets.

Multiple-breed genetic models recently have been demonstrated to account for the heterogenous genetic variances that exist between different beef cattle breed groups. We extend these models to allow for residual heteroskedasticity (heterogeneous residual variances), specified as a function of fixed effects (e.g., sex, breed proportion, breed group heterozygosity) and random effects such as contemporary groups (CG). We additionally specify the residual distributions to be either Gaussian or based on heavier-tailed alternatives such as the Student's t or Slash densities. For each of these three residual densities using either homoskedastic (homogeneous variance) or heteroskedastic error specifications, we analyzed 22,717 postweaning gain records from a Nelore-Hereford population based on a Markov chain Monte Carlo animal model implementation. The heteroskedastic Student's t error model (with estimated df = 7.33 +/- 0.48) was clearly the best-fitting model based on a pseudo-Bayes factor criterion. Breed group heterozygosity and, to a lesser extent, calf sex seemed to be marginally important sources of residual heteroskedasticity. Specifically, the residual variance in F1 animals was estimated to be 0.70 +/- 0.16 times that for purebreds, whereas the male residual variance was estimated to be 1.13 +/- 0.09 times that for females. The CG effects were important random sources of residual heteroskedasticity (i.e., the coefficient of variation of CG-specific residual variances was estimated to be 0.72 +/- 0.06). Purebred Nelores were estimated to have a larger genetic variance (124.84 +/- 21.75 kg2) compared with Herefords (40.89 +/- 6.70 kg2) under the heteroskedastic Student's t error model; however, the converse was observed from results based on a homoskedastic Student's t error model (46.24 +/- 10.90 kg2 and 60.11 +/- 8.54 kg2, respectively). These results indicate that allowing for robustness to outliers and accounting for heteroskedasticity of residual variances has potentially important implications for variance component and genetic parameter estimates from data on multiple-breed populations.

A hierarchical Bayes (HIER) model for the quantitative genetic analysis of performance data was compared to a model based on the use of Henderson's average numerator relationship matrix (ANRM) when some pedigrees are characterized by uncertain paternity. A simulation study consisted of ten datasets characterized by 30% of animals having uncertain paternity for each of two traits: one having moderate heritabilities for direct and maternal genetic effects on weaning weight (WWT) and another having a high heritability for direct genetic effects on postweaning gain (PWG). Posterior inference on the variance components was very similar between the two models. In an application to WWT and PWG data from Brazilian Herefords, posterior inference on variance components was also very similar between ANRM and HIER. Furthermore, rank correlations on posterior means for genetic effects between the two models exceeded 0.90. Nevertheless, large differences in posterior means between these two models were observed for some animals. Furthermore, animals with uncertain paternity had generally larger posterior standard deviations of genetic effects using the HIER model compared to the ANRM model, likely because the HIER model infers upon sire assignment probabilities. Bayesian model choice criteria consistently favored the HIER model over the ANRM model in both simulation and Hereford data analysis studies.

The primary objective of this study was to demonstrate the utility of a hierarchical Bayes implementation of a multiple-breed animal model (MBAM) to estimate breed composition means and additive genetic variances as well as on variances due to the segregation between breeds. The MBAM and a conventional animal model (AM) were both applied to five simulated data sets derived from each of two different populations. Population I consisted of crosses between two breeds having a twofold difference in genetic variance and a nonzero segregation variance. Population II had the same population structure as Population I, except that the two breeds had the same genetic variance with no segregation variance; that is, Population II was essentially single breed in its genetic architecture. For Population I, posterior means of all variance components obtained by MBAM were unbiased, with 95% posterior probability intervals (PPI) having the expected coverage based on five replicates. The MBAM showed a slightly superior performance over the AM for genetic predictions in Population I, although there was no evidence that the use of the MBAM translated into greater genetic gains relative to the use of the AM. Nevertheless, the MBAM was clearly demonstrated to have superior fit to the data using pseudo-Bayes factors (PBF) as the basis for model choice. As expected, the MBAM and AM performed equally well in Population II. A data set consisting of 22,717 postweaning gain (PWG) records of a Nelore-Hereford population (40,082 animals in the pedigree) also was analyzed. The MBAM inference on Nelore and Hereford additive heritabilities (h 2 A) substantially differed. Herefords had a posterior mean h 2 A of 0.20 with a 95% PPI of 0.15 to 0.25, whereas the corresponding values for the Nelores were 0.07 and 0.04 to 0.11, respectively. The posterior mean genetic variance due to the segregation between these breeds was 8.4 kg2, with a 95% PPI of 2.3 to 24.8 kg2, and represented 35.4% of the Nelore but only 9.9% of the Hereford posterior mean genetic variance. The posterior mean h 2 A using the AM was 0.15, presumed common across the two breeds, with a 95% PPI of 0.11 to 0.19. The PBF heavily favored the MBAM over the AM for the PWG data. Accordingly, the MBAM represents a viable alternative to AM for multiple-breed genetic evaluations, providing the necessary flexibility in modeling heteroskedastic genetic variances of breed composition groups.