Associations among the genome, rumen metabolome, ruminal bacteria, and milk production in early-lactation Holsteins

A multicenter observational study to evaluate genome-wide association was conducted in early-lactation Holstein cows (n = 293) from 36 herds in Canada, the USA, and Australia. Phenotypic observations included rumen metabolome, acidosis risk, ruminal bacterial taxa, and milk composition and yield measures. Diets ranged from pasture supplemented with concentrates to total mixed rations (nonfiber carbohydrates = 17 to 47, and neutral detergent fiber = 27 to 58% of dry matter). Rumen samples were collected <3 h after feeding and analyzed for pH, ammonia, d- and l-lactate, volatile fatty acid (VFA) concentrations, and abundance of bacterial phyla and families. Eigenvectors were produced using cluster and discriminant analyses from a combination of pH and ammonia, d-lactate, and VFA concentrations, and were used to estimate the probability of the risk of ruminal acidosis based on proximity to the centroid of 3 clusters, termed high (24.0% of cows), medium (24.2%), and low risk (51.8%) for acidosis. DNA of sufficient quality was successfully extracted from whole blood (218 cows) or hair (65 cows) collected simultaneously with the rumen samples and sequenced using the Geneseek Genomic Profiler Bovine 150K Illumina SNPchip. Genome-wide association used an additive model and linear regression with principal component analysis (PCA) population stratification and a Bonferroni correction for multiple comparisons. Population structure was visualized using PCA plots. Single genomic markers were associated with milk protein percent and the center logged ratio abundance of the phyla Chloroflexi, SR1, and Spirochaetes, and tended to be associated with milk fat yield, rumen acetate, butyrate, and isovalerate concentrations and with the probability of being in the low-risk acidosis group. More than one genomic marker was associated or tended to be associated with rumen isobutyrate and caproate concentrations, and the center log ratio of the phyla Bacteroidetes and Firmicutes and center log ratio of the families Prevotellaceae, BS11, S24-7, Acidaminococcaceae, Carnobacteriaceae, Lactobacillaceae, Leuconostocaceae, and Streptococcaceae. The provisional NTN4 gene, involved in several functions, had pleiotropy with 10 bacterial families, the phyla Bacteroidetes and Firmicutes, and butyrate. The ATP2CA1 gene, involved in the ATPase secretory pathway for Ca²⁺ transport, overlapped for the families Prevotellaceae, S24-7, and Streptococcaceae, the phylum Bacteroidetes, and isobutyrate. No genomic markers were associated with milk yield, fat percentage, protein yield, total solids, energy-corrected milk, somatic cell count, rumen pH, ammonia, propionate, valerate, total VFA, and d-, l-, or total lactate concentrations, or probability of being in the high- or medium-risk acidosis groups. Genome-wide associations with the rumen metabolome, microbial taxa, and milk composition were present across a wide geographical and management range of herds, suggesting the existence of markers for the rumen environment but not for acidosis susceptibility. The variation in pathogenesis of ruminal acidosis in the small population of cattle in the high risk for acidosis group and the dynamic nature of the rumen as cows cycle through a bout of acidosis may have precluded the identification of markers for acidosis susceptibility. Despite a limited sample size, this study provides evidence of interactions between the mammalian genome, the rumen metabolome, ruminal bacteria, and milk protein percentage.

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Heterosis Derived From Nonadditive Effects of the BnFLC Homologs Coordinates Early Flowering and High Yield in Rapeseed (Brassica napus L.)

Early flowering facilitates crops to adapt multiple cropping systems or growing regions with a short frost-free season; however, it usually brings an obvious yield loss. In this study, we identified that the three genes, namely, BnFLC.A2, BnFLC.C2, and BnFLC.A3b, are the major determinants for the flowering time (FT) variation of two elite rapeseed (Brassica napus L.) accessions, i.e., 616A and R11. The early-flowering alleles (i.e., Bnflc.a2 and Bnflc.c2) and late-flowering allele (i.e., BnFLC.A3b) from R11 were introgressed into the recipient parent 616A through a breeding strategy of marker-assisted backcross, giving rise to eight homozygous near-isogenic lines (NILs) associated with these three loci and 19 NIL hybrids produced by the mutual crossing of these NILs. Phenotypic investigations showed that NILs displayed significant variations in both FT and plant yield (PY). Notably, genetic analysis indicated that BnFLC.A2, BnFLC.C2, and BnFLC.A3b have additive effects of 1.446, 1.365, and 1.361 g on PY, respectively, while their dominant effects reached 3.504, 2.991, and 3.284 g, respectively, indicating that the yield loss caused by early flowering can be successfully compensated by exploring the heterosis of FT genes in the hybrid NILs. Moreover, we further validated that the heterosis of FT genes in PY was also effective in non-NIL hybrids. The results demonstrate that the exploration of the potential heterosis underlying the FT genes can coordinate early flowering (maturation) and high yield in rapeseed (B. napus L.), providing an effective strategy for early flowering breeding in crops.

Contribution of herd characteristics to best linear unbiased estimates of slaughter traits in beef cattle

Genetic evaluations separate phenotypes into their contributing additive genetic effects and non-(additive) genetic effects, with the former termed best linear unbiased predictions, and the latter termed best linear unbiased estimates (BLUEs). For the purpose of the present study, genetic evaluations, along with phenotypic data from 4 137 376 animals, were used to generate herd, year of slaughter and sex contemporary group BLUEs for various slaughter-related traits. These slaughter traits included carcass weight (CW), carcass conformation (CC) and carcass fat (CF). For the 4 665 herds that were consistently slaughtering ≥10 animals/year between the years 2014 and 2018, inclusive, all relevant contemporary group BLUEs were collapsed into a single herd-year value; results herein relate to these herds. The within-year herd-year BLUE correlations between CW and CC, between CW and CF, and between CC and CF were 0.51, 0.10 and -0.04, respectively. The repeatability across years of the herd-year BLUEs for CW, CC and CF was 0.66, 0.59 and 0.50, respectively. Furthermore, when the herds were stratified, within year, on the percentile rank of their herd-year BLUEs, herds had the greatest probability of remaining in the same BLUE stratum from one year to the next. In addition, results from the present study determined that various herd characteristics are associated with differences in the herd BLUEs. Results from the present study could be used to advise beef producers on the most promising strategy to improve the carcass merit of their animals.

Invited review: The future of selection decisions and breeding programs: What are we breeding for, and who decides?

Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.

Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).

Factors associated with the weight of individual primal cuts and their inter-relationship in cattle

Input parameters for decision support tools are comprised of, amongst others, knowledge of the associated factors and the extent of those associations with the animal-level feature of interest. The objective of the present study was to quantify the association between animal-level factors with primal cut yields in cattle and to understand the extent of the variability in primal cut yields independent carcass weight. The data used consisted of the weight of 14 primal carcass cuts (as well as carcass weight, conformation, and fat score) on up to 54,250 young cattle slaughtered between the years 2013 and 2017. Linear mixed models, with contemporary group of herd-sex-season of slaughter as a random effect, were used to quantify the associations between a range of model fixed effects with each primal cut separately. Fixed effects in the model were dam parity, heterosis coefficient, recombination loss, a covariate per breed representing the proportion of Angus, Belgian Blue, Charolais, Jersey, Hereford, Limousin, Simmental, and Holstein-Friesian and a three-way interaction between whether the animal was born in a dairy or beef herd, sex, and age at slaughter, with or without carcass weight as a covariate in the mixed model. The raw correlations among all cuts were all positive varying from 0.33 (between the bavette and the striploin) to 0.93 (between the topside and knuckle). The partial correlation among cuts, following adjustment for differences in carcass weight, varied from -0.36 to 0.74. Age at slaughter, sex, dam parity, and breed were all associated (P < 0.05) with the primal cut weight. Knowledge of the relationship between the individual primal cuts, and the solutions from the models developed in the study, could prove useful inputs for decision support systems to increase performance.