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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.

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

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.

Renibacterium salmoninarum (RS), the causative agent of bacterial kidney disease, has been a serious threat to salmonid health in the Laurentian Great Lakes. Despite its wide spread presence in the Great Lakes basin, little is known about RS ecology and the potential role of non-salmonid species as one of the pathogen’s reservoirs. This information is of paramount importance to fishery managers in order to better understand RS distribution in the different biotic components of the Great Lakes watershed. In this study, non-salmonid species from lakes Michigan and Huron, and from 13 inland waters of the Great Lakes watershed were collected from 1999 to 2008. Out of 380 fish sampled from lakes Michigan and Huron, 42 (11.05%) tested positive for RS as determined by the nested polymerase chain reaction. Prevalence was lower in Lake Huron (5.71%) compared to Michigan (20.74%), but the difference was not statistically significant. Prevalence of RS was not found to be significantly different between species or sites; however, when species were grouped into demersal vs. pelagic categories, significant differences (P

The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture covers a select set of topics currently of primary importance in the field. Efficient use of resources in agricultural research, as well as valid statistical inference, requires good designs, and this special issue boasts seven papers providing both review and cutting-edge methodology for the purpose. A broad range of methods for analysis of data arising in different branches agricultural research is covered in another five exciting papers. This special issue highlights the importance of and opportunities for applied statistics in agriculture.

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.

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.

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.

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

Death-associated protein (DAP) undergoes substantial changes in expression during turkey skeletal muscle development, decreasing from the 18 day embryonic stage to 1 day posthatch, and again from 1 day posthatch to 16 weeks of age. These changes suggest that DAP plays an important role at critical stages of the developmental process. The objective of this study was to elucidate the role of DAP in muscle development by examining the effect of reduced DAP expression on global gene expression in proliferating and differentiating turkey pectoralis major muscle satellite cells. Small interfering RNA was used to knock down expression of DAP and the transcriptome was subsequently profiled using a turkey skeletal muscle long oligonucleotide microarray. Microarray data were corroborated using quantitative real-time PCR. In proliferating cells, 458 loci, resulting in 378 uniquely annotated genes, showed differential expression (false discovery rate, FDR < 0.05). Pathway analysis highlighted altered eukaryotic translational initiation factors (eIFs) signaling, protein ubiquitination, sirtuin signaling, and mechanistic target of rapamycin (mTOR) signaling as the primary pathways affected in the knockdown proliferating cells. The findings underpinned the potential DAP involvement in cell proliferation of turkey satellite cells through the coordination between protein synthesis and cell cycle. In differentiating cells, 270 loci, accounting for 189 unique genes, showed differential expression (FDR < 0.05). Decreased expression of genes encoding various myofibrillar proteins and proteins involved in sarcoplasmic reticulum calcium flux suggests that DAP may affect regulation of calcium homeostasis and cytoskeleton signaling. This study provides the first evidence that reduced expression of DAP significantly alters the transcriptome profile of pectoralis major muscle satellite cells, thereby reducing proliferation and differentiation.

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.

A currently popular strategy (EMMAX) for genome-wide association (GWA) analysis infers association for the specific marker of interest by treating its effect as fixed while treating all other marker effects as classical Gaussian random effects. It may be more statistically coherent to specify all markers as sharing the same prior distribution, whether that distribution is Gaussian, heavy-tailed (BayesA), or has variable selection specifications based on a mixture of, say, two Gaussian distributions [stochastic search and variable selection (SSVS)]. Furthermore, all such GWA inference should be formally based on posterior probabilities or test statistics as we present here, rather than merely being based on point estimates. We compared these three broad categories of priors within a simulation study to investigate the effects of different degrees of skewness for quantitative trait loci (QTL) effects and numbers of QTL using 43,266 SNP marker genotypes from 922 Duroc–Pietrain F2-cross pigs. Genomic regions were based either on single SNP associations, on nonoverlapping windows of various fixed sizes (0.5–3 Mb), or on adaptively determined windows that cluster the genome into blocks based on linkage disequilibrium. We found that SSVS and BayesA lead to the best receiver operating curve properties in almost all cases. We also evaluated approximate maximum a posteriori (MAP) approaches to BayesA and SSVS as potential computationally feasible alternatives; however, MAP inferences were not promising, particularly due to their sensitivity to starting values. We determined that it is advantageous to use variable selection specifications based on adaptively constructed genomic window lengths for GWA studies.

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.

Bacterial kidney disease, caused by Renibacterium salmoninarum (RS), is a chronic and often fatal disease of salmonid species, and can be particularly harmful to hatchery-reared Chinook Salmon Oncorhynchus tshawytscha. A considerable amount of research has focused on the prevention of vertical and horizontal transmission; however, a comparatively little amount has investigated factors that increase the prevalence of RS infection in captive environments. We evaluated the effects of three common hatchery conditions (handling, nutrition level, and rearing density) on RS infection prevalence. Fish were sampled at 30-d and 60-d postexposure to RS. Of 577 juveniles examined, 65 (11.27%) had anterior kidneys infected with RS. Using a logistic mixed model analysis, we found effects of nutrition level (P = 0.018), handling (P = 0.010), and sampling period (P = 0.003) on the prevalence of RS. The interactions of nutrition and handling (P = 0.008) and nutrition and time (P

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.

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit.

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.

Background Copy number variation (CNV) is an important type of genetic variation contributing to phenotypic differences among mammals and may serve as an alternative molecular marker to single nucleotide polymorphism (SNP) for genome-wide association study (GWAS). Recently, GWAS analysis using CNV has been applied in livestock, although few studies have focused on Holstein cattle. Results We describe 191 CNV detected using intensity data from over 700,000 SNP genotypes generated with the BovineHD Genotyping BeadChip (Illumina, San Diego, CA) in 528 Holstein cows. The CNV were used for GWAS analysis of 10 important production traits of 473 cattle related to feed intake, milk quality, and female fertility, as well as 2 composite traits of net merit and productive life. In total, we detected 57 CNV associated (P

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) modeling and genome-wide association (GWA) analyses are big data issues in agricultural quantitative genetics. Both areas require meaningful input from the statistical scholarly community in order to further improve the accuracy of prediction of genetic merit and inference on putative causal variants as well as improving the computational efficiency of existing methods and algorithms. These concerns have become increasingly critical as new sequencing technologies will only exacerbate current model dimensionality problems. We focus primarily on mixed model and hierarchical Bayesian analyses which have been most commonly pursued by animal and plant breeders for WGP thus far. We draw attention to our observation that many such previous analyses have not carefully inferred upon hyperparameters defined at the top levels of the Bayesian model hierarchy, but simply arbitrarily specify their values. We also reassess previous discussions on WGP model dimensionality, believing that useful data augmentation schemes utilized in various Markov Chain Monte Carlo (MCMC) schemes have led to a general misunderstanding that heavy-tailed or variable selection-based WGP models may be highly parameterized relative to more standard mixed model representations. Computational efficiency is addressed with respect to MCMC and competitive, albeit approximate, alternatives. Furthermore, GWA analyses are reassessed, encouraging a greater reliance on shrinkage-based inferences based on critically chosen priors, instead of potentially nonreproducible fixed effects P value-based inference.

Computational efficiency is an increasing concern for whole genome prediction (WGP) based on denser genetic marker panels such that algorithms other than Markov Chain Monte Carlo (MCMC) warrant greater consideration, particularly for hierarchical models that flexibly confer either heavy-tailed (e.g., BayesA) or stochastic search and variable selection (SSVS) instead of Gaussian specifications on marker effect distributions. The expectation maximization (EM) algorithm is one attractive alternative; however, recently proposed hierarchical model implementations of EM have not addressed formal estimation of underlying hyperparameters even though their specifications are known to impact WGP accuracy. Furthermore, EM can be sensitive to starting values. We develop and explore the properties of an empirical Bayes strategy by conditioning EM implementations of BayesA or SSVS WGP models on marginal modal estimation of variance components and other key hyperparameters. These empirical Bayes implementations are compared against their MCMC counterparts for estimation of hyperparameters and WGP accuracy, both within the context of a simulation study and application to a loblolly pine dataset. In all cases, starting values were deemed to be important for EM-based estimates. Starting values based on MCMC posterior means were preferable, whereas those based on setting all marker effects equal to zero generally led to inferior performance. Nevertheless, a recently proposed regularization procedure was useful in alleviating the impact of starting values in the EM implementation of the SSVS model, as was modifying the expectation step in the BayesA model to be based on relative variances rather than on relative precisions.

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.

A popular strategy (EMMAX) for genome wide association (GWA) analysis fits all marker effects as classical random effects (i.e., Gaussian prior) by which association for the specific marker of interest is inferred by treating its effect as fixed. It seems more statistically coherent to specify all markers as sharing the same prior distribution, whether it is Gaussian, heavy-tailed (BayesA), or has variable selection specifications based on a mixture of, say, two Gaussian distributions (SSVS). Furthermore, all such GWA inference should be formally based on posterior probabilities or test statistics as we present here, rather than merely being based on point estimates. We compared these three broad categories of priors within a simulation study to investigate the effects of different degrees of skewness for quantitative trait loci (QTL) effects and numbers of QTL using 43,266 SNP marker genotypes from 922 Duroc-Pietrain F2 cross pigs. Genomic regions were based either on single SNP associations, on non-overlapping windows of various fixed sizes (0.5 to 3 Mb) or on adaptively determined windows that cluster the genome into blocks based on linkage disequilibrium (LD). We found that SSVS and BayesA lead to the best receiver operating curve properties in almost all cases. We also evaluated approximate marginal a posteriori (MAP) approaches to BayesA and SSVS as potential computationally feasible alternatives; however, MAP inferences were not promising, particularly due to their sensitivity to starting values. We determined that it is advantageous to use variable selection specifications based on adaptively constructed genomic window lengths for GWA studies.SUMMARYGenome wide association (GWA) analyses strategies have been improved by simultaneously fitting all marker effects when inferring upon any single marker effect, with the most popular distributional assumption being normality. Using data generated from 43,266 genotypes on 922 Duroc-Pietrain F2 cross pigs, we demonstrate that GWA studies could particularly benefit from more flexible heavy-tailed or variable selection distributional assumptions. Furthermore, these associations should not just be based on single markers or even genomic windows of markers of fixed physical distances (0.5 − 3.0 Mb) but based on adaptively determined genomic windows using linkage disequilibrium information.

Aflatoxin, a mycotoxin found commonly in maize and peanuts worldwide, is associated with liver cancer, acute toxicosis, and growth impairment in humans and animals. In Tanzania, sunflower seeds are a source of snacks, cooking oil, and animal feed. These seeds are a potential source of aflatoxin contamination. However, reports on aflatoxin contamination in sunflower seeds and cakes are scarce. The objective of the current study was to determine total aflatoxin concentrations in sunflower seeds and cakes from small-scale oil processors across Tanzania. Samples of sunflower seeds (n = 90) and cakes (n = 92) were collected across two years, and analyzed for total aflatoxin concentrations using a direct competitive enzyme-linked immunosorbent assay (ELISA). For seed samples collected June-August 2014, the highest aflatoxin concentrations were from Dodoma (1.7-280.6 ng/g), Singida (1.4-261.8 ng/g), and Babati-Manyara (1.8-162.0 ng/g). The highest concentrations for cakes were from Mbeya (2.8-97.7 ng/g), Dodoma (1.9-88.2 ng/g), and Singida (2.0-34.3 ng/g). For seed samples collected August-October 2015, the highest concentrations were from Morogoro (2.8-662.7 ng/g), Singida (1.6-217.6 ng/g) and Mbeya (1.4-174.2 ng/g). The highest concentrations for cakes were from Morogoro (2.7-536.0 ng/g), Dodoma (1.4-598.4 ng/g) and Singida (3.2-52.8 ng/g). In summary, humans and animals are potentially at high risk of exposure to aflatoxins through sunflower seeds and cakes from micro-scale millers in Tanzania; and location influences risk.

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.

Hierarchical mixed effects models have been demonstrated to be powerful for predicting genomic merit of livestock and plants, on the basis of high-density single-nucleotide polymorphism (SNP) marker panels, and their use is being increasingly advocated for genomic predictions in human health. Two particularly popular approaches, labeled BayesA and BayesB, are based on specifying all SNP-associated effects to be independent of each other. BayesB extends BayesA by allowing a large proportion of SNP markers to be associated with null effects. We further extend these two models to specify SNP effects as being spatially correlated due to the chromosomally proximal effects of causal variants. These two models, that we respectively dub as ante-BayesA and ante-BayesB, are based on a first-order nonstationary antedependence specification between SNP effects. In a simulation study involving 20 replicate data sets, each analyzed at six different SNP marker densities with average LD levels ranging from r2 = 0.15 to 0.31, the antedependence methods had significantly (P < 0.01) higher accuracies than their corresponding classical counterparts at higher LD levels (r2 > 0. 24) with differences exceeding 3%. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS/) using 6-week body weights as the phenotype. The antedependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their classical counterparts (P < 0.001). Finally, we applied our method to other benchmark data sets and demonstrated that the antedependence methods were more accurate than their classical counterparts for genomic predictions, even for individuals several generations beyond the training data.