Genetic analysis of production traits and body size measurements and their relationships with metabolic diseases in German Holstein cattle

This study sheds light on the genetic complexity and interplay of production, body size, and metabolic health in dairy cattle. Phenotypes for body size-related traits from conformation classification (130,166 animals) and production (101,562 animals) of primiparous German Holstein cows were available. Additionally, 21,992, 16,641, and 7,096 animals were from herds with recordings of the metabolic diseases ketosis, displaced abomasum, and milk fever in first, second, and third lactation. Moreover, all animals were genotyped. Heritabilities of traits and genetic correlations between all traits were estimated and GWAS were performed. Heritability was between 0.240 and 0.333 for production and between 0.149 and 0.368 for body size traits. Metabolic diseases were lowly heritable, with estimates ranging from 0.011 to 0.029 in primiparous cows, from 0.008 to 0.031 in second lactation, and from 0.037 to 0.052 in third lactation. Production was found to have negative genetic correlations with body condition score (BCS; -0.279 to -0.343) and udder depth (-0.348 to -0.419). Positive correlations were observed for production and body depth (0.138-0.228), dairy character (DCH) (0.334-0.422), and stature (STAT) (0.084-0.158). In first parity cows, metabolic disease traits were unfavorably correlated with production, with genetic correlations varying from 0.111 to 0.224, implying that higher yielding cows have more metabolic problems. Genetic correlations of disease traits in second and third lactation with production in primiparous cows were low to moderate and in most cases unfavorable. While BCS was negatively correlated with metabolic diseases (-0.255 to -0.470), positive correlations were found between disease traits and DCH (0.269-0.469) as well as STAT (0.172-0.242). Thus, the results indicate that larger and sharper animals with low BCS are more susceptible to metabolic disorders. Genome-wide association studies revealed several significantly associated SNPs for production and conformation traits, confirming previous findings from literature. Moreover, for production and conformation traits, shared significant signals on Bos taurus autosome (BTA) 5 (88.36 Mb) and BTA 6 (86.40 to 87.27 Mb) were found, implying pleiotropy. Additionally, significant SNPs were observed for metabolic diseases on BTA 3, 10, 14, 17, and 26 in first lactation and on BTA 2, 6, 8, 17, and 23 in third lactation. Overall, this study provides important insights into the genetic basis and interrelations of relevant traits in today's Holstein cattle breeding programs, and findings may help to improve selection decisions.

Effect of feeding a palmitic acid-enriched supplement on production responses and nitrogen metabolism of mid-lactating Holstein and Jersey cows

This study evaluated the effect of feeding a palmitic acid-enriched supplement on production responses and nitrogen metabolism of mid-lactating Holstein and Jersey cows. Eighty mid-lactating dairy cows, 40 Holstein and 40 Jersey, were used in a randomized complete block design with a split-plot arrangement; the main plot was breed and the subplot was fatty acid treatment. Cows within each breed were assigned to 1 of 2 treatments: (1) control diet with no fat supplement or (2) control diet plus a palmitic acid-enriched supplement dosed at 1.5% of diet dry matter (PA treatment). The treatment period was 6 wk with the final 3 wk used for data and sample collection. There were no treatment × breed interactions for the variables analyzed. Compared with control, PA treatment increased milk fat yield (1.36 vs. 1.26 kg/d) and tended to increase 3.5% fat-corrected milk (35.6 vs. 34.0 kg/d) and energy-corrected milk (35.7 vs. 34.1 kg/d). There was no effect of PA treatment on dry matter intake, milk yield, milk protein yield, milk lactose yield, body condition score, body weight (BW) change, nitrogen intake, and variables related to nitrogen metabolism and excretion. Compared with Holstein cows, Jersey cows had greater dry matter intake as a percent of BW (4.90 vs. 3.37% of BW) and lower milk production (29.6 vs. 32.7 kg/d) and milk lactose yield (1.58 vs. 1.42 kg/d), but tended to have greater milk fat yield (1.36 vs. 1.26 kg/d). There was a breed effect on BW change; Holstein cows gained 0.385 kg/d during the experiment, and Jersey cows gained 0.145 kg/d. Jersey cows had lower nitrogen intake (636 vs. 694 g/d), blood urea nitrogen (12.6 vs. 13.8 mg/dL), urine total nitrogen (125 vs. 145 g/d), and urine total nitrogen as a percent of nitrogen intake (19.5 vs. 21.1%). Overall, feeding a palmitic acid-enriched supplement increased milk fat yield as well as dry matter and fiber digestibility in both Holstein and Jersey cows. The PA treatment did not have any major effects on nitrogen metabolism in both Holstein and Jersey cows. In addition, our results indicated that Jersey cows had lower urinary nitrogen excretion (g/d) than Holstein cows.

Altering the ratio of dietary C16:0 and cis-9 C18:1 interacts with production level in dairy cows: Effects on production responses and energy partitioning

The objective of our study was to evaluate the effects of altering the dietary ratio of palmitic (C16:0) and oleic (cis-9 C18:1) acids on nutrient digestibility, energy partitioning, and production responses of lactating dairy cows. Cows were blocked by milk yield and assigned to 3 groups (12 cows per group) in a main plot: low (45.2 ± 1.7 kg/d), medium (53.0 ± 1.6 kg/d), and high (60.0 ± 1.9 kg/d). Within each production group, a truncated Latin square arrangement of fatty acid (FA) treatments was used in 2 consecutive 35-d periods. The FA treatments supplemented at 1.5% of diet dry matter were (1) 80:10 (80% C16:0 + 10% cis-9 C18:1), (2) 73:17 (73% C16:0 + 17% cis-9 C18:1), (3) 66:24 (66% C16:0 + 24% cis-9 C18:1), and (4) 60:30 (60% C16:0 + 30% cis-9 C18:1). Treatment × production group interactions were observed for yields of milk, fat-corrected milk, energy-corrected milk, milk fat, milk protein, and milk lactose and energy partitioned to milk. Increasing cis-9 C18:1 in FA treatments reduced fat-corrected milk, energy-corrected milk, and milk energy output in low-producing cows but increased these in high-producing cows. Increasing cis-9 C18:1 in FA treatments did not affect milk yield, milk protein yield, and milk lactose yield in low- and medium-producing cows but increased these in high-producing cows. Regardless of production level, there was no effect of treatments on dry matter intake; however, increasing cis-9 C18:1 in FA treatments increased body weight change and body condition score change. Increasing cis-9 C18:1 in FA treatments increased total FA digestibility due to a linear increase in 16- and 18-carbon FA digestibilities. Interactions between FA treatments and production level were observed for the yield of milk fat and milk FA sources. In low-producing cows, increasing cis-9 C18:1 in FA treatments decreased milk fat yield due to a decrease in de novo and mixed milk FA without changes in preformed milk FA. In contrast, in high-producing cows, increasing cis-9 C18:1 in FA treatments increased milk fat yield due to an increase in de novo and preformed milk FA. Our results indicate that high-producing dairy cows (averaging 60 kg/d) responded better to a fat supplement containing more cis-9 C18:1, whereas low-producing cows (averaging 45 kg/d) responded better to a supplement containing more C16:0.

Discrepancies and Uncertainties in Bottom-up Gridded Inventories of Livestock Methane Emissions for the Contiguous United States

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

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

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.