Investigation of cosmopolitan and local Italian beef cattle breeds uncover common patterns of heterozygosity.

The analysis of livestock heterozygosity is less common compared to the study of homozygous patterns. Heterozygous-Rich Regions (HRRs) may harbor significant loci for functional traits such as immune response, survival rate, and fertility. For this reason, this study was conducted to investigate and characterize the heterozygosity patterns of four beef cattle breeds, which included two cosmopolitan breeds (Limousine and Charolaise) and two local breeds (Sarda and Sardo Bruna). Our analysis identified regions with a high degree of heterozygosity using a consecutive runs approach, the Tajima D test, nucleotide diversity estimation, and Hardy Weinberg equilibrium test. These regions exhibited recurrent heterozygosity peaks and were consistently found on specific chromosomes across all breeds, specifically autosomes 15, 16, 20, and 23. The cosmopolitan and Sardo Bruna breeds also displayed peaks on autosomes 2 and 21, respectively. Thirty-five top runs shared by more than 25% of the populations were identified. These genomic fragments encompassed 18 genes, two of which are directly linked to male fertility, while four are associated with lactation. Two other genes play roles in survival and immune response. Our study also detected a region related to growth and carcass traits in Limousine breed. Our analysis of heterozygosity-rich regions revealed particular segments of the cattle genome linked to various functional traits. It appears that balancing selection is occurring in specific regions within the four examined breeds, and unexpectedly, they are common across cosmopolitan and local breeds. The genes identified hold potential for applications in breeding programs and conservation studies to investigate the phenotypes associated with these heterozygous genotypes. In addition, Tajima D test, Nucleotide diversity, and Hardy Weinberg equilibrium test confirmed the presence of heterozygous fragments found with Heterozygous-Rich Regions analysis.

Definition of Environmental Variables and Critical Periods to Evaluate Heat Tolerance in Large White Pigs Based on Single-Step Genomic Reaction Norms.

Properly quantifying environmental heat stress (HS) is still a major challenge in livestock breeding programs, especially as adverse climatic events become more common. The definition of critical periods and climatic variables to be used as the environmental gradient is a key step for genetically evaluating heat tolerance (HTol). Therefore, the main objectives of this study were to define the best critical periods and environmental variables (ENV) to evaluate HT and estimate variance components for HT in Large White pigs. The traits included in this study were ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN), and weaning to estrus interval (IWE). Seven climatic variables based on public weather station data were compared based on three criteria, including the following: (1) strongest G×E estimate as measured by the slope term, (2) ENV yielding the highest theoretical accuracy of the genomic estimated breeding values (GEBV), and (3) variable yielding the highest distribution of GEBV per ENV. Relative humidity (for BFT, MDP, NBD, WN, and WW) and maximum temperature (for OTW, TNB, NBA, IBF, and IWE) are the recommended ENV based on the analyzed criteria. The acute HS (average of 30 days before the measurement date) is the critical period recommended for OTW, BFT, and MDP in the studied population. For WN, WW, IBF, and IWE, a period ranging from 34 days prior to farrowing up to weaning is recommended. For TNB, NBA, and NBD, the critical period from 20 days prior to breeding up to 30 days into gestation is recommended. The genetic correlation values indicate that the traits were largely (WN, WW, IBF, and IWE), moderately (OTW, TNB, and NBA), or weakly (MDP, BFT, and NBD) affected by G×E interactions. This study provides relevant recommendations of critical periods and climatic gradients for several traits in order to evaluate HS in Large White pigs. These observations demonstrate that HT in Large White pigs is heritable, and genetic progress can be achieved through genetic and genomic selection.

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study.

About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1-S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.

Symposium review: Exploiting homozygosity in the era of genomics-Selection, inbreeding, and mating programs.

The advent of genomic selection paved the way for an unprecedented acceleration in genetic progress. The increased ability to select superior individuals has been coupled with a drastic reduction in the generation interval for most dairy populations, representing both an opportunity and a challenge. Homozygosity is now rapidly accumulating in dairy populations. Currently, inbreeding depression is managed mostly by culling at the farm level and by controlling the overall accumulation of homozygosity at the population level. A better understanding of how homozygosity and recessive load are related will guarantee continued genetic improvement while curtailing the accumulation of harmful recessives and maintaining enough genetic variability to ensure the possibility of selection in the face of changing environmental conditions. In this review, we present a snapshot of the current dairy selection structure as it relates to response to selection and accumulation of homozygosity, briefly outline the main approaches currently used to manage inbreeding and overall variability, and present some approaches that can be used in the short term to control accumulation of harmful recessives while maintaining sustained selection pressure.

Analysis of milk leukocyte differential measures for use in management practices for decreased mastitis incidence.

The aim of this study was to assess the usefulness of measures derived from milk leukocyte differential (MLD) in practices that improve fresh cow mastitis monitoring and decrease mastitis incidence. Quarter milk samples were collected from Holstein and Jersey cows on d 4 and 11 postcalving. Samples were analyzed using MLD, whereby cell counts and quarter infection diagnosis were obtained. Measures derived from MLD included cell scores (total leukocyte, neutrophil, macrophage, and lymphocyte scores), cell proportions (neutrophil, macrophage, and lymphocyte percentages), cell thresholds (total leukocyte, neutrophil, macrophage, and lymphocyte thresholds), and MLD diagnosis at different threshold settings (A, B, and C). Microbiological culturing of milk samples was used to determine infection status to compare the MLD diagnosis and serve as an indicator of infection. Measures derived from the microbiological analysis included occurrence of major pathogens, minor pathogens, and infection. Data analysis was based on a linear mixed model, which was used on all measures for the estimation of the fixed effects of breed, lactation number, day of sample collection, time of sampling, and quarter location, and the random effects of animal and week of sampling. All the fixed effects studied were significant for one or more of the analyzed measures. The results of this study showed that MLD-derived measures justify further study on their use for management practices for mastitis screening and prevention in early lactation.

Short communication: Association analysis of diacylglycerol acyltransferase (DGAT1) mutation on chromosome 14 for milk yield and composition traits, somatic cell score, and coagulation properties in Holstein bulls.

The aim of the present study was to determine the allele frequencies of the diacylglycerol acyltransferase (DGAT1) K232A mutation in Italian Holstein bulls and to estimate the effect of the mutation on milk yield, composition, somatic cell score, and coagulation traits (rennet coagulation time and curd firmness). For this purpose, 349 Italian Holstein bulls were genotyped for the DGAT1 mutation on chromosome 14. Association analysis was performed by regressing the number of copies for the K allele on the deregressed estimated breeding value of the individual. Breeding values were calculated using field data routinely collected in Northeast Italy. The frequencies of the AA, KA, and KK genotypes were 59.6, 32.1, and 8.3%, respectively, and the minor allele frequency (K variant) was 24.7%. The K allele was significantly associated with greater fat yield and fat, protein, and casein percentages and with reduced protein:fat ratio. The association between the DGAT1 mutation and somatic cell score was not significant, whereas a favorable association between presence of the K allele and milk coagulation properties was found. Results from the present study confirmed the effect of the diallelic DGAT1 polymorphism K232A on milk production traits and, for the first time, provided evidence that this mutation also affects milk coagulation properties in the Italian Holstein breed.

Bacterial and fungal communities, fermentation, and aerobic stability of conventional hybrids and brown midrib hybrids ensiled at low moisture with or without a homo- and heterofermentative inoculant.

We evaluated the effects of adding a combination inoculant to 4 corn (Zea mays L.) hybrids harvested at low moisture on the nutritive value, fermentation profile, aerobic stability, bacterial and fungal populations, and community structure. The treatment design was the factorial combination of 4 corn hybrids ensiled with (INO) and without (CON) inoculant. The hybrids were TMF2R737 (MCN), F2F817 (MBR), P2089YHR (PCN), and PI144XR (PBR), ensiled at 44.0, 38.1, 42.0, and 41.3% of dry matter, respectively; MBR and PBR were brown midrib mutants. The inoculant contained Lactobacillus buchneri and Pediococcus pentosaceus (4 × 105 and 1 × 105 cfu/g of fresh corn). The experimental design was a complete randomized design with treatments replicated 6 times. Corn was chopped, treated or not with inoculant, packed into 7.6-L bucket silos, and stored for 100 d. At d 0, we found higher bacterial observed operational taxonomic units in the brown midrib mutants (MBR and PBR) relative to MCN and PCN (654 and 534 vs. 434 and 444 ± 15.5, respectively). The bacterial and fungal families with the highest relative abundance (RA) were Enterobacteriaceae (61.4%) and incertae sedis Tremellales (12.5%). At silo opening, we observed no effects of INO treatment on dry matter recovery (∼94.3 ± 1.07%), but aerobic stability was extended for all INO-treated hybrids (∼217 vs. ∼34.7 h), except for MBR (∼49 ± 38 h), due to a decreased yeast population (3.78 vs. 5.13 ± 0.440 log cfu/g of fresh corn) and increased acetic acid concentration (1.69 vs. 0.51 ± 0.132%) compared with the control. Furthermore, INO treatment reduced bacterial (61.2 vs. 276 ± 8.70) and increased fungal (59.8 vs. 43.6 ± 2.95) observed operational taxonomic units compared with CON. We observed that INO treatment increased the RA of Lactobacillaceae across all hybrids (∼99.1 vs. ∼58.9), and to larger extent MBR (98.3 vs. 34.3 ± 5.29), and decreased Enterobacteriaceae (0.614 vs. 23.5 ± 2.825%) among 4 other bacterial families relative to CON. For fungi, INO treatment increased the RA of Debaryomycetaceae (63.1 vs. 17.3 ± 8.55) and 5 other fungal families and decreased the RA of Pichiaceae (6.47 vs. 47.3 ± 10.95) and incertae sedis Saccharomycetales (8.47 vs. 25.9 ± 5.748) compared with CON. The bacterial and fungal community structures changed, due to ensiling, to a distinct and more stable community dominated by Lactobacillaceae and Debaryomycetaceae, respectively, when INO treatment was applied relative to CON. In conclusion, the INO treatment used in this study improved low-moisture whole-crop corn silage quality because of a shift in the bacterial and fungal community composition during ensiling.

A comparison of accuracy validation methods for genomic and pedigree-based predictions of swine litter size traits using Large White and simulated data.

The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single-step GBLUP (ssGBLUP) to traditional pedigree-based BLUP. Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance (PEV) of the direct inverse (iLHS), (ii) approximated accuracies from the accf90(GS) program in the BLUPF90 family of programs (Approx), (iii) correlation between predictions and the single-step GEBVs from the full data set (GEBVFull ), (iv) correlation between predictions and the corrected phenotypes of females from the full data set (Yc ), (v) correlation from method iv divided by the square root of the heritability (Ych ) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes (Ycs ). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ssGBLUP. Both iLHS and Ych approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBVFull performed poorly in both data sets and is not recommended. Results suggest that for within-breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV, correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set.

A heuristic method to identify runs of homozygosity associated with reduced performance in livestock.

Although, for the most part, genome-wide metrics are currently used in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here, we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix (ILM). Diagonals of ILM characterize the functional individual inbreeding load (IIL). We estimated the accuracy of predicting the phenotype based on IIL. We further compared the significance of the regression coefficient for IIL on phenotypes with genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (12 scenarios), combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from 2 maternal purebred swine lines. In simulated data, as the LD in the population increased, the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified rose from 32 to 41% for the low- to the high-LD scenario. In both simulated and real data, the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than 0 across all scenarios for simulated data (mean of 0.49 [95% confidence interval 0.47-0.52] for the high-LD scenario) and for nearly all swine traits (mean of 0.17 [SD 0.10]). On average, across simulated and swine data sets, the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.

The relationship between different measures of feed efficiency and feeding behavior traits in Duroc pigs.

Utilization of feed in livestock species consists of a wide range of biological processes, and therefore, its efficiency can be expressed in various ways, including direct measurement, such as daily feed intake, as well as indicator measures, such as feeding behavior. Measuring feed efficiency is important to the swine industry, and its accuracy can be enhanced by using automated feeding systems, which record feed intake and associated feeding behavior of individual animals. Each automated feeder space is often shared among several pigs and therefore raises concerns about social interactions among pen mates with regard to feeding behavior. The study herein used a data set of 14,901 Duroc boars with individual records on feed intake, feeding behavior, and other off-test traits. These traits were modeled with and without the random spatial effect of Pen_Room, a concatenation of room and pen, or random social interaction among pen mates. The nonheritable spatial effect of common Pen-Room was observed for traits directly measuring feed intake and accounted for up to 13% of the total phenotypic variance in the average daily feeding rate. The social interaction effect explained larger proportions of phenotypic variation in all the traits studied, with the highest being 59% for ADFI in the group of feeding behaviors, 73% for residual feed intake (RFI; RFI4 and RFI6) in the feed efficiency traits, and 69% for intramuscular fat percentage in the off-test traits. After accounting for the social interaction effect, residual BW gain and RFI and BW gain (RIG) were found to have the heritability of 0.38 and 0.18, respectively, and had strong genetic correlations with growth and off-test traits. Feeding behavior traits were found to be moderately heritable, ranging from 0.14 (ADFI) to 0.52 (average daily occupation time), and some of them were strongly correlated with feed efficiency measures; for example, there was a genetic correlation of 0.88 between ADFI and RFI6. Our work suggested that accounting for the social common pen effect was important for estimating genetic parameters of traits recorded by the automated feeding system. Residual BW gain and RIG appeared to be two robust measures of feed efficiency. Feeding behavior measures are worth further investigation as indicators of feed efficiency.

Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires.

Advances in DNA-based marker technology have enabled the identification of genomic regions underlying complex phenotypic traits in livestock species. The incorporation of detected quantitative trait loci into genetic evaluation provides great potential to enhance selection accuracies, hence expediting the genetic improvement of economically important traits. The objective of the present study was to investigate 96 single nucleotide polymorphisms (SNP) located in 53 candidate genes previously reported to have effects on milk production and quality traits in a population of highly selected Holstein-Friesian bulls. A total of 423 semen samples were used to genotype the bulls through a custom oligo pool assay. Forty-five SNP in 32 genes were found to be associated with at least 1 of the tested traits. Most significant and favorable SNP trait associations were observed for polymorphisms located in CCL3 and AGPAT6 genes for fat yield (0.037 and 0.033 kg/d, respectively), DGKG gene for milk yield (0.698 kg/d), PPARGC1A, CSN1S1, and AGPAT6 genes for fat percentage (0.127, 0.113, and 0.093%, respectively), GHR gene for protein (0.064%) and casein percentage (0.053%), and TLR4 gene for fat (0.090%), protein (0.066%), and casein percentage (0.050%). Somatic cell score was favorably affected by GHR (-0.095) and POU1F1 (-0.137), and interesting SNP-trait associations were observed for polymorphisms located in CSN2, POU1F1, and AGPAT6 genes for rennet coagulation time (-0.592, -0.558, and -0.462 min, respectively), and GHR and CSN2 genes for curd firmness 30 min after rennet addition (1.264 and 1.183 mm, respectively). In addition to the influence of individual SNP, the effects of composite genotypes constructed by grouping SNP according to their individual effects on traits considered in the analysis were also examined. Favorable and significant effects on milk traits were observed for 2 composite genotypes, one including 10 SNP and the other 4 SNP. The former was associated with an increase of milk (0.075 kg/d), fat (0.097 kg/d), protein (0.083 kg/d), and casein yields (0.065 kg/d), and the latter was associated with an increase of fat (0.244%), protein (0.071%), and casein percentage (0.047%). Although further research is required to validate the identified SNP loci in other populations and breeds, our results can be considered as a preliminary foundation for further replication studies on gene-assisted selection programs.

Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations.

The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of αS2-casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible.

Geno-Diver: A combined coalescence and forward-in-time simulator for populations undergoing selection for complex traits.

Geno-Diver is a combined coalescence and forward-in-time simulator designed to simulate complex traits with a quantitative and/or fitness component and implement multiple selection and mating strategies utilizing pedigree or genomic information. The simulation is carried out in two steps. The first step generates whole-genome sequence data for founder individuals. A variety of trait architectures can be generated for quantitative and fitness traits along with their covariance. The second step generates new individuals forward-in-time based on a variety of selection and mating scenarios. Genetic values are predicted for individuals utilizing pedigree or genomic information. Relationship matrices and their associated inverses are generated using computationally efficient routines. We benchmarked Geno-Diver with a previous simulation program and described how to simulate a traditional quantitative trait along with a quantitative and fitness trait. A user manual with examples, source code in C++11 and executable versions of Geno-Diver for Linux are freely available at https://github.com/jeremyhoward/Geno-Diver.

Laboratory silo type and inoculation effects on nutritional composition, fermentation, and bacterial and fungal communities of oat silage.

The objectives were to evaluate (1) the use of 2 types of experimental silos (S) to characterize whole-crop oat (Avena sativa L.) silage with or without addition of an inoculant (I), and (2) the effect of inoculation on the microbial community structure of oats ensiled using only plastic bucket silos (BKT). From each of 6 sections in a field, oats were harvested, treated (INO) or not (CON) with inoculant, packed into 19-L BKT or vacuum bags (BG), and ensiled for 217 d. The inoculant added contained Lactobacillus buchneri and Pediococcus pentosaceus (4 × 105 and 1 × 105 cfu/g of fresh oats, respectively). The experimental design was a complete randomized design replicated 6 times. Treatment design was the factorial combination of 2 S × 2 I. Some differences existed between BG versus BKT at silo opening (217 d), including a decreased CP (7.73 vs. 7.04 ± 0.247% of DM) and ethanol (1.93 vs. 1.55 ± 0.155) and increased lactic acid (4.28 vs. 3.65 ± 0.241), respectively. Also, WSC and mold counts were reduced in BG versus BKT for CON (1.78 vs. 2.70 ± 0.162% of DM and 0.8 vs. 2.82 ± 0.409 log cfu/fresh g) but not for INO (∼1.53 and 1.55), respectively. Application of INO increased DM recovery (96.1 vs. 92.9 ± 0.63%), aerobic stability (565 vs. 133 ± 29.2 h), acetic acid (2.38 vs. 1.22 ± 0.116% of DM), and reduced NDF (65.0 vs. 67.0 ± 0.57), ADF (36.7 vs. 38.1 ± 0.60), ethanol (0.63 vs. 2.85 ± 0.155), and yeast counts (1.10 vs. 4.13 ± 0.484 log cfu/fresh g) in INO versus CON, respectively. At d 0, no differences were found for S and I on the nutritional composition and background microbial counts. Leuconostocaceae (82.9 ± 4.27%) and Enterobacteriaceae (15.2 ± 3.52) were the predominant bacterial families and unidentified sequences were predominant for fungi. A higher relative abundance of the Davidiellaceae fungal family (34.3 vs. 19.6 ± 4.47) was observed in INO versus CON. At opening (217 d), INO had a lower relative abundance of Leuconostocaceae (42.3 vs. 95.8 ± 4.64) and higher Lactobacillaceae (57.4 vs. 3.9 ± 4.65) versus CON. Despite several differences were found between BKT and BG, both techniques can be comparable for characterizing effects of INO on the most basic measures used in silage evaluation. The use of inoculant improved oat silage quality partially by a shift in the bacterial community composition during ensiling, which mainly consisted of an increased relative abundance of Lactobacillaceae and reduction of Leuconostocaceae relative to CON.

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle.

Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments.

The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders.

Obtaining accurate individual feed intake records is the key first step in achieving genetic progress toward more efficient nutrient utilization in pigs. Feed intake records collected by electronic feeding systems contain errors (erroneous and abnormal values exceeding certain cutoff criteria), which are due to feeder malfunction or animal-feeder interaction. In this study, we examined the use of a novel data-editing strategy involving multiple imputation to minimize the impact of errors and missing values on the quality of feed intake data collected by an electronic feeding system. Accuracy of feed intake data adjustment obtained from the conventional linear mixed model (LMM) approach was compared with 2 alternative implementations of multiple imputation by chained equation, denoted as MI (multiple imputation) and MICE (multiple imputation by chained equation). The 3 methods were compared under 3 scenarios, where 5, 10, and 20% feed intake error rates were simulated. Each of the scenarios was replicated 5 times. Accuracy of the alternative error adjustment was measured as the correlation between the true daily feed intake (DFI; daily feed intake in the testing period) or true ADFI (the mean DFI across testing period) and the adjusted DFI or adjusted ADFI. In the editing process, error cutoff criteria are used to define if a feed intake visit contains errors. To investigate the possibility that the error cutoff criteria may affect any of the 3 methods, the simulation was repeated with 2 alternative error cutoff values. Multiple imputation methods outperformed the LMM approach in all scenarios with mean accuracies of 96.7, 93.5, and 90.2% obtained with MI and 96.8, 94.4, and 90.1% obtained with MICE compared with 91.0, 82.6, and 68.7% using LMM for DFI. Similar results were obtained for ADFI. Furthermore, multiple imputation methods consistently performed better than LMM regardless of the cutoff criteria applied to define errors. In conclusion, multiple imputation is proposed as a more accurate and flexible method for error adjustments in feed intake data collected by electronic feeders.

Variance component estimates for alternative litter size traits in swine.

Litter size at d 5 (LS5) has been shown to be an effective trait to increase total number born (TNB) while simultaneously decreasing preweaning mortality. The objective of this study was to determine the optimal litter size day for selection (i.e., other than d 5). Traits included TNB, number born alive (NBA), litter size at d 2, 5, 10, 30 (LS2, LS5, LS10, LS30, respectively), litter size at weaning (LSW), number weaned (NW), piglet mortality at d 30 (MortD30), and average piglet birth weight (BirthWt). Litter size traits were assigned to biological litters and treated as a trait of the sow. In contrast, NW was the number of piglets weaned by the nurse dam. Bivariate animal models included farm, year-season, and parity as fixed effects. Number born alive was fit as a covariate for BirthWt. Random effects included additive genetics and the permanent environment of the sow. Variance components were plotted for TNB, NBA, and LS2 to LS30 using univariate animal models to determine how variances changed over time. Additive genetic variance was minimized at d 7 in Large White and at d 14 in Landrace pigs. Total phenotypic variance for litter size traits decreased over the first 10 d and then stabilized. Heritability estimates increased between TNB and LS30. Genetic correlations between TNB, NBA, and LS2 to LS29 with LS30 plateaued within the first 10 d. A genetic correlation with LS30 of 0.95 was reached at d 4 for Large White and at d 8 for Landrace pigs. Heritability estimates ranged from 0.07 to 0.13 for litter size traits and MortD30. Birth weight had an h of 0.24 and 0.26 for Large White and Landrace pigs, respectively. Genetic correlations among LS30, LSW, and NW ranged from 0.97 to 1.00. In the Large White breed, genetic correlations between MortD30 with TNB and LS30 were 0.23 and -0.64, respectively. These correlations were 0.10 and -0.61 in the Landrace breed. A high genetic correlation of 0.98 and 0.97 was observed between LS10 and NW for Large White and Landrace breeds, respectively. This would indicate that NW could possibly be used as an effective maternal trait, given a low level of cross-fostering, to avoid back calculating litter size traits from piglet records. Litter size at d 10 would be a compromise between gain in litter size at weaning and minimizing the potentially negative effects of the nurse dam and direct additive genetics of the piglets, as they are expected to increase throughout lactation.

Short communication: Genomic selection for hoof lesions in first-parity US Holsteins.

Hoof lesions contributing to lameness are crucial economic factors that hinder the profitability of dairy enterprises. Producer-recorded hoof lesions data of US Holsteins were categorized into infectious (abscess, digital and interdigital dermatitis, heel erosion, and foot rot) and noninfectious (korn, corkscrew, sole and toe ulcer, sole hemorrhage, white line separation, fissures, thin soles, and upper leg lesions) categories of hoof lesions. Pedigree- and genomic-based univariate analyses were conducted to estimate the variance components and heritability of infectious and noninfectious hoof lesions. A threshold sire model was used with fixed effects of year-seasons and random effects of herd and sire. For genomic-based analysis, a single-step procedure was conducted, incorporating H matrix to estimate genomic variance components and heritability for hoof lesions. The pedigree-based analysis produced heritability estimates of 0.11 (±0.05) for infectious hoof lesions and 0.08 (±0.05) for noninfectious hoof lesions. The single-step genomic analysis produced heritability estimates of 0.14 (±0.06) for infectious hoof lesions and 0.12 (±0.08) for noninfectious hoof lesions. Approximated genetic correlations between hoof lesion traits and hoof type traits along with productive life and net merit were all low and ranged between -0.25 and 0.14. Sire reliabilities increased, on average, by 0.24 and 0.18 for infectious and noninfectious hoof lesions, respectively, with incorporation of genomic data.

Inferring causal relationships between reproductive and metabolic health disorders and production traits in first-lactation US Holsteins using recursive models.

Health disorders in dairy cows have a substantial effect on the profitability of a dairy enterprise because of loss in milk sales, culling of unhealthy cows, and replacement costs. Complex relationships exist between health disorders and production traits. Understanding the causal structures among these traits may help us disentangle these complex relationships. The principal objective of this study was to use producer-recorded data to explore phenotypic and genetic relationships among reproductive and metabolic health disorders and production traits in first-lactation US Holsteins. A total of 77,004 first-lactation daughters' records of 2,183 sires were analyzed using recursive models. Health data contained information on reproductive health disorders [retained placenta (RP); metritis (METR)] and metabolic health disorders [ketosis (KETO); displaced abomasum (DA)]. Production traits included mean milk yield (MY) from early lactation (mean MY from 6 to 60 d in milk and from 61 to 120 d in milk), peak milk yield (PMY), day in milk of peak milk yield (PeakD), and lactation persistency (LP). Three different sets of traits were analyzed in which recursive effects from each health disorder on culling, recursive effects of one health disorder on another health disorder and on MY, and recursive effects of each health disorder on production traits, including PeakD, PMY, and LP, were assumed. Different recursive Gaussian-threshold and threshold models were implemented in a Bayesian framework. Estimates of the structural coefficients obtained between health disorders and culling were positive; on the liability scale, the structural coefficients ranged from 0.929 to 1.590, confirming that the presence of a health disorder increased culling. Positive recursive effects of RP to METR (0.117) and of KETO to DA (0.122) were estimated, whereas recursive effects from health disorders to production traits were negligible in all cases. Heritability estimates of health disorders ranged from 0.023 to 0.114, in accordance with previous studies. Similarly, genetic correlations obtained between health disorders were moderate. The results obtained suggest that reproductive and metabolic health disorder and culling due to metabolic and reproductive diseases have strong causal relationships. Based on these results, we concluded that a health disorder (either reproductive or metabolic) occurring in early lactation has a moderate causal effect on the reproductive or metabolic health disorder occurring in later lactation. In addition, direct, indirect, and overall effects of reproductive and metabolic health disorders on milk yields for cows that avoid culling are weak.

Short communication: Genetic analysis of dairy bull fertility from field data of Brown Swiss cattle.

The aim of this study was to estimate heritability and repeatability of dairy bull fertility in Italian Brown Swiss cattle. Bull fertility indicators were calving per service and nonreturn rate at 56 d after service. Data included 124,206 inseminations carried out by 86 technicians on 28,873 heifers and cows in 1,400 herds. Services were recorded from 1999 to 2008 and were performed with semen from 306 AI Brown Swiss bulls. Data were analyzed with a threshold animal model, which included the fixed effects of parity by class of days in milk of the inseminated cow (age at insemination for heifers), year-season of insemination, and status of the service bull at the time of insemination (progeny testing or proven), and the random effects of herd, technician, additive genetic, and permanent environment of inseminated heifer/cow and service bull, and residual. Also, genetic covariance between heifer/cow and service bull effects was considered in the model. Heritability and repeatability were 0.0079 and 0.0100 for nonreturn rate at 56 d after service, and 0.0153 and 0.0202 for calving per service, respectively. The low estimates obtained in the present study indicate that selection for male fertility using field data is hardly pursuable.