Effect of diet energy level and genomic residual feed intake on bred Holstein dairy heifer growth and feed efficiency

The objective of this study was to determine growth, feed intake, and feed efficiency of postbred dairy heifers with different genomic residual feed intake (RFI) predicted as a lactating cow when offered diets differing in energy density. Postbred Holstein heifers (n = 128, ages 14-20 mo) were blocked by initial weight (high, medium-high, medium-low, and low) with 32 heifers per block. Each weight block was sorted by RFI (high or low) to obtain 2 pens of heifers with high and low genomically predicted RFI within each block (8 heifers per pen). Low RFI heifers were expected to have greater feed efficiency than high RFI heifers. Dietary treatments consisted of a higher energy control diet based on corn silage and alfalfa haylage [HE; 62.7% total digestible nutrients, 11.8% crude protein, and 45.6% neutral detergent fiber; dry matter (DM) basis], and a lower energy diet diluted with straw (LE; 57.0% total digestible nutrients, 11.7% crude protein, and 50.1% neutral detergent fiber; DM basis). Each pen within a block was randomly allocated a diet treatment to obtain a 2 × 2 factorial arrangement (2 RFI levels and 2 dietary energy levels). Diets were offered in a 120-d trial. Dry matter intake by heifers was affected by diet (11.0 vs. 10.0 kg/d for HE and LE, respectively) but not by RFI or the interaction of RFI and diet. Daily gain was affected by the interaction of RFI and diet, with low RFI heifers gaining more than high RFI heifers when fed LE (0.94 vs. 0.85 kg/d for low and high RFI, respectively), but no difference for RFI groups when fed HE (1.16 vs. 1.19 kg/d for low and high RFI, respectively). Respective feed efficiencies were improved for low RFI compared with high RFI heifers when fed LE (10.6 vs. 11.8 kg of feed DM/kg of gain), but no effect of RFI was found when fed HE (9.4 vs. 9.5 kg of DM/kg of gain for high and low RFI, respectively). No effect of RFI or diet on first-lactation performance through 150 DIM was observed. Based on these results, the feed efficiency of heifers having different genomic RFI may be dependent on diet energy level, whereby low RFI heifers utilized the LE diet more efficiently. The higher fiber straw (LE) diet controlled intake and maintained more desirable heifer weight gains. This suggests that selection for improved RFI in lactating cows may improve feed efficiency in growing heifers when fed to meet growth goals of 0.9 to 1.0 kg of gain/d.

Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables

Predicting dry matter intake (DMI) and feed efficiency by leveraging the use of data streams available on farm could aid efforts to improve the feed efficiency of dairy cattle. Residual feed intake (RFI) is the difference between predicted and observed feed intake after accounting for body size, body weight change, and milk production, making it a valuable metric for feed efficiency research. Our objective was to develop and evaluate DMI and RFI prediction models using multiple linear regression (MLR), partial least squares regression, artificial neural networks, and stacked ensembles using different combinations of cow descriptive, performance, sensor-derived behavioral (SMARTBOW; Zoetis), and blood metabolite data. Data were collected from mid-lactation Holstein cows (n = 124; 102 multiparous, 22 primiparous) split equally between 2 replicates of 45-d duration with ad libitum access to feed. Within each predictive approach, 4 data streams were added in sequence: dataset M (week of lactation, parity, milk yield, and milk components), dataset MB (dataset M plus body condition score and metabolic body weight), dataset MBS (dataset MB plus sensor-derived behavioral variables), and dataset MBSP (dataset MBS plus physiological blood metabolites). The combination of 4 datasets and 4 analytical approaches resulted in 16 analyses of DMI and RFI, using variables averaged within cow across the study period. Additional models using weekly averaged data within cow and study were built using all predictive approaches for datasets M, MB, and MBS. Model performance was assessed using the coefficient of determination, concordance correlation coefficient, and root mean square error of prediction. Predictive models of DMI performed similarly across all approaches, and models using dataset MBS had the greatest model performance. The best approach-dataset combination was MLR-dataset MBS, although several models performed similarly. Weekly DMI models had the greatest performance with MLR and partial least squares regression approaches. Dataset MBS models had incrementally better performance than datasets MB and M. Within each approach-dataset combination, models with DMI averaged over the study period had slightly greater model performance than DMI averaged weekly. Predictive performance of all RFI models was poor, but slight improvements when using MLR applied to dataset MBS suggest that rumination and activity behaviors may explain some of the variation in RFI. Overall, similar performance of MLR, compared with machine learning techniques, indicates MLR may be sufficient to predict DMI. The improvement in model performance with each additional data stream supports the idea of integrating data streams to improve model predictions and farm management decisions.

Enteric methane, lactation performances, digestibility, and metabolism of nitrogen and energy of Holsteins and Jerseys fed 2 levels of forage fiber from alfalfa silage or corn silage

Our objective was to determine the effects of replacing alfalfa silage (AS) neutral detergent fiber (NDF) with corn silage (CS) NDF at 2 levels of forage NDF (FNDF) on enteric methane (CH₄), lactation performance, ruminal fluid characteristics, digestibility, and metabolism of N and energy in Holstein and Jersey cows. Twelve Holstein and 12 Jersey cows (all primiparous and mid-lactation) were used in a triplicated split-plot 4 × 4 Latin square experiment, where breed and diet formed the main and subplots, respectively. The 4 iso-nitrogenous and iso-starch dietary treatments were arranged as a 2 × 2 factorial with 2 levels of FNDF [19 (low FNDF, LF) and 24% (high FNDF, HF) of dry matter] and 2 sources of FNDF (70:30 and 30:70 ratio of AS NDF to CS NDF). Soyhull (non-forage NDF) and corn grain were respectively used to keep dietary NDF and starch content similar across diets. Total collection of feces and urine over 3 d was performed on 8 cows (1 Latin square from each breed). The difference in dry matter intake (DMI) between Holsteins and Jerseys was greater when fed AS than CS. Compared with Jerseys, Holstein cows had greater body weight (48%), DMI (34%), fat- and protein-corrected milk (FPCM; 31%) and CH₄ production (22%; 471 vs. 385 g/d). However, breed did not affect CH₄ intensity (g/kg of FPCM) or yield (g/kg of DMI), nutrient digestibility, and N partitioning. Compared with HF, LF-fed cows had greater DMI (10%), N intake (8%), and FPCM (5%), but they were 5% less efficient (both FPCM/DMI and milk N/intake N). Compared with HF, LF-fed cows excreted 11 and 17% less urinary N (g/d and % of N intake, respectively). In spite of lower (2.5%) acetate and higher (10%) propionate (mol/100 mol ruminal volatile fatty acids) LF-fed cows had greater (6%) CH₄ production (g/d) than did HF-fed cows, most likely due to increased DMI, as affected mainly by the soyhulls. Compared with AS, CS-fed cows had greater DMI (7%) and FPCM (4%), but they were less efficient (5%), and CH₄ yield (g/kg of DMI) was reduced by 8%. In addition, per unit of gross energy intake, CS-fed cows lost less urinary energy (15%) and CH energy (11%) than did AS-fed cows. We concluded that, in contrast to level and source of FNDF, breed did not affect digestive and metabolic efficiencies, and, furthermore, neither breed nor dietary treatments affected CH₄ intensity. The tradeoff between CH₄ and N losses may have implications in future studies assessing the environmental effects of milk production when approached from a whole-farm perspective.

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

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.

Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within North American dairy herds

Genomic data are widely available in the dairy industry and provide a cost-effective means of predicting genetic merit to inform selection decisions and increase genetic gains. As more dairy farms adopt genomic selection practices, dairy producers will soon have genomic data available on all of the animals within their herds. This is a very rich, but currently underused, source of information. Herdmates provide an excellent indication of how a selection candidate's genetics will perform within a given herd, noting that herdmates often include close relatives that share a similar environment. The study objective was to evaluate the utility of incorporating herdmate data into genomic predictions in a data set composed of 3,303 Holsteins from one herd in Canada and 6 herds throughout the United States. Within-herd prediction accuracy was assessed for milk-production and feed-efficiency traits determined from genomic best linear unbiased prediction under 4 different scenarios. Scenario 1 did not include herdmates in the training population. Scenarios 2 through 4 included herdmates in the training population, and scenarios 3 and 4 also included modeling of herd-specific marker effects. Leave-one-out cross validation was used to maximize the number of herdmates in the training population in scenarios 2 through 4, while maintaining constant training population size with scenario 1. Results from the present study reveal the importance of incorporating herdmate data into genomic evaluations. Inclusion of herdmates in the training population improved mean within-herd prediction accuracy for milk-production traits (± standard error) by 0.08 ± 0.03 (milk yield), 0.07 ± 0.03 (fat percentage), and 0.05 ± 0.01 (protein percentage) and feed-efficiency traits by 0.07 ± 0.02 (milk energy), 0.03 ± 0.02 (DMI), and 0.08 ± 0.01 (metabolic body weight). Modeling herd-specific marker effects further improved mean within-herd prediction accuracy for milk yield and energy by 0.03 ± 0.01 and 0.02 ± 0.01, respectively. Herds with higher within-herd heritability and low genomic correlation with the remaining herds benefitted most from the inclusion of herdmate data.

Effect of diet energy density and genomic residual feed intake on prebred dairy heifer feed efficiency, growth, and manure excretion

The objective of this study was to determine the growth, feed efficiency, and manure excretion of prebred dairy heifers with differing predicted genomic residual feed intakes (RFI) when offered diets differing in energy density. Prebred Holstein heifers (n = 128, ages 4 to 8 mo) were blocked by weight (low, medium-low, medium-high, or high) with 32 heifers per block. Heifers in each weight block were grouped by RFI and randomly assigned to obtain 2 pens of high (HRFI) and 2 pens of low RFI (LRFI) heifers within each block (8 heifers/pen). Heifers with LRFI were hypothesized to have greater feed efficiency than HRFI heifers. Dietary treatments were a high-energy diet (HE; 66.6% total digestible nutrients, 14.0% crude protein, and 36.3% neutral detergent fiber, dry matter basis) and a low-energy diet (LE; 63.8% total digestible nutrients, 13.5% crude protein, and 41.2% neutral detergent fiber, dry matter basis). Each pen of heifers was randomly assigned to a treatment to obtain a 2 × 2 factorial arrangement (2 RFI levels × 2 diet energy densities). Diets were offered in a 120-d trial. Dry matter intake was not affected by diet, RFI, or their interaction. Average daily gain (ADG) was affected by diet, with heifers fed HE having greater ADG than heifers fed LE. In addition, RFI affected ADG, with LRFI heifers having greater ADG than HRFI heifers, whereas the interaction of RFI and diet was not significant. Feed efficiency was improved for heifers fed the HE diet, but it was not affected by RFI or the interaction of RFI and diet. Overall, feed efficiency of prebred heifers was not dependent on predicted genomic RFI, because the greater ADG of LRFI heifers was accompanied by slightly higher dry matter intake. Feed efficiency of heifers was reduced when heifers were fed the LE diet, but this resulted in more optimal ADG compared with the HE diet fed for ad libitum intake.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency

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.

Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle

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 × 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.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors

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.

Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population

Bovine leukosis virus is an oncogenic virus that infects B cells, causing bovine leukosis disease. This disease is known to have a negative impact on dairy cattle production and, because no treatment or vaccine is available, finding a possible genetic solution is important. Our objective was to perform a comprehensive genetic analysis of leukosis incidence in dairy cattle. Data on leukosis occurrence, pedigree and molecular information were combined into multitrait GBLUP models with milk yield (MY) and somatic cell score (SCS) to estimate genetic parameters and to perform whole-genome scans and pathway analysis. Leukosis data were available for 11 554 Holsteins daughters of 3002 sires from 112 herds in 16 US states. Genotypes from a 60K SNP panel were available for 961 of those bulls as well as for 2039 additional bulls. Heritability for leukosis incidence was estimated at about 8%, and the genetic correlations of leukosis disease incidence with MY and SCS were moderate at 0.18 and 0.20 respectively. The genome-wide scan indicated that leukosis is a complex trait, possibly modulated by many genes. The gene set analysis identified many functional terms that showed significant enrichment of genes associated with leukosis. Many of these terms, such as G-Protein Coupled Receptor Signaling Pathway, Regulation of Nucleotide Metabolic Process and different calcium-related processes, are known to be related to retrovirus infection. Overall, our findings contribute to a better understanding of the genetic architecture of this complex disease. The functional categories associated with leukosis may be useful in future studies on fine mapping of genes and development of dairy cattle breeding strategies.

Short communication: Genetic correlation of bovine leukosis incidence with somatic cell score and milk yield in a US Holstein population

Bovine leukosis (BL) is a retroviral disease caused by the bovine leukosis virus (BLV), which affects only cattle. Dairy cows positive for BL produce less milk and have more days open than cows negative for BL. In addition, the virus also affects the immune system and causes weaker response to vaccines. Heritability estimates of BL incidence have been reported for Jersey and Holstein populations at about 0.08, indicating an important genetic component that can potentially be exploited to reduce the prevalence of the disease. However, before BL is used in selection programs, it is important to study its genetic associations with other economically important traits such that correlated responses to selection can be predicted. Hence, this study aimed to estimate the genetic correlations of BL with milk yield (MY) and with somatic cell score (SCS). Data of a commercial assay (ELISA) used to detect BLV antibodies in milk samples were obtained from Antel BioSystems (Lansing, MI). The data included continuous milk ELISA scores and binary milk ELISA results for 11,554 cows from 112 dairy herds across 16 US states. Continuous and binary milk ELISA were analyzed with linear and threshold models, respectively, together with MY and SCS using multitrait animal models. Genetic correlations (posterior means ± standard deviations) between BL incidence and MY were 0.17 ± 0.077 and 0.14 ± 0.076 using ELISA scores and results, respectively; with SCS, such estimates were 0.20 ± 0.081 and 0.17 ± 0.079, respectively. In summary, the results indicate that selection for higher MY may lead to increased BLV prevalence in dairy herds, but that the inclusion of BL (or SCS as an indicator trait) in selection indexes may help attenuate this problem.

Using a family-based structure to detect the effects of genomic inbreeding on embryo viability in Holstein cattle

Recent evidence has suggested that some of the decline in reproductive ability in dairy cattle has been caused by embryonic death. The current study compared expected genomic inbreeding from sire-dam mating pairs to genomic inbreeding from live progeny in an attempt to determine how embryonic inbreeding may affect fertility. A total of 11,484 Holstein cattle with 43,485 SNP markers and pedigree information were available for analysis. A total of 412 sire-dam-progeny trios in which all animals had reliable genotypes were discovered. After removal of trios because of parentage errors, 374 remained for analysis. Additionally, a total of 3,031 animals comprising 3,906 genotyped full-sibling pairs were available for comparison. Expected genomic inbreeding measures were calculated by predicting homozygosity independently per SNP (FPHE) in sire-dam mating pairs and by simulating progeny using phased haplotype information (FROHE and FPHE). Actual genomic inbreeding measures were calculated using the percent homozygosity of all SNP (FPH) and using runs of homozygosity (FROH). Average FPHE values (62.8±0.78%) were slightly lower than FPH (63.1±1.12%), when considering each SNP independently. After phasing haplotypes, FPHE (62.5±0.83%) was again slightly lower than FPH (62.7±1.16%), and FROHE (3.46±1.54%) was slightly lower than FROH (3.53±2.17%). Results suggest increases in expected genomic inbreeding do not explain a large effect on embryo viability at average levels of expected inbreeding. Higher variation in FROH values was present with sire-dam mating pairs exhibiting high FROHE, which may suggest high levels of genomic inbreeding are required for a noticeable effect on overall embryo viability. Genomic inbreeding between full siblings was also compared with moderate correlations (0.47-0.52) present. Overall, expected genomic inbreeding measures were calculated, but results did not suggest a large effect of expected inbreeding on embryo viability.

Short communication: Use of single nucleotide polymorphism genotypes and health history to predict future phenotypes for milk production, dry matter intake, body weight, and residual feed intake in dairy cattle

As feed prices have increased, the efficiency of feed utilization in dairy cattle has attracted increasing attention. In this study, we used residual feed intake (RFI) as a measurement of feed efficiency along with its component traits, adjusted milk energy (aMilkE), adjusted dry matter intake (aDMI), and adjusted metabolic body weight (aMBW), where the adjustment was for environmental factors. These traits may also be affected by prior health problems. Therefore, the carryover effects of 3 health traits from the rearing period and 10 health traits from the lactating period (in the same lactation before phenotype measurements) on RFI, aMilkE, aDMI, and aMBW were evaluated. Cows with heavier birth weight and greater body weight at calving of this lactation had significant increases in aMilkE, aDMI, and aMBW. The only trait associated with RFI was the incidence of diarrhea early in the lactation. Mastitis and reproductive problems had negative carryover effects on aMilkE. The aMBW of cows with metabolic disorders early in the lactation was lower than that of unaffected cows. The incidence of respiratory disease during lactating period was associated with greater aMBW and higher aDMI. To examine the contribution of health traits to the accuracy of predicted phenotype, genomic predictions were computed with or without information regarding 13 health trait phenotypes using random forests (RF) and support vector machine algorithms. Adding health trait phenotypes increased prediction accuracies slightly, except for prediction of RFI using RF. In general, the accuracies were greater for support vector machine than RF, especially for RFI. The methods described herein can be used to predict future phenotypes for dairy replacement heifers, thereby facilitating culling decisions that can lead to decreased feed costs during the rearing period. For these decisions, prediction of the animal's own phenotype is of greater importance than prediction of the genetic superiority or inferiority that will transmit to its offspring.

Short communication: genetic evaluation of stillbirth in US Brown Swiss and Jersey cattle

Stillbirth (SB) often results in reduced milk yield, compromised reproductive performance, and decreased dam longevity. Corrective mating can be used as a short-term solution to the problem, but long-term improvement of the population requires the routine calculation of genetic evaluations. Breeding values for SB have been available for Holstein (HO) bulls since 2006, but not for Brown Swiss (BS) or Jersey (JE) bulls. In this study, a multi-breed sire-maternal grandsire threshold model was used to perform genetic evaluations for SB of BS, JE, and HO bulls using more than 14 million purebred and crossbred calving records. Phenotypically, the percentage of SB (%SB) across all lactations were 3.7% in JE, 5.1% in BS, and 6.3% in HO. Direct heritabilities for BS, JE, and HO were 0.008, 0.007, and 0.008, and maternal heritabilities were 0.002, 0.016, and 0.021, respectively. Compared with HO, crossbred calvings from BS and JE bulls bred to HO cows lowered %SB by 1.5 and 1.2%, respectively. In general, %SB increased considerably as calving difficulty increased in all 3 breeds; however, in JE, %SB was constant for dystocia scores of 3 (needed assistance), 4 (considerable force), and 5 (extreme difficulty). Compared with purebred HO calvings, purebred BS and JE calvings had lower phenotypic %SB by up to 5.5 and 7.8%, respectively, and BS × HO and JE × HO crossbred calvings decreased %SB by up to 3.8 and 4.1%, respectively. As expected, SB rates in primiparous cows were higher than those in multiparous cows. Female calves had greater %SB than male calves in all parities for JE and in second-and-later parities for BS. Favorable (decreasing) phenotypic and genetic trends from 1999 to 2009 were observed in all 3 breeds. Heterosis of SB for BS and JE was -0.026 and -0.149, respectively, on the underlying scale, which corresponds to effects on service-sire SB (SSB) and daughter SB (DSB) predicted transmitting ability (PTA) of -0.3 and -0.5% in BS, and -1.5 and -2.7% in JE. Overall, in the current population, BS bulls had the most desirable average SSB PTA of 4.8%, compared with 5.6% for JE and 5.5% for HO. Brown Swiss and JE bulls both had average DSB PTA of 6.5%, lower than that of 7.7% in HO. Average reliabilities of SSB and DSB in 3 breeds ranged from 45 to 50%. The use of a BS-JE-HO multibreed genetic evaluation for SB in the United States is feasible, and the addition of SSB and DSB to the lifetime net merit selection index will help improve the profitability of BS and JE cattle in the United States.

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations

Combining data from research herds may be advantageous, especially for difficult or expensive-to-measure traits (such as dry matter intake). Cows in research herds are often genotyped using low-density single nucleotide polymorphism (SNP) panels. However, the precision of quantitative trait loci detection in genome-wide association studies and the accuracy of genomic selection may increase when the low-density genotypes are imputed to higher density. Genotype data were available from 10 research herds: 5 from Europe [Denmark, Germany, Ireland, the Netherlands, and the United Kingdom (UK)], 2 from Australasia (Australia and New Zealand), and 3 from North America (Canada and the United States). Heifers from the Australian and New Zealand research herds were already genotyped at high density (approximately 700,000 SNP). The remaining genotypes were imputed from around 50,000 SNP to 700,000 using 2 reference populations. Although it was not possible to use a combined reference population, which would probably result in the highest accuracies of imputation, differences arising from using 2 high-density reference populations on imputing 50,000-marker genotypes of 583 animals (from the UK) were quantified. The European genotypes (n=4,097) were imputed as 1 data set, using a reference population of 3,150 that included genotypes from 835 Australian and 1,053 New Zealand females, with the remainder being males. Imputation was undertaken using population-wide linkage disequilibrium with no family information exploited. The UK animals were also included in the North American data set (n=1,579) that was imputed to high density using a reference population of 2,018 bulls. After editing, 591,213 genotypes on 5,999 animals from 10 research herds remained. The correlation between imputed allele frequencies of the 2 imputed data sets was high (>0.98) and even stronger (>0.99) for the UK animals that were part of each imputation data set. For the UK genotypes, 2.2% were imputed differently in the 2 high-density reference data sets used. Only 0.025% of these were homozygous switches. The number of discordant SNP was lower for animals that had sires that were genotyped. Discordant imputed SNP genotypes were most common when a large difference existed in allele frequency between the 2 imputed genotype data sets. For SNP that had ≥ 20% discordant genotypes, the difference between imputed data sets of allele frequencies of the UK (imputed) genotypes was 0.07, whereas the difference in allele frequencies of the (reference) high-density genotypes was 0.30. In fact, regions existed across the genome where the frequency of discordant SNP was higher. For example, on chromosome 10 (centered on 520,948 bp), 52 SNP (out of a total of 103 SNP) had ≥ 20% discordant SNP. Four hundred and eight SNP had more than 20% discordant genotypes and were removed from the final set of imputed genotypes. We concluded that both discordance of imputed SNP genotypes and differences in allele frequencies, after imputation using different reference data sets, may be used to identify and remove poorly imputed SNP.

Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection

Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0,) and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection candidates). Lower correlations between genomic-based estimates of breeding values (GEBV) and true breeding values (TBV) were obtained when using the Bottom strategy. For Random, Extreme, and Less Related strategies, the correlation between GEBV and TBV became slightly larger as selection intensity decreased and was largest when no selection occurred. These 3 strategies were better than the Top approach. In addition, the Extreme, Random, and Less Related strategies had smaller predictive mean squared errors (PMSE) followed by the Top and Bottom methods. Overall, the Extreme genotyping strategy led to the best predictive ability of breeding values, indicating that animals with extreme yield deviations values in a reference population are the most informative when training genomic selection models.

Genomic architecture of bovine κ-casein and β-lactoglobulin

The objective of this study was to characterize the genetic architecture underlying the absolute concentrations of 2 important milk proteins, κ-casein (κ-CN) and β-lactoglobulin (β-LG), in a backcross population of (Holstein × Jersey) × Holstein cattle. A genome-wide association analysis was performed using a selective DNA pooling strategy and the Illumina BovineHD BeadChip assay [777,000 (777K) SNP markers; Illumina Inc., San Diego, CA]. After correction for multiple testing, 25 single nucleotide polymorphisms were found to be associated with κ-CN and 36 single nucleotide polymorphisms were associated with β-LG. A pathway association analysis revealed 15 Gene Ontology (GO) terms associated with the κ-CN trait and 28 GO terms associated with β-LG. In addition, several GO terms were associated with both milk proteins. Further analysis revealed that κ-CN and β-LG production is regulated by both kinase and phosphatase activity, including mechanisms regulating the extracellular matrix. These results are in concordance with the complex multihormonal process controlling the expression of milk proteins and interactions between mammary epithelial cells and extracellular matrix components. Although κ-CN and β-LG milk proteins are expressed by single genes, the results from this study showed that many loci are involved in the regulation of the concentration of these 2 proteins.

Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding

The effects of increased pedigree inbreeding in dairy cattle populations have been well documented and result in a negative impact on profitability. Recent advances in genotyping technology have allowed researchers to move beyond pedigree analysis and study inbreeding at a molecular level. In this study, 5,853 animals were genotyped for 54,001 single nucleotide polymorphisms (SNP); 2,913 cows had phenotypic records including a single lactation for milk yield (from either lactation 1, 2, 3, or 4), reproductive performance, and linear type conformation. After removing SNP with poor call rates, low minor allele frequencies, and departure from Hardy-Weinberg equilibrium, 33,025 SNP remained for analyses. Three measures of genomic inbreeding were evaluated: percent homozygosity (FPH), inbreeding calculated from runs of homozygosity (FROH), and inbreeding derived from a genomic relationship matrix (FGRM). Average FPH was 60.5±1.1%, average FROH was 3.8±2.1%, and average FGRM was 20.8±2.3%, where animals with larger values for each of the genomic inbreeding indices were considered more inbred. Decreases in total milk yield to 205d postpartum of 53, 20, and 47kg per 1% increase in FPH, FROH, and FGRM, respectively, were observed. Increases in days open per 1% increase in FPH (1.76 d), FROH (1.72 d), and FGRM (1.06 d) were also noted, as well as increases in maternal calving difficulty (0.09, 0.03, and 0.04 on a 5-point scale for FPH, FROH, and FGRM, respectively). Several linear type traits, such as strength (-0.40, -0.11, and -0.19), rear legs rear view (-0.35, -0.16, and -0.14), front teat placement (0.35, 0.25, 0.18), and teat length (-0.24, -0.14, and -0.13) were also affected by increases in FPH, FROH, and FGRM, respectively. Overall, increases in each measure of genomic inbreeding in this study were associated with negative effects on production and reproductive ability in dairy cows.

Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding

In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.

Genome-wide association study identifies candidate markers for bull fertility in Holstein dairy cattle

The decline in the reproductive efficiency of dairy cattle has become a challenging problem worldwide. Female fertility is now taken into account in breeding goals while generally less attention is given to male fertility. The objective of this study was to perform a genome-wide association study in Holstein bulls to identify genetic variants significantly related to sire conception rate (SCR), a new phenotypic evaluation of bull fertility. The analysis included 1755 sires with SCR data and 38,650 single nucleotide polymorphisms (SNPs) spanning the entire bovine genome. Associations between SNPs and SCR were analyzed using a mixed linear model that included a random polygenic effect and SNP genotype either as a linear covariate or as a categorical variable. A multiple testing correction approach was used to account for the correlation between SNPs because of linkage disequilibrium. After genome-wide correction, eight SNPs showed significant association with SCR. Some of these SNPs are located close to or in the middle of genes with functions related to male fertility, such as the sperm acrosome reaction, chromatin remodeling during the spermatogenesis, and the meiotic process during male germ cell maturation. Some SNPs showed marked dominance effects, which provide more evidence for the relevance of non-additive effects in traits closely related to fitness such as fertility. The results could contribute to the identification of genes and pathways associated with male fertility in dairy cattle.

An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network

Linkage disequilibrium (LD) is defined as a non-random association of the distributions of alleles at different loci within a population. This association between loci is valuable in prediction of quantitative traits in animals and plants and in genome-wide association studies. A question that arises is whether standard metrics such as D' and r(2) reflect complex associations in a genetic system properly. It seems reasonable to take the view that loci associate and interact together as a system or network, as opposed to in a simple pairwise manner. We used a Bayesian network (BN) as a representation of choice for an LD network. A BN is a graphical depiction of a probability distribution and can represent sets of conditional independencies. Moreover, it provides a visual display of the joint distribution of the set of random variables in question. The usefulness of BN for linkage disequilibrium was explored and illustrated using genetic marker loci found to have the strongest effects on milk protein in Holstein cattle based on three strategies for ranking marker effect estimates: posterior means, standardized posterior means and additive genetic variance. Two different algorithms, Tabu search (a local score-based algorithm) and incremental association Markov blanket (a constraint-based algorithm), coupled with the chi-square test, were used for learning the structure of the BN and were compared with the reference r(2) metric represented as an LD heat map. The BN captured several genetic markers associated as clusters, implying that markers are inter-related in a complicated manner. Further, the BN detected conditionally dependent markers. The results confirm that LD relationships are of a multivariate nature and that r(2) gives an incomplete description and understanding of LD. Use of an LD Bayesian network enables inferring associations between loci in a systems framework and provides a more accurate picture of LD than that resulting from the use of pairwise metrics.

Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms

The objective of this study was to quantify the gains in genetic potential of replacement females that could be achieved by using genomic testing to facilitate selection and culling decisions on commercial dairy farms. Data were simulated for 100 commercial dairy herds, each with 1,850 cows, heifers, and calves. Parameters of the simulation were based on the US Holstein population, and assumed reliabilities of traditional and genomic predictions matched reliabilities of animals that have been genotyped to date. Selection of the top 10, 20, 30, …, 90% of animals within each age group was based on parent averages and predicted transmitting abilities with or without genomic testing of all animals or subsets of animals that had been presorted by traditional predictions. Average gains in lifetime net merit breeding value of selected females due to genomic testing, minus prorated costs of genotyping the animals and their unselected contemporaries, ranged from $28 (top 90% selected) to $259 (top 20% selected) for heifer calves with no pedigrees, $14 (top 90% selected) to $121 (top 10% selected) for heifer calves with known sires, and $7 (top 90% selected) to $87 (top 20% selected) for heifer calves with full pedigrees. In most cases, gains in genetic merit of selected heifer calves far exceeded prorated genotyping costs, and gains were greater for animals with missing or incomplete pedigree information. Gains in genetic merit due to genomic testing were smaller for lactating cows that had phenotypic records, and in many cases, these gains barely exceeded or failed to exceed genotyping costs. Strategies based on selective genotyping of the top, middle, or bottom 50% of animals after presorting by traditional parent averages or predicted transmitting abilities were cost effective, particularly when pedigrees or phenotypes were available and a relatively small proportion of animals were to be selected or culled. Based on these results, it appears that routine genotyping of heifer calves or yearling heifers can be a cost-effective strategy for enhancing the genetic level of replacement females on commercial dairy farms. Increasing the accuracy of predicted breeding values for young females with genomic testing might lead to synergies with other management tools and strategies, such as propagating genetically superior females using advanced reproductive technologies or selling excess females that were generated by the use of sex-enhanced semen.

Association between milk protein gene variants and protein composition traits in dairy cattle

The objective of this study was to identify DNA markers in the 4 casein genes (CSN1S1, CSN1S2, CSN2, and CSN3) and the 2 major whey protein genes (LALBA and LGB) that show associations with milk protein profile measured by reverse-phase HPLC. Fifty-three single nucleotide polymorphisms (SNP) were genotyped for cows in a unique resource population consisting of purebred Holstein and (Holstein × Jersey) × Holstein crossbred animals. Seven traits were analyzed, including concentrations of α(S)-casein (CN), β-CN, κ-CN, α-lactalbumin, β-lactoglobulin, and 2 additional secondary traits, the total concentration of the above 5 milk proteins and the α(S)-CN to β-CN ratio. A substantial fraction of phenotypic variation could be explained by the additive genetic component for the 7 milk protein composition traits studied. Moreover, several SNP were significantly associated with all examined traits at an experiment-wise error rate of 0.05, except for α-lactalbumin. Importantly, the significant SNP explained a large proportion of the phenotypic variation of milk protein composition. Our findings could be used for selecting animals that produce milk with desired composition or desired processing and manufacturing properties.

Production, reproduction, health, and growth traits in backcross Holstein × Jersey cows and their Holstein contemporaries

A total of 648 purebred Holstein and 319 backcross Holstein × Jersey dairy cattle were compared for production, reproduction, health, linear type, and growth traits. Animals were born between 2003 and 2009 and were housed in the University of Wisconsin-Madison Integrated Dairy Facility. All animals had Holstein dams; lactating dams were mated to unproven Holstein sires to produce purebred (control) Holsteins or to unproven F(1) Jersey × Holstein crossbred sires to produce backcross animals, whereas nulliparous dams were mated to proven Holstein sires to produce purebred (other) Holsteins. Traits were analyzed using mixed linear models with effects of season of birth, age of dam, sire, birth year of sire, days in milk, lactation, and linear type score evaluator. Control Holsteins had greater 305-d milk yield (12,645 vs. 11,456 kg), 305-d mature equivalent milk yield (13,420 vs. 12,180 kg), peak daily milk yield (49.5 vs. 46.4 kg), total lactation milk yield (11,556 vs. 10,796 kg), and daily fat-corrected milk yield (43 vs. 40 kg) compared with backcrosses. Days open and services per conception as a heifer or cow did not differ between control Holsteins, other Holsteins, or backcrosses. The proportion of first-parity births that required assistance was less in control Holsteins than in backcross cows (3.7 vs. 11.2%). The incidence of scours or respiratory problems in calves did not differ between control Holsteins, other Holsteins, and backcrosses, nor did the incidence of mastitis, injury, or feet problems. Control Holstein heifers were heavier (629 vs. 557 kg), with greater hip height (145 vs. 139 cm), body length (167 vs. 163 cm), heart girth (205 vs. 198 cm), and hip width (54 vs. 53 cm) at 22 mo of age. On a 50-point scale for linear type traits, Holsteins were larger in stature compared with backcrosses (41 vs. 28), had wider rumps (37 vs. 33), and wider rear udders (34 vs. 32). Results of this study suggest that backcross Holstein × Jersey cattle have decreased production but fail to demonstrate an advantage in health and reproduction compared with purebred Holsteins.

Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins

Genome-enabled prediction of breeding values using high-density panels (HDP) can be highly accurate, even for young sires. However, the cost of the assay may limit its use to elite animals only. Low-density panels (LDP) containing a subset of single nucleotide polymorphisms (SNP) may give reasonably accurate predictions and could be used cost-effectively with young males and females. This study evaluates strategies for selecting subsets of SNP for several traits, compares predictive ability of LDP with that of HDP, and assesses the benefits of including parent average (PA) as a predictor in models using LDP. Data consisting of progeny-test predicted transmitting ability (PTA) for net merit and 6 other traits of economic interest from 4,783 Holstein sires were evaluated using testing and training sets with regressions on their high-density genotypes and parent averages for net merit index. Additionally, SNP subsets of different sizes were selected using different strategies, including the "best" SNP based on the absolute values of their estimated effects from HDP models for either the trait itself or lifetime net merit, and evenly spaced (ES) SNP across the genome. Overall, HDP models had the best predictive ability, setting an upper bound for the predictive ability of LDP sets. Low-density panels targeting the SNP with strongest effects (for either a single trait or lifetime net merit) provided reasonably accurate predictions and generally outperformed predictions based on evenly spaced SNP. For example, evenly spaced sets would require at least 5,000 to 7,500 SNP to reach 95% of the predictive ability provided by HDP. On the other hand, this level of predictive ability can be achieved with sets of 2,000 SNP when SNP are selected based on magnitude of estimated effects for the trait. Accuracy of predictions based on LDP can be improved markedly by including parent average as a fixed effect in the model; for example, a set with the 1,000 best SNP using the parent average achieved the 95% of the accuracy of a HDP model.

Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms

The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance.

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R(2)) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R(2) values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.

Predictive ability of models for calving difficulty in US Holsteins

The performance of alternative threshold models for analyzing calving difficulty (CD) in Holstein cows was evaluated in terms of predictive ability. Four models were considered, with CD classified into either three or four categories and analysed either as a single trait or jointly with gestation length (GL). The data contained GL and CD records from 90 393 primiparous cows, sired by 1122 bulls and distributed over 935 herd-calving year classes. Predictive ability of each model was evaluated using four criteria: mean squared error of the difference between observed and predicted CD scores; a Kullback-Leibler divergence measure between the observed and predicted distributions of CD scores; Pearson's correlation between observed and predicted CD scores and ability to correctly classify bulls as above or below average for incidence of CD. In general, the four models had similar predictive abilities. The joint analysis of CD with GL produced little, if any, improvement in predictive ability over univariate models. In light of the small difference in predictive ability between models treating CD with three or four categories and considering that a greater number of categories can provide more information, analysis of CD classified into four categories seems warranted.

Technical note: an R package for fitting generalized linear mixed models in animal breeding

Mixed models have been used extensively in quantitative genetics to study continuous and discrete traits. A standard quantitative genetic model proposes that the effects of levels of some random factor (e.g., sire) are correlated accordingly with their relationships. For this reason, routines for mixed models available in standard packages cannot be used for genetic analysis. The pedigreemm package of R was developed as an extension of the lme4 package, and allows mixed models with correlated random effects to be fitted for Gaussian, binary, and count responses. Following the method of Harville and Callanan (1989), a correlation between levels of the grouping factor (e.g., sire) is induced by post-multiplying the incidence matrix of the levels of this random factor by the Cholesky factor of the corresponding (co)variance matrix (e.g., the numerator relationship matrix between sires). Estimation methods available in pedigreemm include approximations to maximum likelihood and REML. This note describes the classes of models that can be fitted using pedigreemm and presents examples that illustrate its use.

Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows

Clinical mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of mastitis, it has rarely been used for studying the genetics of mastitis. Many models have been proposed for genetic analysis of mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were clinical mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, ..). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.

Effect of suckling restriction with nose plates and premature weaning on postpartum anestrous interval in primiparous cows under range conditions

Suckling and nutrition are generally recognized as two major factors controlling the duration of the postpartum anovulatory period. In the present study, the effect of premature weaning and suckling restriction with nose plates (NPs) on cow and calf performance was evaluated. The study was conducted over 2 years; primiparous Hereford cows, weighing (mean+/-S.E.M.) 344+/-3.5kg and with 4.1+/-0.05 units of body condition score (BCS) (scale 1-8 [Vizcarra, J.A., Ibañez, W., Orcasberro, R., 1986. Repetibilidad y reproductibilidad de dos escalas para estimar la condición corporal de vacas Hereford. Investigaciones Agronómicas 7 (1), 45-47]) at calving, remained with their calves until 72.5+/-1.2 days postpartum (day 0). They were then assigned to one of three treatments: (i) calves with free access to their dams and ad libitum suckling (S, n=29); (ii) calves fitted with NPs for 14 days, but remained with their dams (NP, n=29), and (iii) calves that were weaned from their dams (W, n=28). All cows were anestrus at the time treatments commenced (day 0). All cows were blood sampled twice weekly from 1 week before the beginning of the experiment until the end of the mating period (day 74) for progesterone analysis. The mating period began on day 14. Cows in W treatment had ovulations earlier (P<0.05) than those in NP and S groups. Cows in the NP group had longer (P<0.05) intervals between the first progesterone increase and normal luteal phase than cows in the other two treatments groups (23.3+/-3.2 vs. 6.5+/-3.2 and 5.2+/-3.3 days for NP, S and W cows, respectively). Fifty per cent of the cows with NP had a short cycle (7 days) but there was a group of cows that had longer (P<0.05) intervals (66 days) between first progesterone increase and normal estrous activity. In the NP group, 8 of 29 cows had a short luteal phase and then a normal one; for 9 of these 29 cows progesterone concentrations remained low for 6 weeks from the beginning of the treatment; and for 12 of these 29 cows progesterone concentrations initially increased after treatment initiation, but these animals became anestrus thereafter. Short-term suckling restriction with NPs led to a variable response in primiparous cows of moderate body condition under range conditions.

Nonparametric analysis of the impact of inbreeding on production in Jersey cows

Under dominance, the relationship between a quantitative trait and the inbreeding coefficient (F) is expected to be linear. Epistasis involving dominance effects or selection against inbred individuals can produce nonlinear patterns in the form of inbreeding depression. The form of the relationship between F and yield traits was explored via local regression (LOESS). First-lactation milk, fat, and protein records from 59,778 Jersey cows with at least 6 generations of known pedigree were used. The F ranged from 0.6 to 34% and median F was 6.3%. The LOESS regressions of predicted residuals from an animal model (empirical best linear unbiased predictions, EBLUP) on F were calculated for each trait; the EBLUP model included fixed herd-year-season, age at calving and DIM effects, and random additive genetic effects. The relationship between EBLUP residuals and inbreeding was complex and nonlinear. Yields were unaffected for F <or=7%, and inbreeding depression seemed to stabilize at F >20% for fat and protein yield. For SCS, both parametric and LOESS fits were consistent with the absence of sizable dominance effects. Effects of inbreeding on performance seemed to be more complex than suggested by previous studies based on linear regression. Results should be interpreted with caution because the data were scarce at high levels of F.

Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of Holstein cows has declined during the past 3 decades. Traits such as first-service conception rate and pregnancy status at 150 d in milk (DIM) are influenced by numerous explanatory factors common to specific farms or individual cows on these farms. Machine learning algorithms offer great flexibility with regard to problems of multicollinearity, missing values, or complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the reproductive performance of lactating Holstein cows on large dairy farms. This study used data from farms in the Alta Genetics Advantage progeny-testing program. Production and reproductive records from 153 farms were obtained from on-farm DHI-Plus, Dairy Comp 305, or PCDART herd management software. A survey regarding management, facilities, labor, nutrition, reproduction, genetic selection, climate, and milk production was completed by managers of 103 farms; body condition scores were measured by a single evaluator on 63 farms; and temperature data were obtained from nearby weather stations. The edited data consisted of 31,076 lactation records, 14,804 cows, and 317 explanatory variables for first-service conception rate and 17,587 lactation records, 9,516 cows, and 341 explanatory variables for pregnancy status at 150 DIM. An alternating decision tree algorithm for first-service conception rate classified 75.6% of records correctly and identified the frequency of hoof trimming maintenance, type of bedding in the dry cow pen, type of cow restraint system, and duration of the voluntary waiting period as key explanatory variables. An alternating decision tree algorithm for pregnancy status at 150 DIM classified 71.4% of records correctly and identified bunk space per cow, temperature for thawing semen, percentage of cows with low body condition scores, number of cows in the maternity pen, strategy for using a clean-up bull, and milk yield at first service as key factors.

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers

Genome-wide association studies using single nucleotide polymorphisms (SNPs) can identify genetic variants related to complex traits. Typically thousands of SNPs are genotyped, whereas the number of phenotypes for which there is genomic information may be smaller. When predicting phenotypes, options for statistical model building range from incorporating all possible markers into the specification to including only sets of relevant SNPs (features). In the latter case, an efficient method of selecting influential features is required. A two-step feature selection method for binary traits was developed, which consisted of filtering (using information gain), and wrapping (using naïve Bayesian classification). The filter reduces the large number of SNPs to a much smaller size, to facilitate the wrapper step. As the procedure is tailored for discrete outcomes, an approach based on discretization of phenotypic values was developed, to enable feature selection in a classification framework. The method was applied to chick mortality rates (0-14 days of age) on progeny from 201 sires in a commercial broiler line, with the goal of identifying SNPs (over 5000) related to progeny mortality. To mimic a case-control study, sires were clustered into two groups, low and high, according to two arbitrarily chosen mortality rate cut points. By varying these thresholds, 11 different 'case-control' samples were formed, and the SNP selection procedure was applied to each sample. To compare the 11 sets of chosen SNPs, predicted residual sum of squares (PRESS) from a linear model was used. The two-step method improved naïve Bayesian classification accuracy over the case without feature selection (from around 50 to above 90% without and with feature selection in each case-control sample). The best case-control group (63 sires above or below the thresholds) had the smallest PRESS statistic among groups with model p-values below 0.003. The 17 SNPs selected using this group accounted for 31% of the variation in raw mortality rates between sire families.

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers

In genome-wide association studies using single nucleotide polymorphisms (SNPs), typically thousands of SNPs are genotyped, whereas the number of phenotypes for which there is genomic information may be smaller. Atwo-step SNP (feature) selection method was developed, which consisted of filtering (using information gain), and wrapping (using naïve Bayesian classification). This was based on discretization of the continuous phenotypic values. The method was applied to chick early mortality rates (0-14 days of age) on progeny from 201 sires in a commercial broiler line, with the goal of identifying SNPs (over 5000) related to progeny mortality. Sires were clustered into two groups, low and high, according to two arbitrarily chosen mortality rate thresholds. By varying these thresholds, 11 different "case-control" samples were formed, and the SNP selection procedure was applied to each sample. To compare the 11 sets of chosen SNPs, predicted residual sum of squares (PRESS)from a linear model was used. Naive Bayesian classification accuracy was improved over the case without feature selection (from 50% to 90%). Seventeen SNPs in the best case-control group (with smallest PRESS) accounted for 31% of the variance among sire family mortality rates.

Embryonic disc development and subsequent viability of cattle embryos following culture in two media under two oxygen concentrations

Bovine embryos were produced in vitro using a 2 x 2 design of modified medium (KSOM or SOF) and oxygen concentration (5% or 20%). Day 7 blastocysts were transferred in bulk (n = 11, on average) to recipient heifers and recovered non-surgically at Day 14. In two replications of a Latin square, eight heifers received embryos from each combination of factors. Recovered embryos were evaluated for trophoblast length and width, as well as the presence and diameter of an embryonic disc (ED). An ED was detected in a higher percentage of embryos that had been cultured in KSOM than SOF (72% v. 46%, respectively; P < 0.05). The aim of a second series of experiments was to associate Day 14 morphology with subsequent developmental capacity. In vitro-produced blastocysts were transferred (n = 17-20) on Day 7 to each of eight heifers and recovered at Day 14. Thirty-eight blastocysts were retransferred to heifers following morphological evaluation. Embryos in which an ED with no signs of degeneration had been detected maintained more pregnancies than other embryos in which an ED had either shown signs of degeneration or had not been detected (5/8 v. 2/30, respectively; P < 0.01). Further investigation into ED integrity at the elongating stage may contribute to our understanding of pregnancy establishment and maintenance.

Estimation of Genetic Parameters for Perinatal Sucking Behavior of Italian Brown Swiss Calves

Brown Swiss breeders sometimes experience difficulties in feeding calves because of the weak sucking ability of the calves in the early days of life. For the welfare of the calves, they should be suckled by their dams or should aggressively ingest colostrum immediately after birth. The composition of colostrum changes rapidly during the first few days of lactation, and the ability of calves to absorb the Ig decreases quickly as well. The aim of this study was to increase our knowledge of environmental and genetic components affecting the sucking response, to evaluate the possibility of selecting for this trait. Sucking ability was recorded in 3 categories (drank from the milk bucket nipple or bottle without help, drank with help, did not drink) at 5 post-natal meals (6, 12, 24, 48, and 72 h from birth). Records were analyzed with 2 different models: a single-trait threshold sire model, in which all observations were analyzed as a single trait with 5 levels, and a multiple-trait threshold liability sire model, in which meal-by-meal observations were analyzed as 5 different binary traits. Management procedures, the interval between birth and meals, parity, and season of birth were environmental factors affecting the variability in sucking ability. The heritability estimate for the single-trait analysis was 0.14, whereas heritabilities for the multiple-trait analysis were 0.26, 0.22, 0.21 0.12, and 0.13 for the first, second, third, fourth, and fifth meal, respectively. Estimated genetic correlations among traits were high (0.82 to 0.99). This study suggests the possibility of selection based on sucking ability. Future collection of larger data sets on the sucking response of calves in the first 2 meals after birth would increase the accuracy of genetic parameter estimates.

Genetic Analysis of the Twenty-One-Day Pregnancy Rate in US Holsteins Using an Ordinal Censored Threshold Model with Unknown Voluntary Waiting Period

Genetic variation in the number of 21-d opportunity periods required to achieve pregnancy after the voluntary waiting period (VWP) had passed was examined using 44,901 lactation records of 29,422 lactating Holstein cows on 61 large commercial dairy farms in the United States. Cows were allowed a maximum of 8 opportunity periods, and the cumulative percentages of cows that became pregnant by the end of the first, second, third, fourth, and fifth opportunity periods were 19, 29, 37, 43, and 47%, respectively. In addition, 38% of records were censored because of culling or failure to achieve pregnancy after 8 opportunity periods. Mean days open was 128 d for complete records, whereas mean days to last service was 148 d for censored records. An ordinal censored threshold model was developed, in which duration of the VWP was estimated simultaneously with prediction of sire breeding values. The posterior mean of intraherd-year heritability for the number of 21-d opportunity periods required to achieve pregnancy was 0.06, with a posterior standard deviation of 0.01. Posterior means for duration of the VWP ranged from 28 to 74 d postpartum among the 116 herd-parity classes represented in the study, whereas farmer-reported survey values for duration of the VWP ranged from 30 to 78 d postpartum. Sires' predicted transmitting abilities were computed, assuming an unknown VWP (i.e., estimated from the data), a VWP fixed at 60 d postpartum, or a VWP fixed at farmer survey values. Correlations among sire predicted transmitting abilities from different models were > or = 0.98, although some reranking occurred among top sires. In summary, the proposed model for genetic evaluation of female fertility can accommodate heterogeneity in duration of the VWP between herds, as well as heterogeneity that may arise within herds owing to management practices such as intentional delay of first insemination in high-producing cows or cows with poor body condition, and it can also accommodate censored records for nonpregnant cows.

Survey of Management Practices on Reproductive Performance of Dairy Cattle on Large US Commercial Farms

A survey regarding general management, sire selection, reproductive management, inseminator training and technique, heat abatement, body condition scoring, facility design and grouping, nutrition, employee training and management, and animal health and bio-security was carried out from March to September of 2004 in 153 herds in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program. A total of 103 herds (67.3%) completed the survey. Herd size was 613 +/- 46 cows, with herds located in Wisconsin (26), California (12), New York (11), Minnesota (10), Michigan (7), Washington (6), Pennsylvania (6), Iowa (5), Idaho (5), Texas (4), Ohio (4), and other states (7). These farms sold 34.5 +/- 0.3 kg of milk/d per cow, with an annual culling rate of 34 +/- 1% and a calving interval of 13.8 +/- 0.1 mo. Cows were observed for estrus 2.8 +/- 0.3 times/d, for a duration of 27 +/- 4 min, but 78% of the respondents admitted that detection of estrus was not the employee's sole responsibility at that time. Managers tried to achieve pregnancy until 8.8 +/- 0.9 failed inseminations, 300 +/- 26 d postpartum, or milk yield <17.7 +/- 0.5 kg/d. Nonpregnant cows were culled at 326 +/- 36 d postpartum or milk yield <16.4 +/- 0.3 kg/ d. Mean durations of the voluntary waiting period were 52 +/- 1.3 and 53 +/- 1.4 d for primiparous and multiparous cows, respectively. Hormonal synchronization or timed artificial insemination programs were used in 87% of the herds, with 86% synchronizing first services, 77% resynchronizing repeat services, and 59% treating cystic, anestrous, or anovular cows. Finding good employees was identified as the greatest labor challenge, followed by training and supervising employees. Mastitis and hairy heel warts were noted as the greatest animal health concerns, followed by lameness, abortions, and death losses, whereas the greatest reproductive challenges were artificial insemination service rate, conception rate, twinning, and retained placenta or metritis. Results of this study can provide a useful benchmark or reference with regard to commonly used management practices on large commercial US dairy farms at the present time.

Selection for Female Fertility Using Censored Fertility Traits and Investigation of the Relationship with Milk Production

Bivariate models (censored linear-linear and censored threshold-linear) were used to estimate genetic parameters for production and fertility traits in the Spanish Holstein population. Records on 71,217 lactations from 41,515 cows were used: 30 and 36% of lactations were censored for days open (DO) and number of inseminations to conception (INS), respectively. Heritability estimates for production traits (milk, fat, protein) ranged between 0.18 and 0.25. Heritability of days to first service (DFS) and DO was 0.05; heritability of INS on the liability scale was 0.04. Genetic correlations between fertility traits were 0.41, 0.71, and 0.87 for DFS-INS, DO-INS, and DO-DFS, respectively. Days open had a larger genetic correlation (ranging from 0.63 to 0.76) with production traits than did DFS (0.47 to 0.59) or INS (0.16 to 0.23). Greater antagonism between production and DO may be due to voluntary management decisions for high-yielding cows, resulting in longer lactation lengths. Inseminations to conception appeared to be less correlated with milk production than were the other 2 female fertility traits. Including INS in a total merit index would be expected to increase genetic gain in terms of profit, but profit would decrease if either DO or DO and DFS were included in the index. Thus, INS is the trait to be preferred when selecting for female fertility. The genetic correlation between actual milk yield and 305-d standardized milk yield was 0.96 in the present study, suggesting that some reranking of sires could occur. Because the target of attaining a 12-mo calving interval, as implied by a 305-d standardized lactation length, is changing in the dairy industry, routine genetic evaluation of actual total lactation milk yield should be considered.