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Bull fertility has been recognized as an important factor affecting dairy herd fertility. The objective of this study was to assess the feasibility of predicting male fertility in Brown Swiss cattle using genomic data. The dataset consisted of 1,102 Italian Brown Swiss bulls with sire conception rate (SCR) records and genotype data for roughly 480k SNP. The analyses included the use of linear kernel-based regression models fitting all SNPs or incorporating markers with large effect. Predictive performance was evaluated in 5-fold cross-validation using the correlation between observed and predicted SCR values and mean squared error of prediction. The entire SNP set exhibited predictive correlations around 0.19. Interestingly, the inclusion of 2 markers with large effect yielded predictive correlations around 0.32. Overall, using linear kernel-based models fitting markers with large effect is a promising approach. Our findings could help Brown Swiss breeders make enhanced genome-guided management and selection decisions on male fertility.

Abstract Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1–6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

ABSTRACT: The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first dataset consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear-tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin–Madison. The second dataset consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; SAE Afikim, Kibbutz Afikim, Israel) and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included DMI, change in BW, metabolic BW, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) and lying time had a negative genetic correlation with this residual feed intake (−0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed-efficient cows within the herd.

ABSTRACT: The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly affects the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, the rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (1) infer the mediation effects of the rumen microbiome on feed efficiency traits, (2) estimate the direct and total heritability of feed efficiency traits, and (3) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), and the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7% to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability

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

ABSTRACT: Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, IDEXX Paratuberculosis Screening Ab Test (IDX) and Parachek 2 (PCK), were used to analyze milk samples with the ELISA technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of EBVs of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant. The correlations of bulls' PTA with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

ABSTRACT: The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were −0.38 and −0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from −0.41 to −0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

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

Abstract Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Abstract Periparturient hypocalcemia is a complex metabolic disorder that occurs at the onset of lactation because of a sudden irreversible loss of Ca incorporated into colostrum and milk. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, commonly known as milk fever, whereas a larger proportion of cows develop subclinical hypocalcemia. The main goal of this study was to identify causative mutations and candidate genes affecting postpartum blood calcium concentration in Holstein cows. Data consisted of blood calcium concentration measured in 2513 Holstein cows on the first three days after parturition. All cows had genotypic information for 79 k SNP markers. Two consecutive rounds of imputation were performed: first, the 2513 Holstein cows were imputed from 79 k to 312 k SNP markers. This imputation was performed using a reference set of 17,131 proven Holstein bulls with 312 k SNP markers. Then, the 2513 Holstein cows were imputed from 312 k markers to whole-genome sequence data. This second round of imputation used 179 Holstein animals from the 1000 Bulls Genome Project as a reference set. Three alternative phenotypes were evaluated: (1) total calcium concentration in the first 24 h postpartum, (2) total calcium concentration in the first 72 h postpartum calculated as the area under the curve; and (3) the recovery of total calcium concentration calculated as the difference in total calcium concentration between 72 and 24 h. The identification of genetic variants associated with these traits was performed using a two-step mixed model-based approach implemented in the R package MixABEL. The most significant variants were located within or near genes involved in calcium homeostasis and vitamin D transport (GC), calcium and potassium channels (JPH3 and KCNK13), energy and lipid metabolism (CA5A, PRORP, and SREBP1), and immune response (IL12RB2 and CXCL8), among other functions. This work provides the foundation for the development of novel breeding and management tools for reducing the incidence of periparturient hypocalcemia in dairy cattle.

ABSTRACT: Greater longevity is associated with lower replacement costs, higher average milk production, and fewer replacement heifers needed. Longevity data are obtained late in life, and for this reason stayability, defined as the probability of survival from birth until a certain age, can be used as an alternative measure. The objective of this study was to evaluate the effects of different type traits, inbreeding, and production level on the stayability of Jersey cows to various ages, and to assess trends over time. Data consisted of 460,172 to 204,658 stayability records, depending on length of the opportunity period, for survival from birth until 36, 48, 60, 72, or 84 mo of age. Threshold models were used to analyze the stayability traits, including different type traits, inbreeding coefficient, and within-herd production level as explanatory variables. Heritability estimates for stayability traits ranged from 0.05 (36 mo) to 0.22 (84 mo). As expected, the probability of survival decreased as age increased. Highly productive cows were more likely to survive than their poor-producing contemporaries regardless of age and the type trait evaluated. Our data indicate that farmers' selection decisions tend to punish poor production at early ages and reward high production at later stages. Inbreeding negatively affected the probability of survival, especially when inbreeding coefficients exceeded 10%, and this impact was most noticeable at 48 mo of age or later. Some type traits, such as stature and foot angle, had little effect on the probability of survival. Other type traits, such as strength, dairy form, rump width, and rear legs, showed higher probability of survival at intermediate scores, whereas other type traits, such as fore udder attachment, rear udder height, udder depth, and final score, showed higher probability of survival at higher scores. Finally, our results indicate that the probability of survival has decreased in the last decade, probably due to a greater number of heifers available and, therefore, higher culling rates.

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

The aim of the current study was to determine individual animal variability in rumination, activity, and lying behavior during the periparturient period within the context of dairy cattle nutrition, social, and physical environment. Holstein animals (nulliparous = 77, parous = 219) from one sand-bedded, freestall dairy in northwest Wisconsin were enrolled −17 d in milk (DIM, d 0 = calving), when they were fitted with an automated monitoring device (Hi-Tag, SCR Engineers Ltd.). At −11 DIM, animals were fitted with HOBO Pendant G Data Loggers. The HOBO Pendant G Data Loggers were fitted 6 d later because they were set up to collect data for 22 d (d −11 to 11), to avoid constant handling of the animals that could alter their behavior. Prepartum, nulliparous and parous animals were housed separately. Postpartum (1 to 17 ± 3 DIM), primiparous and multiparous cows were commingled. Samples of the total mixed ration were submitted for wet chemistry analysis and determination of physically effective NDF (peNDF). Temperature and humidity data were collected using RH Temp probes (HOBO Pro Series) installed in each of the pens, and the percentages of 30-min intervals within a day with temperature-humidity index ≥68 (PctTHI68) were calculated. Stocking density (cows per stall) during the pre- and postpartum periods were calculated daily. Prepartum data from nulliparous and parous animals were analyzed separately, and postpartum data from primiparous and multiparous animals were analyzed together. Prepartum, nulliparous and parous animals explained 83.9 and 64.5% of the variability in rumination, 70.7 and 60.9% of the variability in activity, and 38.1 and 63.6% of the variability in lying time, respectively. Postpartum, animal explained 49.7, 56.8, and 35.6% of the variability in rumination, activity, and lying time, respectively. Although stocking density, PctTHI68, peNDF, crude protein, and ether extract were associated with the variability in rumination, activity, and lying time, they explained ≤6.6% of the daily variability in these behaviors. We conclude that, within the conditions of the collaborating commercial herd, individual animal is the most important factor explaining daily variability in rumination, activity, and lying time.

Abstract Maternal nutrition during pregnancy can induce epigenetic alterations in the fetal genome, such as changes in DNA methylation. It remains unclear whether these epigenetic alterations due to changes in maternal nutrition are transitory or persist over time. Here, we hypothesized that maternal methionine supplementation during preconception and early pregnancy could alter the fetal epigenome, and some of these alterations could persist throughout different developmental stages of the offspring. Beef cows were randomly assigned to either a control or a methionine-rich diet from − 30 to + 90 d, relative to the beginning of the breeding season. The methylome of loin muscle from the same bull calves (n = 10 per maternal diet) at 30 and 200 days of age were evaluated using whole-genome bisulfite sequencing. Notably, a total of 28,310 cytosines showed persistent methylation differences over time between maternal diets (q-value 20%). These differentially methylated cytosines were in the transcription start sites, exons, or splice sites of 341 annotated genes. Over-representation analysis revealed that these differentially methylated genes are involved in muscle contraction, DNA and histone methylation, mitochondrial function, reactive oxygen species homeostasis, autophagy, and PI3K signaling pathway, among other functions. In addition, some of the persistently, differentially methylated cytosines were found in CpG islands upstream of genes implicated in mitochondrial activities and immune response. Overall, our study provides evidence that a maternal methionine-rich diet altered fetal epigenome, and some of these epigenetic changes persisted over time.

ABSTRACT: Pregnancy loss is recognized as one of the major factors contributing to poor reproductive performance in dairy cattle. Here, we performed a comprehensive genetic analysis of fetal loss, defined as a pregnancy loss that occurs after detection of a viable embryo around 42 d of gestation. The objectives of this study were to reveal (1) whether fetal loss is heritable and, hence, whether it will respond to selection, and (2) to what extent current fertility traits, such as daughter pregnancy rate, are associated with fetal loss. Data consisted of 59,308 confirmed pregnancy or fetal loss records distributed across nulliparous heifers and primiparous and multiparous cows. We defined fetal loss as a binary trait (yes vs. no) or as an ordinal trait (pregnancy maintenance, early fetal loss ≤150 d of gestation, and late fetal loss >150 d of gestation), and we assessed both linear and threshold models. Heritability estimates for fetal loss ranged from 1 to 18%, depending upon parity, trait definition, and statistical model used. Heritability estimates were greater for lactating cows than for nonlactating nulliparous heifers. Threshold models were able to capture more additive genetic variance and, thus, yielded higher heritability estimates than linear models. Notably, fetal loss traits were highly genetically correlated with each other but only weakly correlated with current fertility traits included in the national genetic evaluation. Overall, our study provides evidence that fetal loss is heritable enough to make genetic selection for reducing fetal loss and improving pregnancy maintenance feasible. In addition, our results suggest that fetal loss is largely independent from current traits used to select for cow fertility, and thus current breeding efforts have unfortunately little effect on reducing the incidence of fetal loss.

Intensive selection for improved productivity has been accompanied by an increase in inbreeding rates and a reduction in genetic diversity. The increase in inbreeding tends to impact performance, especially fitness-related traits such as male fertility. Inbreeding can be monitored using runs of homozygosity (ROH), defined as contiguous lengths of homozygous genotypes observed in an individual’s chromosome. The goal of this study was to evaluate the presence of ROH in Italian Brown Swiss cattle and assess its association with bull fertility. First, we evaluated the association between ROH and male fertility using 1,102 Italian Brown Swiss bulls with sire conception rate records and 572 K SNPs spanning the entire genome. Second, we split the entire population into 100 high-fertility and 100 low-fertility bulls to investigate the potential enrichment of ROH segments in the low-fertility group. Finally, we mapped the significant ROH regions to the bovine genome to identify candidate genes associated with sperm biology and male fertility. Notably, there was a negative association between bull fertility and the amount of homozygosity. Four different ROH regions located in chromosomes 6, 10, 11, and 24 were significantly overrepresented in low-fertility bulls (Fisher’s exact test, p-value

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

Abstract Improving reproductive performance remains a major goal in dairy cattle worldwide. Service sire has been recognized as an important factor affecting herd fertility. The main objective of this study was to reveal the genetic basis of male fertility in Italian Brown Swiss dairy cattle. Dataset included 1102 Italian Brown Swiss bulls with sire conception rate records genotyped with 454k single nucleotide polymorphisms. The analysis included whole-genome scans and gene-set analyses to identify genomic regions, individual genes and genetic mechanisms affecting Brown Swiss bull fertility. One genomic region on BTA1 showed significant additive effects. This region harbors gene RABL3 which is implicated cell proliferation and motility. Two genomic regions, located on BTA6 and BTA26, showed marked non-additive effects. These regions harbor genes, such as WDR19 and ADGRA1, that are directly involved in male fertility, including sperm motility, acrosome reaction, and embryonic development. The gene-set analysis revealed functional terms related to cell adhesion, cellular signaling, cellular transport, immune system, and embryonic development. Remarkably, a gene-set analysis also including Holstein and Jersey data, revealed significant processes that are common to the three dairy breeds, including cell migration, cell–cell interaction, GTPase activity, and the immune function. Overall, this comprehensive study contributes to a better understanding of the genetic basis of male fertility in cattle. In addition, our findings may guide the development of novel genomic strategies for improving service sire fertility in Brown Swiss cattle.

ABSTRACT: The onset of lactation results in a sudden irreversible loss of Ca for colostrum and milk synthesis. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, whereas a larger proportion of cows develop subclinical hypocalcemia that predisposes them to other peripartum diseases. The objective of this study was to perform a comprehensive genomic analysis of blood total Ca concentration in periparturient Holstein cows. We first performed a genomic scan and a subsequent gene-set analysis to identify candidate genes, biological pathways, and molecular mechanisms affecting postpartum Ca concentration. Then, we assessed the prediction of postpartum Ca concentration using genomic information. Data consisted of 7,691 records of plasma or serum concentrations of Ca measured in the first, second, and third day after parturition of 959 primiparous and 1,615 multiparous cows that calved between December 2015 and June 2020 in 2 dairy herds. All cows were genotyped with 80k SNPs. The statistical model included lactation (1 to 5+), calf category (male, females, twins), and day as fixed effects, and season-treatment-experiment, animal, and permanent environmental as random effects. Model predictive ability was evaluated using 10-fold cross-validation. Heritability and repeatability estimates were 0.083 (standard error = 0.017) and 0.444 (standard error = 0.028). The association mapping identified 2 major regions located on Bos taurus autosome (BTA)6 and BTA16 that explained 1.2% and 0.7% of additive genetic variance of Ca concentration, respectively. Interestingly, the region on BTA6 harbors the GC gene, which encodes the vitamin D binding protein, and the region on BTA16 harbors LRRC38, which is actively involved in K transport. Other sizable peaks were identified on BTA5, BTA2, BTA7, BTA14, and BTA9. These regions harbor genes associated with Ca channels (CACNA1S, CRACR2A), K channels (KCNK9), bone remodeling (LRP6), and milk production (SOCS2). The gene-set analysis revealed terms related to vitamin transport, calcium ion transport, calcium ion binding, and calcium signaling. Genomic predictions of phenotypic and genomic estimated breeding values of Ca concentration yielded predictive correlations up to 0.50 and 0.15, respectively. Overall, the present study contributes to a better understanding of the genetic basis of postpartum blood Ca concentration in Holstein cows. In addition, the findings may contribute to the development of novel selection and management strategies for reducing periparturient hypocalcemia in dairy cattle.

Efforts made so far to increase milk production of indigenous cattle by upgrading with exotic genotypes through crossbreeding under smallholder farmer conditions in Ethiopia have resulted in limited improvement. This study was conducted to determine if combining improved feed and management-related trainings with Holstein genetics will synergistically increase the milk production of dairy cows, and to examine the degree to which the increase or improvement is related to the level of exotic genetics involved. A total of 96 smallholder dairy farmers were purposively selected and allocated to one of three treatments reflecting differing degrees of capacity building support: (1) no training and no material support (Control); (2) training provided by another project (PAID) but without material support; (3) enhanced training plus material support (Feed-Mgt). Sixteen extension development agents and eight livestock experts were selected to provide training and weekly on-farm data collection, monitoring, technical support, and messaging reinforcement visits. Enhanced training addressed a broad range of topics related to feed and feeding, improved forage production, milk production and handling, construction and use of improved dairy housing, disease control, and dairy farm record keeping. Material support was in the form of formulated dairy concentrate feed and improved forage planting materials (seeds/seedlings). Data collection included variables describing animal performance, management practice adoption, and milk and butter quality. Tail hair samples for genetic profiling of breed makeup were also collected from all cows in the study. Results showed that there was a very significant effect of treatment (P < 0.001) on milk production, as well as treatment by week interaction (P = 0.034), with the effect of treatment markedly increasing over time (P = 0.032). That is, cows in Feed-Mgt group had up to 26.6% greater milk production compared to those in the Control group. Region and region by treatment interaction have no effects. Cows used in the experiment had varying proportions of Holstein-Friesian genome that ranged between 0 and 100%, suggesting prevalence of indiscriminate crossbreeding. Cows with a high proportion of Holstein genetics in treatment 3 produced more milk as compared to those in control group. Besides, supplemental feed improved body condition, reduced number of services per conception and resulted in higher lactation length of cows on treatment 3 compared with those on treatments 1 and 2 (P < 0.001). Overall, our findings suggest that an integrated approach of improved feed, feeding practices, and management training for smallholder dairy that goes beyond the improvement of genetics will increase milk production, improve milk quality and body condition, and resulted in higher length of lactation.

Abstract Background The evaluation of alternative splicing, including differential isoform expression and differential exon usage, can provide some insights on the transcriptional changes that occur in response to environmental perturbations. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to assess potential changes in splicing events in the longissimus dorsi muscle of beef calves gestated under control or methionine-rich diets. RNA sequencing and whole-genome bisulfite sequencing were used to evaluate muscle transcriptome and methylome, respectively. Results Alternative splicing patterns were significantly altered by maternal methionine supplementation. Most of the altered genes were directly implicated in muscle development, muscle physiology, ATP activities, RNA splicing and DNA methylation, among other functions. Interestingly, there was a significant association between DNA methylation and differential exon usage. Indeed, among the set of genes that showed differential exon usage, significant differences in methylation level were detected between significant and non-significant exons, and between contiguous and non-contiguous introns to significant exons. Conclusions Overall, our findings provide evidence that a prenatal diet rich in methyl donors can significantly alter the offspring transcriptome, including changes in isoform expression and exon usage, and some of these changes are mediated by changes in DNA methylation.

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

Abstract Pregnancy loss directly impairs reproductive performance in dairy cattle. Here, we investigated genetic factors associated with pregnancy loss following detection of a viable embryo around 42 days of gestation. The objectives of this study were to perform whole-genome scans and subsequent gene-set analyses for identifying candidate genes, functional gene-sets and gene signaling pathways implicated in pregnancy loss in US Holstein cows. Data consisted of about 58,000 pregnancy/abortion records distributed over nulliparous, primiparous, and multiparous cows. Threshold models were used to assess the binary response of pregnancy loss. Whole‐genome scans identified at least seven genomic regions on BTA2, BTA10, BTA14, BTA16, BTA21, BTA24 and BTA29 associated with pregnancy loss in heifers and lactating cows. These regions harbor several candidate genes that are directly implicated in pregnancy maintenance and fetal growth, such as CHST14, IGF1R, IGF2, PSEN2, SLC2A5 and WNT4. Moreover, the enrichment analysis revealed at least seven significantly enriched processes, containing genes associated with pregnancy loss, including calcium signaling, cell–cell attachment, cellular proliferation, fetal development, immunity, membrane permeability, and steroid metabolism. Additionally, the pathway analysis revealed a number of significant gene signaling pathways that regulate placental development and fetal growth, including Wnt, Hedgehog, Notch, MAPK, Hippo, mTOR and TGFβ pathways. Overall, our findings contribute to a better understanding of the genetic and biological basis of pregnancy loss in dairy cattle and points out novel strategies for improving pregnancy maintenance via marker‐assisted breeding.

Abstract In mammals, peripheral serotonin is involved in regulating energy balance. Herein, we characterized the transcriptomic profile and microstructure of adipose and muscle in pre-weaned calves with increased circulating serotonin. Holstein bull calves (21 ± 2 days old) were fed milk replacer supplemented with saline (CON, 8 mL/day n = 4) or 5-hydroxytryptophan (5-HTP, 90 mg/day, n = 4) for 10 consecutive days. Calves were euthanized on d10 to harvest adipose and muscle for RNA-Sequencing and histological analyses. Twenty-two genes were differentially expressed in adipose, and 33 in muscle. Notably, Interferon gamma inducible protein-47 was highly expressed and upregulated in muscle and adipose (avg. log FC = 6.5). Enriched pathways in adipose tissue revealed serotonin’s participation in lipid metabolism and PPAR signaling. In muscle, enriched pathways were related to histone acetyltransferase binding, Jak-STAT signaling, PI3K-Akt signaling and cell proliferation. Supplementation of 5-HTP increased cell proliferation and total cell number in adipose and muscle. Adipocyte surface area was smaller and muscle fiber area was not different in the 5-HTP group. Manipulating the serotonin pathway, through oral supplementation of 5-HTP, influences signaling pathways and cellular processes in adipose and muscle related to endocrine and metabolic functions which might translate into improvements in calf growth and development.

Abstract Background Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV). Results Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV. Conclusions Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

Several ocular pathologies in cattle, such as ocular squamous cell carcinoma and infectious keratoconjunctivitis, have been associated with low pigmentation of the eyelids. The main objective of this study was to analyze the transcriptome of eyelid skin in Hereford cattle using strand-specific RNA sequencing technology to characterize and identify long noncoding RNAs (lncRNAs). We compared the expression of lncRNAs between pigmented and unpigmented eyelids and analyzed the interaction of lncRNAs and putative target genes to reveal the genetic basis underlying eyelid pigmentation in cattle. We predicted 4,937 putative lncRNAs mapped to the bovine reference genome, enriching the catalog of lncRNAs in Bos taurus. We found 27 differentially expressed lncRNAs between pigmented and unpigmented eyelids, suggesting their involvement in eyelid pigmentation. In addition, we revealed potential links between some significant differentially expressed lncRNAs and target mRNAs involved in the immune response and pigmentation. Overall, this study expands the catalog of lncRNAs in cattle and contributes to a better understanding of the biology of eyelid pigmentation.

Genetic selection has been an effective strategy to improve calving traits including stillbirth in dairy cattle. The primary objectives of the present study were to characterize stillbirth data and determine the feasibility of implementing routine genetic evaluations of stillbirth in five non-Holstein dairy breeds, namely Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey. An updated sire-maternal grandsire threshold model was used to estimate genetic parameters and genetic values for stillbirth. Stillbirth data with the birth years of dams from 1995 to 2018 were extracted from the United States national calving ease database maintained by the Council on Dairy Cattle Breeding. The extracted stillbirth records varied drastically among the five dairy breeds. There were approximately 486K stillbirth records for Jersey and more than 80K stillbirth records for Brown Swiss. The direct and maternal heritability estimates of stillbirth were 6.0% (4.5–7.6%) and 4.7% (3.3–6.1%) in Jersey and 6.8% (3.2–10.5%) and 1.1% (0.6–2.9%) in Brown Swiss. The estimated genetic correlations between direct and maternal genetic effects for stillbirth were −0.15 (−0.38 to −0.08) in Jersey and −0.35 (−0.47 to −0.12) in Brown Swiss. The estimated genetic parameters for stillbirth in these two breeds were within close ranges of previous studies. The reliabilities of predicted transmitting abilities in Jersey and Brown Swiss increased substantially, thanks to the substantial increase in available stillbirth data in the past 10 years. The stillbirth records for Ayrshire, Guernsey, and Milking Shorthorn, which ranged approximately between 3K and 12K, are insufficient to implement reliable routine genetic evaluations of stillbirth in these three dairy breeds. Estimated genetic (co)variances and genetic values deviated considerably from the reported ranges of previous studies, and the reliabilities of predicted transmitting abilities were low in these three breeds. In conclusion, routine genetic evaluations of stillbirth are feasible in Brown Swiss and Jersey. However, reliable genetic evaluations of stillbirth in Ayrshire, Guernsey, and Milking Shorthorn require further data collection on stillbirth.

Abstract Background Coexpression network analysis is a powerful tool to reveal transcriptional regulatory mechanisms, identify transcription factors, and discover gene functions. It can also be used to investigate changes in coexpression patterns in response to environmental insults or changes in experimental conditions. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to investigate structural changes in gene coexpression networks in the muscle of bull beef calves gestated under diets with or without methionine supplementation. Both muscle transcriptome and methylome were evaluated using next generation sequencing. Results Maternal methionine supplementation significantly perturbed coexpression patterns in the offspring’s muscle. Indeed, we found that neither the connection strength nor the connectivity pattern of six modules (subnetworks) detected in the control diet were preserved in the methionine-rich diet. Functional characterization revealed that some of the unpreserved modules are implicated in myogenesis, adipogenesis, fibrogenesis, canonical Wnt/β-catenin pathway, ribosome structure, rRNA binding and processing, mitochondrial activities, ATP synthesis and NAD(P) H oxidoreductases, among other functions. The bisulfite sequencing analysis showed that nearly 2% of all evaluated cytosines were differentially methylated between maternal diets. Interestingly, there were significant differences in the levels of gene body DNA methylation between preserved and unpreserved modules. Conclusions Overall, our findings provide evidence that maternal nutrition can significantly alter gene coexpression patterns in the offspring, and some of these perturbations are mediated by changes in DNA methylation.

Abstract Background Achieving rapid genetic progress while maintaining adequate genetic diversity is one of the main challenges facing the dairy industry. The increase in inbreeding can be used to monitor the loss of genetic diversity. Inbreeding tends to increase the proportion of homozygous loci, some of which cause homozygosity of recessive alleles that results in reduced performance. This phenomenon is known as inbreeding depression and tends to be most prominent on fitness-related traits, such as male fertility. Traditionally, inbreeding has been monitored using pedigree information, or more recently, genomic data. Alternatively, it can be quantified using runs of homozygosity (ROH), i.e., contiguous lengths of homozygous genotypes observed in an individual’s chromosome. Results The objective of this study was to evaluate the association between ROH and sire conception rate. ROH were evaluated using 268 k genetic markers in 11,790 US Holstein bulls. Interestingly, either the sum, mean, or maximum length of ROH were negatively associated with bull fertility. The association analysis between ROH and sire fertility was performed comparing 300 high-fertility vs. 300 low-fertility bulls. Both the average and sum of ROH length were higher in the low-fertility group. The enrichment of ROH regions in bulls with low fertility was assessed using a Fisher’s exact test. Nine regions were significantly enriched in low-fertility compared to high-fertility bulls. Notably, these regions harbor genes that are closely related to sperm biology and male fertility, including genes exclusively or highly expressed in testis. Conclusions The results of this study can help not only to manage inbreeding in genomic selection programs by designing custom mating schemes, but also to better understand the mechanisms underlying male fertility in dairy cattle.

Abstract Background Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine-learning, including interest in deep learning algorithms such as multilayer perceptrons (MLP) and convolutional neural networks (CNN). The aim of this study was to compare the predictive performance of two deep learning methods (MLP and CNN), two ensemble learning methods [random forests (RF) and gradient boosting (GB)], and two parametric methods [genomic best linear unbiased prediction (GBLUP) and Bayes B] using real and simulated datasets. Methods The real dataset consisted of 11,790 Holstein bulls with sire conception rate (SCR) records and genotyped for 58k single nucleotide polymorphisms (SNPs). To support the evaluation of deep learning methods, various simulation studies were conducted using the observed genotype data as template, assuming a heritability of 0.30 with either additive or non-additive gene effects, and two different numbers of quantitative trait nucleotides (100 and 1000). Results In the bull dataset, the best predictive correlation was obtained with GB (0.36), followed by Bayes B (0.34), GBLUP (0.33), RF (0.32), CNN (0.29) and MLP (0.26). The same trend was observed when using mean squared error of prediction. The simulation indicated that when gene action was purely additive, parametric methods outperformed other methods. When the gene action was a combination of additive, dominance and of two-locus epistasis, the best predictive ability was obtained with gradient boosting, and the superiority of deep learning over the parametric methods depended on the number of loci controlling the trait and on sample size. In fact, with a large dataset including 80k individuals, the predictive performance of deep learning methods was similar or slightly better than that of parametric methods for traits with non-additive gene action. Conclusions For prediction of traits with non-additive gene action, gradient boosting was a robust method. Deep learning approaches were not better for genomic prediction unless non-additive variance was sizable.

Abstract Background Reproductive capacity can be altered by challenges experienced during critical periods of development, including fetal development and early neonatal life. Gossypol is a polyphenolic compound, commonly found in cotton seeds, that impairs male reproduction. Here, we investigated whether the exposure to gossypol in utero and during lactation alters male reproductive function in sheep. From conception until 60 days postpartum, ewes were randomly assigned to a control diet or a gossypol-rich diet based on cottonseed. Lamb testicles were removed at 60 days of age and subjected to RNA-sequencing. Results Lambs derived from the maternal cottonseed diet showed significantly lower growth and lower testis weight as a proportion of the total body weight, and reduced testosterone levels. In addition, the testis transcriptome was significantly altered by the maternal cottonseed diet. Most of the altered genes are directly implicated in testis development and sperm biology, cell communication, iron ion metabolism, calcium homeostasis and signaling, among other functions. Interestingly, network analysis revealed that exposure to gossypol significantly disturbed coexpression patterns among spermatogenesis-related genes, suggesting a disruption in coregulation mechanisms. Conclusions Our findings provide evidence that maternal exposure to gossypol alters male reproductive function in the offspring, with potential lasting or lifelong negative consequences.

Serotonin is a key regulator of mammary gland homeostasis during lactation. Selective serotonin reuptake inhibitors (SSRIs) are commonly used to treat peripartum depression, but also modulates mammary gland serotonin concentrations and signaling in part through DNA methylation. The objective of this study was to determine mouse mammary transcriptome changes in response to the SSRI fluoxetine and how methyl donor supplementation, achieved by folic acid supplementation, affected the transcriptome. Female C57BL/6J mice were fed either breeder diet (containing 4 mg/kg folic acid) or supplemented diet (containing 24 mg/kg folic acid) beginning 2 weeks prior to mating, then on embryonic day 13 mice were injected daily with either saline or 20 mg/kg fluoxetine. Mammary glands were harvested at peak lactation, lactation day 10, for transcriptomic analysis. Fluoxetine but not folic acid altered circulating serotonin and calcium concentrations, and folic acid reduced mammary serotonin concentrations, however only fluoxetine altered genes in the mammary transcriptome. Fluoxetine treatment altered fifty-six genes. Elovl6 was the most significantly altered gene by fluoxetine treatment along with gene pathways involving fatty acid homeostasis, PPARγ, and adipogenesis, which are critical for milk fat synthesis. Enriched pathways in the mammary gland by fluoxetine revealed pathways including calcium signaling, serotonin receptors, milk proteins, and cellular response to cytokine stimulus which are important for lactation. Although folic acid did not impact specific genes, a less stringent pathway analysis revealed more diffuse effects where folic acid enriched pathways involving negative regulation of gene expression as expected, but additionally enriched pathways involving serotonin, glycolysis, and lactalbumin which are critical for lactation. In conclusion, peripartal SSRI use and folic acid supplementation altered critical genes related to milk synthesis and mammary gland function that are important to a successful lactation. However, folic acid supplementation did not reverse changes in the mammary gland transcriptome altered by peripartal SSRI treatment.

Visceral fat is related to important metabolic processes, including insulin sensitivity and lipid mobilization. The goal of this study was to identify individual genes, pathways, and molecular processes implicated in visceral fat deposition in dairy cows. Data from 172 genotyped Holstein cows classified at slaughterhouse as having low (n = 77; omental fold

Abstract Background Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of both single-kernel and multi-kernel predictive models fitting either all SNPs, markers with large effect, or markers with presumed functional roles, such as non-synonymous, synonymous, or non-coding regulatory variants. Results The entire set of SNPs yielded predictive correlations of 0.340. Five markers located on chromosomes BTA8, BTA9, BTA13, BTA17, and BTA27 showed marked dominance effects. Interestingly, the inclusion of these five major markers as fixed effects in the predictive models increased predictive correlations to 0.403, representing an increase in accuracy of about 19% compared with the standard model. Single-kernel models fitting functional SNP classes outperformed their counterparts using random sets of SNPs, suggesting that the predictive power of these functional variants is driven in part by their biological roles. Multi-kernel models fitting all the functional SNP classes together with the five major markers exhibited predictive correlations around 0.405. Conclusions The inclusion of markers with large effect markedly improved the prediction of dairy sire fertility. Functional variants exhibited higher predictive ability than random variants, but did not outperform the standard whole-genome approach. This research is the foundation for the development of novel strategies that could help the dairy industry make accurate genome-guided selection decisions on service sire fertility.

Maternal nutrition during gestation can cause epigenetic effects that translate to alterations in gene expression in offspring. This 2-year study employed RNA-sequencing technology to evaluate the pre- and post-vaccination muscle transcriptome of early-weaned Bos indicus-influenced beef calves born from dams offered different supplementation strategies from 57 ± 5 d prepartum until 17 ± 5 d postpartum. Seventy-two Brangus heifers (36 heifers/yr) were stratified by body weight and body condition score and assigned to bahiagrass pastures (3 heifers/pasture/yr). Treatments were randomly assigned to pastures and consisted of (i) no pre- or postpartum supplementation (NOSUP), (ii) pre- and postpartum supplementation of protein and energy using 7.2 kg of dry matter/heifer/wk of molasses + urea (MOL), or (iii) MOL fortified with 105 g/heifer/wk of methionine hydroxy analog (MOLMET). Calves were weaned on d 147 of the study. On d 154, 24 calves/yr (8 calves/treatment) were randomly selected and individually limit-fed a high-concentrate diet until d 201. Calves were vaccinated on d 160. Muscle biopsies were collected from the same calves (4 calves/treatment/day/yr) on d 154 (pre-vaccination) and 201 (post-vaccination) for gene expression analysis using RNA sequencing. Molasses maternal supplementation led to a downregulation of genes associated with muscle cell differentiation and development along with intracellular signaling pathways (e.g., Wnt and TGF-β signaling pathway) compared to no maternal supplementation. Maternal fortification with methionine altered functional gene-sets involved in amino acid transport and metabolism and the one-carbon cycle. In addition, muscle transcriptome was impacted by vaccination with a total of 2,396 differentially expressed genes (FDR ≤ 0.05) on d 201 vs. d 154. Genes involved in cell cycle progression, extracellular matrix, and collagen formation were upregulated after vaccination. This study demonstrated that maternal supplementation of energy and protein, with or without, methionine has long-term implications on the muscle transcriptome of offspring and potentially influence postnatal muscle development.

Tenderness is a major quality attribute for fresh beef steaks in the United States, and meat quality traits in general are suitable candidates for genomic research. The objectives of the present analysis were to (1) perform genome-wide association (GWA) analysis for marbling, Warner-Bratzler shear force (WBSF), tenderness, and connective tissue using whole-genome data in an Angus population, (2) identify enriched pathways in each GWA analysis; (3) construct a protein–protein interaction network using the associated genes and (4) perform a μ-calpain proteolysis assessment for associated structural proteins. An Angus-sired population of 2,285 individuals was assessed. Animals were transported to a commercial packing plant and harvested at an average age of 457 ± 46 days. After 48 h postmortem, marbling was recorded by graders’ visual appraisal. Two 2.54-cm steaks were sampled from each muscle for recording of WBSF, and tenderness, and connective tissue by a sensory panel. The relevance of additive effects on marbling, WBSF, tenderness, and connective tissue was evaluated on a genome-wide scale using a two-step mixed model-based approach in single-trait analysis. A tissue-restricted gene enrichment was performed for each GWA where all polymorphisms with an association p-value lower than 1 × 10–3 were included. The genes identified as associated were included in a protein–protein interaction network and a candidate structural protein assessment of proteolysis analyses. A total of 1,867, 3,181, 3,926, and 3,678 polymorphisms were significantly associated with marbling, WBSF, tenderness, and connective tissue, respectively. The associate region on BTA29 (36,432,655–44,313,046 bp) harbors 13 highly significant markers for meat quality traits. Enrichment for the GO term GO:0005634 (Nucleus), which includes transcription factors, was evident. The final protein–protein network included 431 interations between 349 genes. The 42 most important genes based on significance that encode structural proteins were included in a proteolysis analysis, and 81% of these proteins were potential μ-Calpain substrates. Overall, this comprehensive study unraveled genetic variants, genes and mechanisms of action responsible for the variation in meat quality traits. Our findings can provide opportunities for improving meat quality in beef cattle via marker-assisted selection.

Abstract Background Service sire has a considerable impact on reproductive success in dairy cattle. Most gene mapping studies for bull fertility have focused on additive effects, while non-additive effects have been largely ignored. The main goal of this study was to assess the relevance of non-additive effects on Sire Conception Rate (SCR) in Holstein dairy cattle. The analysis included 7.5 k Holstein bulls with both SCR records and 57.8 k single nucleotide polymorphism (SNP) markers spanning the entire genome. Results The importance of non-additive effects was evaluated using an efficient two-step mixed model-based approach. Four genomic regions located on chromosomes BTA8, BTA9, BTA13 and BTA17 showed marked dominance and/or recessive effects. Most of these regions harbor genes, such as ADAM28, DNAJA1, TBC1D20, SPO11, PIWIL3 and TMEM119, that are directly implicated in testis development, male germ line maintenance, and sperm maturation. Conclusions This study provides further evidence for the relevance of non-additive effects in fitness-related traits, such as male fertility. In addition, these findings may point out new strategies for improving service sire fertility in dairy cattle via marker-assisted selection.

Abstract Background Numerous studies have established a link between maternal diet and the physiological and metabolic phenotypes of their offspring. In previous studies in sheep, we demonstrated that different maternal diets altered the transcriptome of fetal tissues. However, the mechanisms underlying transcriptomic changes are poorly understood. DNA methylation is an epigenetic mark regulating transcription and is largely influenced by dietary components of the one-carbon cycle that generate the methyl group donor, SAM. Therefore, in the present study, we tested whether different maternal diets during pregnancy would alter the DNA methylation and gene expression patterns in fetal tissues. Results Pregnant ewes were randomly divided into two groups which received either hay or corn diet from mid-gestation (day 67 ± 5) until day 131 ± 1 when fetuses were collected by necropsy. A total of 1516 fetal longissimus dorsi (LD) tissues were used for DNA methylation analysis and gene expression profiling. Whole genome DNA methylation using methyl-binding domain enrichment analysis revealed 60 differentially methylated regions (DMRs) between hay and corn fetuses with 39 DMRs more highly methylated in the hay fetuses vs. 21 DMRs more highly methylated in the corn fetuses. Three DMRs (LPAR3, PLIN5-PLIN4, and the differential methylation of a novel lincRNA) were validated using bisulfite sequencing. These DMRs were associated with differential gene expression. Additionally, significant DNA methylation differences were found at the single CpG level. Integrative methylome and transcriptome analysis revealed an association between gene expression and inter−/intragenic methylated regions. Furthermore, intragenic DMRs were found to be associated with expression of neighboring genes. Conclusions The findings of this study imply that maternal diet from mid- to late-gestation can shape the epigenome and the transcriptome of fetal tissues, and putatively affect phenotypes of the lambs.

In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for k-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased.

Heat stress represents a major environmental factor that negatively affects the health and performance of dairy cows, causing huge economic losses to the dairy industry. Identifying and selecting animals that are thermotolerant is an attractive alternative for reducing the negative effects of heat stress on dairy cattle performance. As such, the objectives of the present study were to estimate genetic components of milk yield, fat yield, and protein yield considering heat stress and to perform whole-genome scans and a subsequent gene-set analysis for identifying candidate genes and functional gene-sets implicated in milk production under heat stress conditions. Data consisted of about 254k test-day records from 17,522 Holstein cows. Multi-trait repeatability test day models with random regressions on a function of temperature-humidity index (THI) values were used for genetic analyses. The models included herd-test-day and DIM classes as fixed effects, and general and thermotolerance additive genetic and permanent environmental as random effects. Notably, thermotolerance additive genetic variances for all milk traits increased across parities suggesting that cows become more sensitive to heat stress as they age. In addition, our study revealed negative genetic correlations between general and thermotolerance additive effects, ranging between −0.18 to −0.68 indicating that high producing cows are more susceptible to heat stress. The association analysis identified at least three different genomic regions on BTA5, BTA14, and BTA15 strongly associated with milk production under heat stress conditions. These regions harbor candidate genes, such as HSF1, MAPK8IP1, and CDKN1B that are directly involved in the cellular response to heat stress. Moreover, the gene-set analysis revealed several functional terms related to heat shock proteins, apoptosis, immune response, and oxidative stress, among others. Overall, the genes and pathways identified in this study provide a better understanding of the genetic architecture underlying dairy cow performance under heat stress conditions. Our findings point out novel opportunities for improving thermotolerance in dairy cattle through marker-assisted breeding.

An economic selection index combines multiple traits into a single value, facilitating the identification of the best animals. This new 3-page document discusses economic selection indices, their changes in the US, and a 2018 update. Written by Francisco Peñagaricano, and published by the UF/IFAS Department of Animal Sciences, March 2019. http://edis.ifas.ufl.edu/an353

Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO), and more recently by Medical Subject Headings (MeSH). Here, we report a suite of MeSH packages for chicken in Bioconductor, and illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published datasets that were employed represent (i) differentially expressed genes, and (ii) candidate genes under selective sweep or epistatic selection. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis, but also that MeSH is able to further enrich the representation of biological knowledge and often provide more interpretable results. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies, as well as semantic similarities among selected genes. These yielded the similarity levels between significant functional terms, and the annotation of each gene yielded the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits.

Structural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2) to perform whole-genome scans for these latent variables in order to identify genomic regions and individual genes with both direct and indirect effects. A total of 726 steers from an Angus-Brahman multibreed population with records for 22 phenotypes were used. A total of 480 animals were genotyped with the GGP Bovine F-250. The single-step genomic best linear unbiased prediction method was used to estimate the amount of genetic variance explained for each latent variable by chromosome regions of 20 adjacent SNP-windows across the genome. Three types of genetic effects were considered: (1) direct effects on a single latent phenotype; (2) direct effects on two latent phenotypes simultaneously; and (3) indirect effects. The final structural model included carcass quality as an independent latent variable and meat quality as a dependent latent variable. Carcass quality was defined by quality grade, fat over the ribeye and marbling, while the meat quality was described by juiciness, tenderness and connective tissue, all of them measured through a taste panel. From 571 associated genomic regions (643 genes), each one explaining at least 0.05% of the additive variance, 159 regions (179 genes) were associated with carcass quality, 106 regions (114 genes) were associated with both carcass and meat quality, 242 regions (266 genes) were associated with meat quality, and 64 regions (84 genes) were associated with carcass quality, having an indirect effect on meat quality. Three biological mechanisms emerged from these findings: postmortem proteolysis of structural proteins and cellular compartmentalization, cellular proliferation and differentiation of adipocytes, and fat deposition.

Milk fever is an important metabolic disorder that affects dairy cows around parturition. It is associated with a breakdown in the mechanisms of calcium homeostasis, resulting in very low blood calcium levels (hypocalcemia). The main objective of this study was to dissect the genetic basis underlying milk fever incidence in Holstein cattle. Data consisted of 31.6 k producer-recorded lactation incidence records from 15.3 k cows. The analysis included a whole-genome scan and a subsequent gene-set analysis in order to reveal individual genes, genetic mechanisms and biological pathways implicated in the incidence of periparturient hypocalcemia. The association analysis identified at least eight different genomic regions that explain considerable amounts of additive genetic variance for milk fever incidence. Notably, some of these regions harbor genes, such as CYP27A1, CYP2J2, GC, SNAI2, and PIM1, that are directly involved in vitamin D metabolic pathway. Moreover, the gene-set analysis revealed several functional terms, such as calcium ion binding, calcium ion transportation, T cell differentiation, B cell activation, protein phosphorylation, apoptosis, and protein kinase activity, among others, that could be implicated in the development of periparturient hypocalcemia. Overall, this comprehensive study contributes to a better understanding of the genetic control of this complex disease. In addition, these findings may contribute to the development of novel breeding strategies for reducing the incidence of milk fever in dairy cattle.

Genomic selection refers to selection decisions based on genomic-estimated breeding values. These genomic breeding values are calculated using genetic markers across the entire genome. This technology has revolutionized dairy cattle breeding globally. This new 4-page fact sheet discusses the effects of genomics on dairy sire selection. Written by Francisco Peñagaricano, and published by the UF/IFAS Department of Animal Sciences, February 2018. http://edis.ifas.ufl.edu/an340

Sire selection is one of the most important decisions that a dairy producer makes. It represents a great opportunity to improve the profitability of the dairy production enterprise. This new 3-page fact sheet reviews some key concepts that should be considered in order to make proper selection decisions and discusses alternative methods for selecting sires based on multiple traits. Written by Francisco Peñagaricano, and published by the UF/IFAS Department of Animal Sciences, January 2018. http://edis.ifas.ufl.edu/an337

Transcriptome deep sequencing is a powerful tool for exploring the genetic architecture of complex traits. Gene expression patterns may explain a high degree of the observed phenotypic differences in histochemical and metabolic parameters related to meat quality among different muscles. In this study, we sequenced by RNA-Seq the whole transcriptome of nine lamb muscles: Semimembranosus (SM), Semitendinosus (ST), Cranial gluteobiceps, Gluteus medius (GM), Rectus femoris, Supraspinatus (SS), Longissimus lumborum (LL), Adductor and Psoas major. Significant gene expression differences were detected between almost all pairwise comparisons, being more pronounced between SS and ST, SM and LL, and ST and GM. These differences can be explained in terms of ATPase and glycolytic activities, muscle fiber typing and oxidative score, clustering muscles as fast glycolytic, intermediate or slow oxidative. ST showed up-regulation of gene pathways related to carbohydrate metabolism, energy generation and protein turnover as expected from a fast white muscle. SS showed myosin isoforms typical of slow muscles and high expression of genes related to calcium homeostasis and vascularization. SM, LL and GM showed in general intermediate gene expression patterns. Several novel transcripts were detected, mostly related to muscle contraction and structure, oxidative metabolism, lipid metabolism and protein phosphorylation. Expression profiles were consistent with previous histochemical and metabolic characterization of these muscles. Up-regulation of ion transport genes may account for significant differences in water holding capacity. High expression of genes related to cell adhesion, cytoskeleton organization, extracellular matrix components and protein phosphorylation may be related to meat yellowness and lower tenderness scores. Differential expression of genes related to glycolytic activity and lactic acid generation among fast, intermediate and slow muscles may explain the detected final meat pH differences. These results reveal new candidate genes associated with lamb meat quality, and give a deeper insight into the genetic architecture of these complex traits.

Serotonin regulates numerous processes in the mammary gland. Our objective was to discover novel genes, pathways and functions which serotonin modulates during lactation. The rate limiting enzyme in the synthesis of non-neuronal serotonin is tryptophan-hydroxylase (TPH1). Therefore, we used TPH1 deficient dams (KO; serotonin deficient, n = 4) and compared them to wild-type (WT; n = 4) and rescue (RC; KO + 100 mg/kg 5-hydroxytryptophan injected daily, n = 4) dams. Mammary tissues were collected on day 10 of lactation. Total RNA extraction, amplification, library preparation and sequencing were performed following the Illumina mRNA-Seq. Overall, 97 and 204 genes (false discovery rate, FDR ≤ 0.01) exhibited a minimum of a 2-fold expression difference between WT vs. KO and WT vs. RC dams, respectively. Most differentially expressed genes were related to calcium homeostasis, apoptosis regulation, cell cycle, cell differentiation and proliferation, and the immune response. Additionally, gene set enrichment analysis using Gene Ontology and Medical Subject Headings databases revealed the alteration of several biological processes (FDR ≤ 0.01) including fat cell differentiation and lipid metabolism, regulation of extracellular signal-related kinase and mitogen-activated kinase cascades, insulin resistance, nuclear transport, membrane potential regulation, and calcium release from the endoplasmic reticulum into the cytosol. The majority of the biological processes and pathways altered in the KO dams are central for mammary gland homeostasis. Increasing peripheral serotonin in the RC dams affects specific pathways that favor lactation. Our data confirms the importance of serotonin during lactation in the mammary gland.

Imprinted genes have been implicated in early embryonic, placental, and neonatal development and alterations in expression levels of these genes can lead to growth abnormalities and embryonic lethality. However, little is known about the functions of bovine imprinted genes during the pre-implantation period. Therefore, the objective of this study was to assess the influence of altered expression of imprinted genes on developmental progress of embryos using small interfering RNA (siRNA). Expression levels of 18 imprinted genes (MAGEL2, UBE3A, IGF2R, NAP1L5, TSSC4, PEG3, NDN, CDKN1C, PHLDA2, MKRN3, USP29, NNAT, PEG10, RTL1, IGF2, H19, MIM1, and XIST) were compared between embryos reaching the blastocyst stage and growth-arrested embryos (degenerates) using quantitative real-time PCR (qRT-PCR). Ten genes were found to be differentially expressed between blastocysts and degenerates. The CDKN1C gene showed the highest upregulation in blastocysts whereas PHLDA2 was highly expressed in degenerates. To assess whether the observed differential gene expression was causative or resultant of embryo degeneration, these genes were selected for functional analysis using siRNA. Injection of siRNA specific to PHLDA2 into one-cell zygotes resulted in a substantial increase in blastocyst development, whereas injection of CDKN1C-specific siRNA resulted in a 45% reduction (P = 0.0006) in blastocyst development. RNA-Seq analysis of CDKN1C-siRNA-injected vs. non-injected embryos revealed 51 differentially expressed genes with functions in apoptosis, lipid metabolism, differentiation, and cell cycle regulation. Gene ontology analysis revealed nine pathways related to cell signaling, metabolism, and nucleic acid processing. Overall, our results show that proper expression levels of the imprinted genes CDKN1C and PHLDA2 are critical for embryo development, which suggests that these genes can be used as markers for normal blastocyst formation.

Maternal nutrition exclusively during the periconceptional period can induce remarkable effects on both oocyte maturation and early embryo development, which in turn can have lifelong consequences. The objective of this study was to evaluate the effect of maternal methionine supplementation on the transcriptome of bovine preimplantation embryos. Holstein cows were randomly assigned to one of two treatments differing in level of dietary methionine (1.89 Met vs. 2.43 Met % of metabolizable protein) from calving until embryo flushing. High quality preimplantation embryos from individual cows were pooled and then analyzed by RNA sequencing. Remarkably, a subtle difference in methionine supplementation in maternal diet was sufficient to cause significant changes in the transcriptome of the embryos. A total of 276 genes out of 10,662 showed differential expression between treatments (FDR