Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Correlation of Genomic and Pedigree Inbreeding Coefficients in Small Cattle Populations

This study aimed to identify inbreeding coefficient (F) estimators useful for improvement programs in a small Holstein population through the evaluation of different methodologies in the Mexican Holstein population. F was estimated as follows: (a) from pedigree information (Fped); (b) through runs of homozygosity (Froh); (c) from the number of observed and expected homozygotic SNP in the individuals (Fgeno); (d) through the genomic relationship matrix (Fmg). The study included information from 4277 animals with pedigree records and 100,806 SNP. The average and standard deviation values of F were 3.11 ± 2.30 for Fped, −0.02 ± 3.55 for Fgeno, 2.77 ± 0.71 for Froh and 3.03 ± 3.05 for Fmg. The correlations between coefficients varied from 0.30 between Fped and Froh, to 0.96 between Fgeno and Fmg. Differences in the level of inbreeding among the parent’s country of origin were found regardless of the method used. The correlations among genomic inbreeding coefficients were high; however, they were low with Fped, so further research on this topic is required.

Truncation of IFT80 causes early embryonic loss in cattle

Recessive alleles represent a substantial risk in populations that have undergone significant bottleneck events. Analyzing millions of genotypes and hundreds of sequenced bulls, we identified one pernicious recessive variant that achieved 1.66% frequency in the US Holstein population and confirmed loss of embryonic development via CRISPR-Cas9 editing. In the process, we identified the developmental significance of two protein domains of the IFT80 gene in the embryonic development of cattle.

Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows

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 κ-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

The Blonde d’Aquitaine T3811>G3811 mutation in the myostatin gene: association with growth, carcass, and muscle phenotypes in veal calves

The mutation T3811 → G3811 (TG3811) discovered in the myostatin gene of the Blonde d’Aquitaine breed is suspected of contributing to the outstanding muscularity of this breed. An experiment was designed to estimate the effect of this mutation in an F2 and back-cross Blonde d’Aquitaine × Holstein population. By genotyping all known mutations in the myostatin gene, it was ensured that the TG3811 mutation was indeed the only known mutation segregating in this population. Fifty-six calves (43 F2, 13 back-cross) were intensively fattened and slaughtered at 24.0 ± 1.4 wk of age. The effects of the mutation were estimated by comparing the calves with the [T/T] (n = 18), [T/G] (n = 30), and [G/G] (n = 8) genotypes. Highly significant substitution effects (P < 0.001), above + 1.2 phenotypic SD, were shown on carcass yield and muscularity scores. Birth weight (P < 0.001) was positively affected by the mutation (+0.8 SD) but not growth rate (P = 0.97), while carcass length (P = 0.03), and fatness (P ≤ 0.03) were negatively affected (–0.5 to –0.7 SD). The characteristics of the Triceps brachii muscle were affected by the mutation (P < 0.001), with lower ICDH activity (oxidative) and a higher proportion of myosin type 2X muscle fibers (fast twitch). The effects of the TG3811 mutation were similar to those of other known myostatin mutations, although the Blonde d’Aquitaine animals, which are predominantly [G/G] homozygous, do not exhibit extreme double muscling.

NEW HAPLOTYPE OF HOLSTEIN CATTLE FERTILITY

В 2018 году методом идентификации регионов с потерей гомозиготности открыта новая генетическая аномалия крупного рогатого скота голштинской породы в гене SDE2 (g.29773628A>G; rs434666183). В гетерозиготном состоянии она приводит к носительству гаплотипа НН6, в гомозиготном — вызывает замедление роста эмбриона и самопроизвольный аборт до 56 дня стельности. Родоначальником этой аномалии считают голштинского производителя MOUNTAIN USAM000002070579, 1987 г. р. Частота встречаемости в современной голштинской популяции носителей НН6 гаплотипа составляет около 1,2%. Была установлена частота встречаемости носителей гаплотипа НН6 среди коров и быков-производителей голштинской породы, используемых в системе искусственного осеменения Краснодарского края. В статье приведены последовательности праймеров и методика генотипирования. Установлено, что частота встречаемости носителей гаплотипа НН6 в генотипированной группе (60 коров и 63 быка-производителя) составила менее 1% (0,8%). Проблема распространения носительства гаплотипа НН6 для молочного скотоводства Краснодарского края в настоящее время не является острой, однако тестирование на наличие гаплотипа НН6 желательно включить вместе с тестированием на наличие других гаплотипов голштинского скота (НН1, НН2, НН3, НН4, НН5, НСD) в список обязательных генетических тестирований племенных животных с целью не допустить в дальнейшем распространения мутации в гене SDE2. In 2018, a new genetic anomaly of Holstein cattle in the SDE2 gene (g.29773628A>G; rs434666183) was discovered by the method of identification of regions with loss of homozygosity. In the heterozygous state, it leads to the carrier of the haplotype HH6, in the homozygous state, it causes a slowdown in the growth of the embryo and spontaneous abortion before the 56th day of pregnancy. The originator of this anomaly is considered to be the Holstein manufacturer MOUNTAIN USAM000002070579 born in 1987. the Frequency of occurrence in the modern Holstein population of carriers of the HH6 haplotype is about 1.2%. The frequency of occurrence of carriers of the haplotype HN6 among Holstein cows and bulls used in the system of artificial insemination of the Krasnodar territory was established. The article presents the sequence of primers and the method of genotyping. It was found that the frequency of occurrence of carriers of the HH6 haplotype in the genotyped group (60 cows and 63 bulls) was less than 1% (0.8%). The problem of spreading the carrier haplotype HH6 for dairy cattle in the Krasnodar territory is currently not acute, but testing for the presence of haplotype HH6 is desirable to include together with testing for the presence of other haplotypes of Holstein cattle (HH1, HH2, HH3, HH4, HH5, HSD) in the list of mandatory genetic testing of breeding animals in order to prevent the further spread of mutations in the SDE2 gene.

Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population

Milk composition always serves as an indicator for the cow’s health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm−1 to 5011.54 cm−1, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm−1 to 1200 cm−1), protein-region (1240 cm−1 to 1600 cm−1), and fat-regions (1680 cm−1 to 1770 cm−1 and 2800 cm−1 to 3015 cm−1). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, −0.29, and −0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and −0.01 to −0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.