Genetic analysis of body condition score in first-parity Danish Holstein cows

The aim of this study was to test whether genetic components for body condition score (BCS) changed during lactation in first-parity Danish Holsteins. Data were extracted from the national conformation scoring system and consisted of 28,948 records from 3894 herds. Cows were scored once during lactation for BCS on a scale from 1 to 9 with increments of 1. The majority of records were made from d 30 to 150 of lactation. Mean BCS was 4.28 +/- 0.98. Body condition score was lowest in wk 8 to 10 from calving. A multivariate sire model with BCS recordings in six lactation stages treated as different traits was used to analyze the data. In addition, a random regression sire model was used to evaluate the changes in BCS as continuous functions of lactation stage. Estimates of heritability from the multivariate approach ranged from 0.14 to 0.29, and the estimated genetic correlations between BCS at different lactation stages were all higher than 0.82. The random regression model was based on Legendre polynomials (LP) specified on days in milk at scoring. To evaluate the change in mean BCS during lactation, the fixed part of the model included a fifth-order LP on the effect of days in milk at scoring. The highest order of fit used for the sire effect was a third-order LP, but based on likelihood ratio tests this could be reduced to a 0 order, i.e., a model with only the intercept term for the sire effect. This means that the genetic variation is constant over the investigated part of the lactation. Therefore, BCS can be considered the same trait during lactation, and a simple sire model can be used for prediction of breeding values.     

The relationship between genetic merit for yield and live weight, condition score, and energy balance of spring calving Holstein Friesian dairy cows on grass based systems of milk production

The objectives of this study were to estimate the effects of genetic merit for milk yield on energy balance, DM intake (DMI), and fertility for cows managed on three different grass-based feeding systems and to estimate possible interactions between genetic merit and feeding system. Individual animal intake estimates were obtained at pasture on 11 occasions across three grazing seasons. The data set contained 96 first lactation, 96 second lactation, and 72 third lactation cows in 1995, 1996, and 1997, respectively. Half of these cows were of high genetic merit, and half were of medium genetic merit for milk solids production. Genetic effects for the traits of interest were estimated as the contrast between the two genetic groups and by the genetic regression of phenotypic performance on the estimated breeding value for fat and protein yield, based on pedigree index. Significant effects of feeding system were observed on yields, DMI, and energy balance, with no effect on live weight, condition score, or reproductive performance. The interaction between genetic merit and feeding system was not significantly different from zero for any of the traits. Yields, grass DMI, and total DMI were all higher for cows of high genetic merit than for those of medium genetic merit and were positively correlated (P < 0.001) with pedigree index. Furthermore, condition score, conception to first and second services, and pregnancy rate were significantly negatively correlated with pedigree index. While at pasture, energy balance was positively (P < 0.01) correlated with pedigree index, although the contrast between high genetic merit and medium genetic merit was not significantly different from zero. This positive energy balance was unexpected and was probably due to the lactation stage that intake was measured. Condition score changes and energy balance measures on a small subgroup of the animals, while indoors offered a diet of silage and concentrates (n = 33), demonstrated that high genetic merit had a more negative energy balance than did medium genetic merit. The results clearly illustrate the production potential of high genetic merit cows on grass-based systems. The reduced reproductive performance questions the suitability of high yield for seasonal calving systems.     

Effect of genetic merit and concentrate supplementation on grass intake and milk production with Holstein Friesian dairy cows

A total of 48 high genetic merit (HM) and 48 medium merit (MM) cows, each given a low (LC), medium (MC), or high (HC) level of concentrate supplementation, were used in a split-plot design experiment, which was run in three consecutive years, to evaluate animal production responses. Individual cow intakes were estimated twice each year while at pasture; measurement period 1 (MP1) was in May/June, and measurement period 2 (MP2) was in early September, corresponding on average to d 110 and 200 of lactation, respectively. In MP1, cows were offered 0 (LC), 3 (MC), and 6 kg (HC), whereas in MP2 the levels were 0 (LC), 0 (MC), and 4 kg (HC) of concentrate daily. Genotype had a significant effect on all milk production parameters in MP1 and MP2. The HM cows had the highest yield of milk, fat, protein, and lactose, whereas the MM cows had the highest milk fat, protein, and lactose concentrations. The HM cows had significantly higher grass dry matter intake (GDMI) estimates. In MP1, the average responses, per kg concentrate dry matter, was +1.10 kg of milk, +0.038 kg of protein, +0.032 kg of fat. The corresponding values in MP2 were +0.94 kg of milk, +0.037 kg of protein, and +0.025 kg of fat. The response to concentrate was linear and independent of preexperimental milk yield. In MP1, the partial regression coefficients relating daily GDMI to an increase in 1 kg of preexperimental milk yield (PMY), preexperimental BW (PBW), and concentrate intake (CI) were 0.123, 0.006, and -0.54, respectively, whereas the corresponding values in MP2 were 0.190,0.007, and-0.444, respectively. This study indicates that with high yielding dairy cows, on gras only GDMI of 17 kg of supporting milk yield of 30-kg/d is achievable. In this scenario, concentrate supplementation will result in lower substitution rates, and higher milk yield response than previously published with lower yielding cows.     

Genetic relationships between body condition score and reproduction traits in Canadian Holstein and Ayrshire first-parity cows

The objective of this study was to investigate the genetic relationship between body condition score (BCS) and reproduction traits for first-parity Canadian Ayrshire and Holstein cows. Body condition scores were collected by field staff several times over the lactation in herds from Québec, and reproduction records (including both fertility and calving traits) were extracted from the official database used for the Canadian genetic evaluation of those herds. For each breed, six 2-trait animal models were run; they included random regressions that allowed the estimation of genetic correlations between BCS over the lactation and reproduction traits that are measured as a single lactation record. Analyses were undertaken on data from 108 Ayrshire herds and 342 Holstein herds. Average daily heritabilities of BCS were close to 0.13 for both breeds; these relatively low estimates might be explained by the high variability among herds and BCS evaluators. Genetic correlations between BCS and interval fertility traits (days from calving to first service, days from first service to conception, and days open) were negative and ranged between −0.77 and −0.58 for Ayrshire and between −0.31 and −0.03 for Holstein. Genetic correlations between BCS and 56-d nonreturn rate at first insemination were positive and moderate. The trends of these genetic correlations over the lactation suggest that a genetically low BCS in early lactation would increase the number of days that the primiparous cow was not pregnant and would decrease the chances of the primiparous cow to conceive at first service. Genetic correlations between BCS and calving traits were generally the strongest at calving and decreased with increasing days in milk. The correlation between BCS at calving and maternal calving ease was 0.21 for Holstein and 0.31 for Ayrshire and emphasized the relationship between fat cows around calving and dystocia. Genetic correlations between calving traits and BCS during the subsequent lactation were moderate and favorable, indicating that primiparous cows with a genetically high BCS over the lactation would have a greater chance of producing a calf that survived (maternal calf survival) and would transmit the genes that allowed the calf to be born more easily (maternal calving ease) and to survive (direct calving ease).     

Short communication: Relationship between body condition score and plasma adipokines in early-lactating Holstein dairy cows

We hypothesized that plasma adipokine concentrations of early-lactation dairy cows are related to body condition score (BCS) at calving and to markers of metabolic status of the cow. As part of a larger study with 117 multiparous Holstein dairy cows, which had high BCS (BCS >4.0) or normal BCS (3.25–3.5) at calving, 22 cows were randomly selected (n = 11 per group) to be enrolled in this study. Cows were divided into 2 groups based on their BCS at calving: (1) normal BCS with BCS of 3.35 ± 0.13 (mean ± SD) and (2) high BCS cows with BCS of 4.14 ± 0.17. The 22 selected animals did not have a clinically diagnosed health problem after calving. Blood samples were taken right after calving (d 1) and before morning feeding on d 8, 15, and 21 postpartum concurrently with body condition scoring for all cows. Blood samples were analyzed for plasma adiponectin, leptin, tumor necrosis factor-α, and IL-6. The mean BCS remained highest in high-BCS cows during the first 21 d in milk. Leptin concentrations decreased progressively for all cows after calving. However, differences in BCS at calving were not related to leptin concentrations. Adiponectin, IL-6, and tumor necrosis factor-α concentrations were neither influenced by days in milk nor BCS after calving. Leptin and the leptin-to-adiponectin ratio did not show any correlation at any time point during the first 21 d in milk with plasma concentrations of nonesterified fatty acids or β-hydroxybutyrate, which are considered as markers of metabolic status. Only for IL-6 at d 8 did we find a strong correlation with metabolic status indicators. In conclusion, plasma adipokine concentrations during the first 3 wk postpartum were not related to BCS in lactating Holstein cows that were clinically healthy at calving.     

International genetic evaluation of Holstein bulls for overall type traits and body condition score

The study documents the procedures used to estimate genetic correlations among countries for overall conformation (OCS), overall udder (OUS), overall feet and legs (OFL), and body condition score (BCS) of Holstein sires. Major differences in traits definition are discussed, in addition to the use of international breeding values (IBV) among countries involved in international genetic evaluations, and similarities among countries through hierarchical clustering. Data were available for populations from 20 countries for OCS and OUS, 18 populations for OFL, and 11 populations for BCS. The IBV for overall traits and BCS were calculated using a multi-trait across-country evaluation model. Distance measures, obtained from genetic correlations, were used as input values in the cluster analysis. Results from surveys sent to countries participating in international genetic evaluation for conformation traits showed that different ways of defining traits are used: the overall traits were either computed from linear or composite traits or defined as general characteristics. For BCS, populations were divided into 2 groups: one scored and evaluated BCS, and one used a best predictor. In general, populations were well connected except for Estonia and French Red Holstein. The average number of common bulls for the overall traits ranged from 19 (OCS and OUS of French Red Holstein) to 514 (OFL of United States), and for BCS from 17 (French Red Holstein) to 413 (the Netherlands). The average genetic correlation (range) across countries was 0.75 (0.35 to 0.95), 0.80 (0.41 to 0.95), and 0.68 (0.12 to 0.89) for OCS, OUS, and OFL, respectively. Genetic correlations among countries that used angularity as best predictor for BCS and countries that scored BCS were negative. The cluster analysis provided a clear picture of the countries distances; differences were due to trait definition, trait composition, and weights in overall traits, genetic ties, and genotype by environment interactions. Harmonization of trait definition and increasing genetic ties could improve genetic correlations across countries and reduce the distances. In each national selection index, all countries, except Estonia and New Zealand, included at least one overall trait, whereas none included BCS. Out of 18 countries, 9 have started genomic evaluation of conformation traits. The first were Canada, France, New Zealand, and United States in 2009, followed by Switzerland, Germany, and the Netherlands in 2010, and Australia and Denmark-Finland-Sweden (joint evaluation) in 2011. Six countries are planning to start soon.     

Genetic analysis of body condition score of lactating Dutch Holstein and Red-and-White heifers

The aim of this study was to estimate phenotypic and genetic parameters for body condition scores (BCS) from the Dutch type classification system. Data included 108,809 Holstein (H) and 26,208 Red-and-White (R) heifers from 9701 herds that were scored once during lactation on a 1 to 9 scale (1 = emaciated and 9 = obese). Mean BCS for H and R data were 4.50 and 4.94, respectively. The BCS decreased as the percentage of Holstein genes increased. For both breeds, BCS after calving was about 5.6 and BCS was lowest around wk 11. For H heifers, mean BCS at drying off was about 0.8 lower than BCS at calving, whereas for R heifers BCS was at about the same level as at calving. Variance components were estimated using an animal model including the effects of herd x visit, classifier, age at calving, DIM, and genetic group. The random herd x visit effect explained about 10 to 15% of the phenotypic variation. Heritabilities ranged from 0.24 to 0.38, depending on breed and lactation period. Genetic correlations between BCS observations in bimonthly lactation periods were close to unity, especially for H. It was concluded that BCS data collected by type classifiers can well be used for genetic evaluation and that genetic variation between animals for BCS-change patterns is a small component of the overall variation in BCS.     

Some attributes of twin-bearing British Friesian and Canadian Holstein cows recorded in England and Wales

The production of milk, fat and protein of 37216 mothers of twins was compared with that of 1 691 127 mothers of single calves of Black and White breeds of cattle recorded in England and Wales. At every parity, the mothers of twins produced more milk, fat and protein than the mothers of single calves. The proportion of twin-bearing cows retained increased with parity, from 0.67% at first calving to approximately 40% at the tenth or eleventh calving. More mothers of twins achieved 305 d of lactation subsequently than mothers of single calves, although their inter-calving intervals were not generally longer.     

Genomic associations with somatic cell score in first-lactation Holstein cows

This genome-wide association study aimed to identify loci associated with lactation-average somatic cell score (LASCS) and the standard deviation of test-day somatic cell score (SCS-SD). It is one of the first studies to combine detailed phenotypic and genotypic cow data from research dairy herds located in different countries. The combined data set contained up to 52 individual test-days per lactation and thereby aimed to capture temporary increases in somatic cell score associated with infection. Phenotypic data for analysis consisted of 46,882 test-day records on 1,484 cows, and genotypic data consisted of 37,590 single nucleotide polymorphisms (SNP). Using an animal model, the associations between each individual SNP and the phenotypic data were estimated. To account for the risk of false positives, a false discovery rate threshold of 0.20 was set. The analyses showed that LASCS was significantly associated with a SNP on Bos taurus autosome (BTA) 4 and a SNP on BTA18. Likewise, SCS-SD was associated with this SNP on BTA18. In addition, SCS-SD significantly associated with a SNP on BTA6. Relatively few associations were found, suggesting that LASCS and SCS-SD are controlled by multiple loci distributed across the genome, each with a relatively small effect. Increased knowledge on genetic regulation of LASCS and SCS-SD may aid in identification of genes that play a role in mastitis resistance. Such knowledge helps us understand the genetic mechanisms leading to mastitis and in discovery of targets for mastitis therapeutics.     

Value of the Dutch Holstein Friesian germplasm collection to increase genetic variability and improve genetic merit

National gene bank collections for Holstein Friesian (HF) dairy cattle were set up in the 1990s. In this study, we assessed the value of bulls from the Dutch HF germplasm collection, also known as cryobank bulls, to increase genetic variability and improve genetic merit in the current bull population (bulls born in 2010–2015). Genetic variability was defined as 1 minus the mean genomic similarity (SIM_SNP) or as 1 minus the mean pedigree-based kinship (f_PED). Genetic merit was defined as the mean estimated breeding value for the total merit index or for 1 of 3 subindices (yield, fertility, and udder health). Using optimal contribution selection, we minimized relatedness (maximized variability) or maximized genetic merit at restricted levels of relatedness. We compared breeding schemes with only bulls from 2010 to 2015 with schemes in which cryobank bulls were also included. When we minimized relatedness, inclusion of genotyped cryobank bulls decreased mean SIM_SNP by 0.7% and inclusion of both genotyped and nongenotyped cryobank bulls decreased mean f_PED by 2.6% (in absolute terms). When we maximized merit at restricted levels of relatedness, inclusion of cryobank bulls provided additional merit at any level of mean SIM_SNP or mean f_PED except for the total merit index at high levels of mean SIM_SNP. Additional merit from cryobank bulls depended on (1) the relative emphasis on genetic variability and (2) the selection criterion. Additional merit was higher when more emphasis was put on genetic variability. For fertility, for example, it was 1.74 SD at a mean SIM_SNP restriction of 64.5% and 0.37 SD at a mean SIM_SNP restriction of 67.5%. Additional merit was low to nonexistent for the total merit index and higher for the subindices, especially for fertility. At a mean SIM_SNP of 64.5%, for example, it was 0.60 SD for the total merit index and 1.74 SD for fertility. In conclusion, Dutch HF cryobank bulls can be used to increase genetic variability and improve genetic merit in the current population, although their value is very limited when selecting for the current total merit index. Anticipating changes in the breeding goal in the future, the germplasm collection is a valuable resource for commercial breeding populations.     

Thin and fat cows,and the nonlinear genetic relationship between body condition score and fertility

Thin and fat cows are often credited for low fertility, but body condition score (BCS) has been traditionally treated as a linear trait when genetic correlations with reproductive performance have been estimated. The aims of this study were to assess genetic parameters for fertility, production, and body condition traits in the Brown Swiss population reared in the Alps (Bolzano-Bozen Province, Italy), and to investigate the possible nonlinearity among BCS and other traits by analyzing fat and thin cows. Records of BCS measured on a 5-point scale were preadjusted for year-season and days in milk at scoring, and were considered positive (1) for fat cows if they exceeded the value of 1 residual standard deviation or null (0) otherwise, whereas positive values for thin cows were imputed to records below −1 residual standard deviation. Fertility indicators measured on first- and second-parity cows were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield, lactation milk yield, and lactation length. Data were from 1,413 herds and included 16,324 records of BCS, fertility, and production for first-parity, and 10,086 fertility records for second-parity cows. Animals calved from 2002 to 2007 and were progeny of 420 artificial insemination bulls. Genetic parameters for the aforementioned traits were obtained under univariate and bivariate threshold and censored linear sire models implemented in a Bayesian framework. Posterior means of heritabilities for BCS, fat cows, and thin cows were 0.141, 0.122, and 0.115, respectively. Genetic correlations of body condition traits with contemporary production were moderate to high and were between −0.556 and 0.623. Body condition score was moderately related to fertility in first (−0.280 to 0.497) and second (−0.392 to 0.248) lactation. The fat cow trait was scarcely related to fertility, particularly in first-parity cows (−0.203 to 0.281). Finally, the genetic relationships between thin cows and fertility were higher than those between BCS and fertility, both in first (−0.456 to 0.431) and second (−0.335 to 0.524) lactation. Body condition score can be considered a predictor of fertility, and it could be included in evaluation either as linear measure or as thin cow. In the second case, the genetic relationship with fertility was stronger, exacerbating the poorest body condition and considering the possible nonlinearity between fertility and energy reserves of the cow.     

Effects of bovine somatotropin on milk yield and composition, body weight, and condition score of Holstein and Jersey cows

Forty cows (20 Holstein, 20 Jersey) were administered 0, 5, 10, 15, or 20 mg of recombinantly derived bST daily to determine the effect on milk yield, milk composition, body weight, and body condition score. Administration of bST was from 75 +/- 7 d through 305 d postpartum. A total mixed diet of 45% corn silage and 55% of a concentrate mixture (dry basis) was provided for ad libitum intake. Milk yield of Holstein and Jersey cows administered 20 mg of bST increased 25.3 and 22.8%, respectively, over controls. Fat-corrected milk from Holsteins and Jerseys that were administered 20 mg of somatotropin increased 32.2 and 18.7% over controls, but Jersey response was greatest when 15 mg of bST were administered (27.1% over controls). Dry matter intake of the 20 mg bST group was 13.5% greater than DM intake of controls. Apparent efficiency of production increased linearly with increasing somatotropin. There was no significant change in body weight, but body condition score declined linearly with increasing somatotropin. Most milk composition measures were unaffected by somatotropin. Jersey and Holstein milk yield increased quadratically and linearly, respectively, with somatotropin dose.     

Association between a visual and an automated locomotion score in lactating Holstein cows

Two studies were conducted to evaluate visual locomotion scoring (VLS) and Stepmetrix locomotion scoring (SLS) in detecting painful digit lesions. In study 1, one veterinarian performed VLS. Cows with VLS ≥3 were hoof trimmed and the presence or absence of a painful lesion (PL), defined as a reaction to digital pressure, was recorded. A strongly increasing pattern in the proportion of cows with PL was detected as VLS increased. The proportions of cows with painful lesions were 5.6% (n = 53), 20.1% (n = 78), 55.5% (n = 164), 79.9% (n = 159), and 100% (n = 5) for VLS 1 to 5, respectively. Study 2 was conducted on a different farm. The entire farm was visually locomotion scored by 3 veterinarians on the same day, and the cows were Stepmetrix locomotion scored by walking through the Stepmetrix system. Every cow was trimmed during the following 2 d by 1 of 8 professional hoof trimmers. The 3 veterinarians identified, scored, and recorded any PL. Interobserver agreement for the 3 veterinarians had a kappa coefficient of between 0.45 and 0.48 ± 0.05. In total, 518 cows were used in the analysis, from which 11.2% were identified with a PL. Of the cows diagnosed with a PL, 32.8% were detected with a sole ulcer, 25.9% with white line disease, 13.8% with white line abscess, and 27.5% with other diseases. A receiver operating characteristic analysis was performed; the area under the curve was larger for VLS (0.80; 95% confidence interval, 0.76 to 0.83) than SLS (0.62; 95% confidence interval, 0.57 to 0.66). When performed by trained veterinarians, VLS performed better than SLS in detecting PL.     

Impact of area and sire by herd interaction on heritability estimates for somatic cell count in Italian Holstein Friesian cows.

The aim of the paper was to estimate variance components for somatic cell scores for Italian Holsteins using data from three different areas of the country. A total of 2,202,804 first-parity test-day records, collected from 1990 to 1997 in three areas of Italy (Mantova, Milano, and Parmigiano cheese area), were available for study. The areas differ in herd size, feeding systems and especially in milk use. A minimum standard of quality is also required by some specific methods of cheese production, as for example from the Parmigiano Reggiano cheese chain. These reasons, taken together, affect the attention given to the quality of milk production in herds, and, therefore, to the sanitation levels. A pedigree file was extracted from the national database of Holstein Friesian breed. For computational reasons, eight samples of the data were extracted per area. Variance components were estimated by sample using two different test-day repeatability models. The first model included fixed effects of herd-test date, days in milk (30-d intervals) and calving month, and random effects of permanent environment, additive genetic and residual error. Estimated heritabilities in the first model ranged from 0.06 to 0.09 and repeatabilities from 0.36 to 0.45. Only small differences were detected among areas. In the second model, a random sire x herd interaction effect was added. Including the sire x herd effect resulted in heritability estimates ranging between 0.05 and 0.08 and repeatabilities from 0.35 to 0.45. The analysis revealed that only a small fraction of the total variance (0.35 to 1.5%) could be explained by sire x herd interaction effect. Based on this research, it appears that parameter estimates for somatic cell count do not differ by region, and inclusion of a sire x herd interaction effect is unnecessary.     

Genetics of body condition score in New Zealand dairy cows

Body condition score (BCS) data were collected on 169,661 first-parity cows from herds participating in progeny testing schemes and linear type assessment. Genetic and residual variances for BCS estimated across time using a quadratic random regression model were found to be largest at the start of lactation. Heritability estimates ranged from 0.32 to 0.23 from d 1 to 200 of lactation, with a mean of 0.26. Genetic correlations between BCS and other traits were estimated using 2 approaches: 1) a multivariate analysis that included BCS and live weight, both adjusted for stage of lactation; 270-d cumulative yields of milk, fat, and protein; average somatic cell score; and 2 measures of fertility; and 2) a bivariate random regression analysis in which BCS was considered to be a longitudinal trait across time, with the same measurements as in approach 1 for all other traits. Genetic correlations of BCS with the 2 fertility traits were 0.43 and 0.50 using the multivariate analysis; the corresponding random regression estimates between BCS as a longitudinal trait across time and 2 measures of fertility were 0.35 to 0.44 and 0.40 to 0.49, and tended to increase with stage of lactation. Genetic correlations estimated using the random regression model fluctuated around the multivariate estimates for live weight and somatic cell score, which were 0.50 and −0.12, respectively. Genetic correlations estimated using the multivariate analysis of BCS with fat and protein yields were close to zero. With the random regression model, genetic correlations between BCS and fat and protein yields were positive at d 1 of lactation (0.16 and 0.08, respectively) and were negative by d 200 of lactation (−0.25 and −0.20, respectively). In pastoral production systems, such as those typical in New Zealand, there appears to be an advantage in the total lactation yields of fat and protein for cows of higher BCS in early lactation, which is likely to be because these cows have body reserves that are available to be mobilized in later lactation, when feed resources are sometimes limited.     

Heritabilities of and genetic correlations among six health problems in Holstein cows

Information from 7712 lactations of Holstein dairy cows was collected from 33 commercial herds around Ithaca, NY in the 3 yr from 1981 to 1983. The data were divided into subsets corresponding to lactation 1, lactation 2, and lactation 3 or greater. To estimate heritabilities of dystocia, retained placenta, metritis, ovarian cysts, milk fever, and mastitis, a mixed linear model (herd-year fixed and sire random effects) with 0 or 1 as the observed response was used. Variance components were estimated using Henderson's Method 3. The results show moderate heritabilities (.15 to .40) for dystocia, metritis, milk fever, and mastitis and low heritability (less than .12) for retained placenta and cystic ovaries. Genetic correlations between dystocia, retained placenta, metritis, and mastitis were moderate in size and positive, whereas cystic ovaries were correlated negatively with dystocia and retained placenta. A general reproductive health trait (dystocia, retained placenta, metritis, cystic ovaries, and milk fever combined in one trait) also was analyzed. The estimated heritability of this trait was .21, .11, and .00 for first calf heifers, second lactation cows, and older cows, respectively.     

Genetic relationship between body condition score, dairy character, mastitis, and diseases other than mastitis in first-parity Danish Holstein cows

The aim of this study was to explore the possibilities of using body condition score (BCS) or dairy character (DC) as indicators of mastitis and diseases other than mastitis in first-parity Danish Holsteins. The dataset included 28,948 observations on conformation scores and 365,136 disease observations. The analysis was performed using a multitrait linear sire model. Heritability estimates for BCS and DC were moderate (0.25 and 0.22), and heritability estimates for mastitis and diseases other than mastitis were low (0.038 and 0.022). Between BCS and diseases other than mastitis, the genetic correlation was -0.22, whereas the genetic correlation was -0.16 between BCS and mastitis. The genetic correlation between DC and diseases other than mastitis was 0.43, and between DC and mastitis it was 0.27. The genetic correlation between BCS and DC was -0.61. Residual correlations were close to 0, except between BCS and DC (-0.37). Including DC as an indicator of diseases other than mastitis will increase the accuracy of the predicted breeding value for diseases, especially when the progeny group is small. Using BCS as an additional indicator of diseases did not increase the accuracy. Breeding for less DC will increase resistance to diseases.     

Genetic background of methane emission by Dutch Holstein Friesian cows measured with infrared sensors in automatic milking systems

International environmental agreements have led to the need to reduce methane emission by dairy cows. Reduction could be achieved through selective breeding. The aim of this study was to quantify the genetic variation of methane emission by Dutch Holstein Friesian cows measured using infrared sensors installed in automatic milking systems (AMS). Measurements of CH₄ and CO₂ on 1,508 Dutch Holstein Friesian cows located on 11 commercial dairy farms were available. Phenotypes per AMS visit were the mean of CH₄, mean of CO₂, mean of CH₄ divided by mean of CO₂, and their log₁₀-transformations. The repeatabilities of the log₁₀-transformated methane phenotypes were 0.27 for CH₄, 0.31 for CO₂, and 0.14 for the ratio. The log₁₀-transformated heritabilities of these phenotypes were 0.11 for CH₄, 0.12 for CO₂, and 0.03 for the ratio. These results indicate that measurements taken using infrared sensors in AMS are repeatable and heritable and, thus, could be used for selection for lower CH₄ emission. Furthermore, it is important to account for farm, AMS, day of measurement, time of day, and lactation stage when estimating genetic parameters for methane phenotypes. Selection based on log₁₀-transformated CH₄ instead of the ratio would be expected to give a greater reduction of CH₄ emission by dairy cows.     

The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential

This study aimed to quantify the relationship between CH₄ emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH₄ production was 359 ± 51.1 g/d, CH₄ yield was 22.6 ± 2.31 g/kg of DMI, and CH₄ intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH₄ production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH₄ production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH₄ prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH₄ emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH₄ production and CH₄ yield. For CH₄ intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH₄ production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH₄ emission of dairy cows. We conclude that MFA have moderate potential to predict CH₄ emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH₄ emissions of dairy cows.     

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain β-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.