Genetic evaluation of gestation length and its use in managing calving patterns

Data collected from Australian Holstein cows that calved between 1995 and 2016 were used for estimating genetic parameters and genetic evaluation of gestation length (GL). Genetic parameters were estimated using a sire maternal grandsire model considering GL in heifers as correlated, but a different trait to adult cows and animal repeatability model. The key objective of this study was to assess if selective mating of bulls with short GL estimated breeding values (EBV) can help to modify calving patterns in predominantly pasture-based production systems and thereby contribute to the reduction of calving induction. The mean GL in heifers and adult cows was 280 and 281 d, respectively. The heritability of direct GL was 0.28 in heifers, which is slightly lower than in adult cows (0.36). The maternal heritability of GL was close to 0.04 in both heifer and adult cows. The genetic correlation between direct effects in heifers and adult cows was lower (0.88) than between maternal effects (0.94). A genetic evaluation model that considered heifer and adult cow data as the same trait in a repeatability animal model showed adequate variability in EBV for both direct and maternal GL. The genetic trend in direct GL EBV declined from 2005 in bulls and from 2006 in cows. Selective mating of bulls with short direct GL EBV showed that the GL and calving interval of their mates can be modified by up to 3.5 d for GL and 2.0 d for calving interval. Relative to parent average, the use of genotype data to calculate genomic EBV increased the reliability of EBV by up to 11% in validation bulls when daughter trait deviation of bulls with trait deviation of cows (11%) and deregressed breeding values (8%) were used as response variables. On average, for animals with only genotype data, the GL EBV can be predicted with about 50 to 60% reliability (expected) depending on the response variable (deregressed breeding values or daughter trait deviation and trait deviation) and the size of reference set. Overall, the results of this study show that calving patterns can be made tighter by selectively mating short GL EBV bulls to cows that do not become pregnant early in the mating season. Additionally, better reproductive management and the use of bulls with high female fertility EBV are still crucial for the success of a pasture-based dairy production system.     

Invited review: Genetics and modeling of milk coagulation properties

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF_t). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a₃₀, mm), and curd-firming time (k₂₀, min). The milk of different ruminant species varies in terms of CF_t pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a₃₀, and samples for which k₂₀ is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a₃₀ are highly correlated, both phenotypically and genetically. This means that the use of a₃₀ data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a₃₀ is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF_t modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.     

Short communication: the beta-casein (CSN2) silent allele C1 is highly spread in goat breeds

Several single nucleotide polymorphisms have been identified in the goat milk casein genes, most of them modifying the amino acid sequence of the coded protein. At least 9 variants have been found in goat β-CN (CSN2); 6 of them were characterized at the DNA level (A, A1, C, E, 0, and 0′), whereas the other 3 variants were described only at the protein level. The recently identified silent A1 allele is characterized by a C→T transition at the 180th nucleotide of the ninth exon. In the present work, typing results from different breeds (3 Italian, 3 German, and a composite of African breeds for a total of 335 samples) demonstrated that the same mutation is carried by the CSN2*C allele. In addition, the T nucleotide at the 180th nucleotide of the ninth exon was always associated with CSN2*C in all the breeds analyzed. Thus, another silent allele occurs at goat CSN2 and can be named CSN2*C1. The much wider distribution of C1 with respect to the A1 allele indicates that the single nucleotide polymorphisms characterizing the silent mutation originated from CSN2*C. A method for the identification of this allele simultaneously with 5 of the 6 DNA-characterized alleles is also proposed. The mutation involved codifies for the same protein of the C allele; nevertheless, its location in the 3′ untranslated region of the gene might affect the specific casein expression.     

A general method to validate breeding value prediction software

The validity of national genetic evaluations depends on the quality of input data, on the model of analysis, and on the correctness of genetic evaluation software. A general strategy was developed to validate national breeding value prediction software: performances from a real data file were replaced with simulated ones, created from simulated fixed and random effects and residuals in such a way that BLUP estimates from the evaluation software must be equal to the simulated effects. This approach was implemented for a multiple-trait model and a random regression test-day model. An example was presented on test-day observations analyzed with a random regression animal model including a lactation curve described as a sum of fixed polynomial regression and fixed spline regression on days in milk, and with genetic and permanent environmental effects modeled by using Legendre polynomials of order 2. Residuals had heterogeneous variances, and phantom parent groups were included. This method can be easily extended to other linear models. The comparison of genetic evaluation results with simulated true effects is used to demonstrate the great efficiency and usefulness of the proposed method.     

Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows

In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.     

Synchronization of breeding and its impact on genetic parameters and evaluation of female fertility traits

Achieving an acceptable level of fertility in herds is difficult for many dairy producers because identifying cows in estrus has become challenging owing to poor estrus expression, increased herd size, and lack of time and skilled labor for estrus detection. As a result, synchronization of estrus is often used to manage reproduction. The aims of this study were (1) to identify artificial inseminations (AI) that were performed following synchronization and (2) to assess the effect of synchronization on genetic parameters and evaluation of fertility traits. This study used breeding data collected between 1995 and 2021 from over 4,600 Australian dairy herds that had at least 30 matings per year. Because breeding methods were not reported, the recording pattern of breeding dates showing a large proportion of the total AI being recorded on a single date of the year served as an indicator of synchronization. First, the proportion of AI recorded on each day of the year was calculated for each herd-year. Subsequently, synchronization was defined when a herd with, for instance, only 30 matings in a year, had at least 0.20 or more AI on the same day. As the number of breedings in a herd-year increased, the threshold for classifying AI was continuously reduced from 0.20 to as low as 0.03 under the assumption that mating of many cows on a single date becomes increasingly difficult without synchronization. From the current data, we deduced that 0.11 of all AI were possibly performed following synchronization (i.e., timed AI, TAI). The proportion of AI classified as TAI increased over time and with herd size. Although the deviation from equal numbers of mating on 7 d of the week was not used for classifying AI, 0.44 of AI being categorized as TAI were performed on just 2 d of the week. When data classified as TAI were used for estimating genetic parameters and breeding values, the interval between calving and first service (CFS) was found to be the most affected trait. The phenotypic and additive genetic variance and heritability, as well as variability and reliability of estimated breeding values of bulls and cows for CFS were lower for TAI than for AI performed following detected estrus (i.e., estrus-detected AI, EAI). For calving interval, first service nonreturn rate (FNRR), and successful calving rate to first service, genetic correlations between the same trait measured in TAI and EAI were close to 1, in contrast to 0.55 for CFS. The lower genetic variances and heritabilities for FNRR and calving interval in TAI than in EAI suggests that synchronization reduces the genetic variability of fertility. In conclusion, TAI makes CFS an ineffective measure of fertility. One approach to minimize this effect on genetic evaluations is to identify TAI (using the method described for example) and then set the CFS of these cows as missing records when running multitrait genetic evaluations of fertility traits that include CFS. In the long term, the most practical and accurate way to reduce the effect of synchronization on genetic evaluations is to record TAI along with mating data.     

Genetic evaluation of in-line recorded milkability from milking parlors and automatic milking systems

The overall objective of this study was to assess the use of in-line recorded milkability information from dairy herds with conventional milking parlors (CMP) and from herds with automatic milking systems (AMS) for genetic evaluation. Some genetic parameters were previously studied on AMS data for 2,053 Swedish Holstein (SH) and 1,749 Swedish Red (SR) cows in 19 herds. These data were combined in the present paper with milkability information from 74 herds with CMP, including 11,123 SH cows and 7,554 SR cows. Genetic parameters were estimated for the CMP data and genetic correlations were estimated between milkability traits measured in the 2 systems. Average flow rate and milking time were derived and used as similar milkability traits for both systems, whereas box time was used only for AMS herds. Estimated heritabilities were in the range from 0.24 to 0.49. Even though the traits were differently defined in the 2 milking systems, the corresponding traits recorded in AMS and CMP were genetically closely related (0.93–1.00). Similarly, close genetic relationships were shown between milkability traits in different lactations in both breeds (0.93–0.99). Thus, it should be possible to treat milkability traits in different lactations and from different milking systems as the same traits in genetic evaluations. The various milkability traits were also highly genetically correlated, indicating that the inclusion of just one trait in the genetic selection program would efficiently select for milkability without the need to consider all measures. Comparisons of repeatability and random regression models, combining all information from the 2 systems for genetic evaluation, were done to find the most suitable model for genetic evaluation purposes. Even though the random regression models were favored in the formal model tests to evaluate suitability, correlation coefficients between test-days within lactation were high (0.7–0.8) and small differences in breeding values resulted among different models. That would indicate that a few test-days per cow would produce accurate breeding values for milkability.     

Genetic parameters for rennet- and acid-induced coagulation properties in milk from Swedish Red dairy cows

Milk coagulation is an important processing trait, being the basis for production of both cheese and fermented products. There is interest in including technological properties of these products in the breeding goal for dairy cattle. The aim of the present study was therefore to estimate genetic parameters for milk coagulation properties, including both rennet- and acid-induced coagulation, in Swedish Red dairy cattle using genomic relationships. Morning milk samples and blood samples were collected from 395 Swedish Red cows that were selected to be as genetically unrelated as possible. Using a rheometer, milk samples were analyzed for rennet- and acid-induced coagulation properties, including gel strength (G′), coagulation time, and yield stress (YS). In addition to the technological traits, milk composition was analyzed. A binary trait was created to reflect that milk samples that had not coagulated 40 min after rennet addition were considered noncoagulating milk. The cows were genotyped by using the Illumina BovineHD BeadChip (Illumina Inc., San Diego, CA). Almost 600,000 markers remained after quality control and were used to construct a matrix of genomic relationships among the cows. Multivariate models including fixed effects of herd, lactation stage, and parity were fitted using the ASReml software to obtain estimates of heritabilities and genetic and phenotypic correlations. Heritability estimates (h²) for G′ and YS in rennet and acid gels were found to be high (h² = 0.38–0.62) and the genetic correlations between rennet-induced and acid-induced coagulation properties were weak but favorable, with the exception of YS_rennet with G′_acid and YS_acid, both of which were strong. The high heritability (h² = 0.45) for milk coagulating ability expressed as a binary trait suggests that noncoagulation could be eliminated through breeding. Additionally, the results indicated that the current breeding objective could increase the frequency of noncoagulating milk and lead to deterioration of acid-induced coagulation through unfavorable genetic associations with protein content (0.38) and milk yield (−0.61 to −0.71), respectively. The outcome of this study suggests that by including more detailed compositional traits genetically associated with milk coagulation or by including milk coagulation properties directly within the breeding goal, it appears possible to breed cows that produce milk better suited for production of cheese and fermented products.     

Genetic parameters for reproductive losses estimated from in-line milk progesterone profiles in Swedish dairy cattle

This study assessed the extent of reproductive losses and associated genetic parameters in dairy cattle, using in-line milk progesterone records for 14 Swedish herds collected by DeLaval's Herd Navigator. A total of 330,071 progesterone samples were linked to 10,219 inseminations (AI) from 5,238 lactations in 1,457 Swedish Red and 1,847 Swedish Holstein cows. Pregnancy loss traits were defined as early embryonic loss (1–24 d after AI), late embryonic loss (25–41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss (from d 1 after AI until calving). The following classical fertility traits were also analyzed: interval from calving to first service, interval from calving to last service, interval between first and last service, calving interval, and number of inseminations per service period. Least squares means with standard error (LSM ± SE), heritabilities, and genetic correlations were estimated in a mixed linear model. Fixed effects included breed, parity (1, 2, ≥3), estrus cycle number when the AI took place, and a linear regression on 305-d milk yield. Herd by year and season of AI, cow, and permanent environmental effect were considered random effects. Extensive (approximately 45%) early embryonic loss was found, but with no difference between the breeds. Swedish Red was superior to Swedish Holstein in the remaining pregnancy loss traits with, respectively: late embryonic loss of 6.1 ± 1.2% compared with 13.3 ± 1.1%, fetal loss of 7.0 ± 1.2% compared with 12.3 ± 1.2%, and total pregnancy loss of 54.4 ± 1.4% compared with 60.6 ± 1.4%. Swedish Red also had shorter calving to first service and calving to last service than Swedish Holstein. Estimated heritability was 0.03, 0.06, and 0.02 for early embryonic, late embryonic, and total pregnancy loss, respectively. Milk yield was moderately genetically correlated with both early and late embryonic loss (0.52 and 0.39, respectively). The pregnancy loss traits were also correlated with several classical fertility traits (?0.46 to 0.92). In conclusion, Swedish Red cows had lower reproductive loss during late embryonic stage, fetal stage, and in total, and better fertility than Swedish Holstein cows. The heritability estimates for pregnancy loss traits were of the same order of magnitude as previously reported for classical fertility traits. These findings could be valuable in work to determine genetic variation in reproductive loss and its potential usefulness as an alternative fertility trait to be considered in genetic or genomic evaluations.     

Heifers with positive genetic merit for fertility traits reach puberty earlier and have a greater pregnancy rate than heifers with negative genetic merit for fertility traits

This study investigated the hypothesis that dairy heifers divergent in genetic merit for fertility traits differ in the age of puberty and reproductive performance. New Zealand's fertility breeding value (FertBV) is the proportion of a sire's daughters expected to calve in the first 42 d of the seasonal calving period. We used the New Zealand national dairy database to identify and select Holstein-Friesian dams with either positive (POS, +5 FertBV, n = 1,334) or negative FertBV (NEG, ?5% FertBV, n = 1,662) for insemination with semen from POS or NEG FertBV sires, respectively. The resulting POS and NEG heifers were predicted to have a difference in average FertBV of 10 percentage points. We enrolled 640 heifer calves (POS, n = 324; NEG, n = 316) at 9 d ± 5.4 d (± standard deviation; SD) for the POS calves and 8 d ± 4.4 d old for the NEG calves. Of these, 275 POS and 248 NEG heifers were DNA parent verified and retained for further study. The average FertBV was +5.0% (SD = 0.74) and ?5.1% (SD = 1.36) for POS and NEG groups, respectively. Heifers were reared at 2 successive facilities as follows: (1) calf rearing (enrollment to ～13 wk of age) and (2) grazier, after 13 wk until 22 mo of age. All heifers wore a collar with an activity sensor to monitor estrus events starting at 8 mo of age, and we collected weekly blood samples when individual heifers reached 190 kg of body weight (BW) to measure plasma progesterone concentrations. Puberty was characterized by plasma progesterone concentrations >1 ng/mL in at least 2 of 3 successive weeks. Date of puberty was defined when the first of these samples was >1 ng/mL. Heifers were seasonally bred for 98 d starting at ～14 mo of age. Transrectal ultrasound was used to confirm pregnancy and combined with activity data to estimate breeding and pregnancy dates. We measured BW every 2 wk, and body condition and stature at 6, 9, 12, and 15 mo of age. The significant FertBV by day interaction for BW was such that the NEG heifers had increasingly greater BW with age. This difference was mirrored with the significant FertBV by month interaction for average daily gain, with the NEG heifers having a greater average daily gain between 9 and 18 mo of age. There was no difference in heifer stature between the POS and NEG heifers. The POS heifers were younger and lighter at puberty, and were at a lesser mature BW, compared with the NEG heifers. As a result, 94 ± 1.6% of the POS and 82 ± 3.2% of the NEG heifers had reached puberty at the start of breeding. The POS heifers were 20% and 11% more likely to be pregnant after 21 d and 42 d of breeding than NEG heifers (relative risk = 1.20, 95% confidence interval of 1.03–1.34; relative risk = 1.11, 95% confidence interval of 1.01–1.16). Results from this experiment support an association between extremes in genetic merit for fertility base on cow traits and heifer reproduction. Our results indicate that heifer puberty and pregnancy rates are affected by genetic merit for fertility traits, and these may be useful phenotypes for genetic selection.