N. Krattenmacher

Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60- to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming near-unity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries.

Genetic and genomic dissection of dry matter intake at different lactation stages in primiparous Holstein cows.

Dry matter intake (DMI) and feed efficiency are economically relevant traits. Simultaneous selection for low DMI and high milk yield might improve feed efficiency, but bears the risk of aggravating the negative energy balance and related health problems in early lactation. Lactation stage-specific selection might provide a possibility to optimize the trajectory of DMI across days in milk (DIM), but requires in-depth knowledge about genetic parameters within and across lactation stages. Within the current study, daily heritabilities and genetic correlations between DMI records from different lactation stages were estimated using random regression models based on 910 primiparous Holstein cows. The heritability estimates from DIM 11 to 180 follow a slightly parabolic curve varying from 0.26 (DIM 121) to 0.37 (DIM 11 and 180). Genetic correlations estimated between DIM 11, 30, 80, 130, and 180 were all positive, ranging from 0.29 (DIM 11 and 180) to 0.97 (DIM 11 and 30; i.e., the correlations are inversely related to the length of the interval between compared DIM). Deregressed estimated breeding values for the same lactation days were used as phenotypes in sequential genome-wide association studies using 681 cows drawn from the study population and genotyped for the Illumina SNP50 BeadChip (Illumina Inc., San Diego, CA). A total of 21 SNP on 10 chromosomes exceeded the chromosome-wise significance threshold for at least 1 analyzed DIM, pointing to some interesting candidate genes directly involved in the regulation of feed intake. Association signals were restricted to certain lactation stages, thus supporting the genetic correlations. Partitioning the explained variance onto chromosomes revealed a large contribution of Bos taurus autosome 7 not harboring any associated marker in the current study. The results contribute to the knowledge about the genetic architecture of the complex phenotype DMI and might provide valuable information for future selection efforts.

Methyl-coenzyme M reductase A as an indicator to estimate methane production from dairy cows

The evaluation of greenhouse gas mitigation strategies requires the quantitative assessment of individual methane production. Because methane measurement in respiration chambers is highly accurate, but also comprises various disadvantages such as limited capacity and high costs, the establishment of an indicator for estimating methane production of individual ruminants would provide an alternative to direct methane measurement. Methyl-coenzyme M reductase is involved in methanogenesis and the subunit α of methyl-coenzyme M reductase is encoded by the mcrA gene of rumen archaea. We therefore examined the relationship between methane emissions of Holstein dairy cows measured in respiration chambers with 2 different diets (high- and medium-concentrate diet) and the mcrA DNA and mcrA cDNA abundance determined from corresponding rumen fluid samples. Whole-body methane production per kilogram of dry matter intake and mcrA DNA normalized to the abundance of the rrs gene coding for 16S rRNA correlated significantly when using qmcrA primers. Use of qmcrA primers also revealed linear correlation between mcrA DNA copy number and methane yield. Regression analyses based on normalized mcrA cDNA abundances revealed no significant linear correlation with methane production per kilogram of dry matter intake. Furthermore, the correlations between normalized mcrA DNA abundance and the rumen fluid concentration of acetic and isobutyric acid were positive, whereas the correlations with propionic and lactic acid were negative. These data suggest that the mcrA DNA approach based on qmcrA primers could potentially be a molecular proxy for methane yield after further refinement.

Polymorphisms within the APOBR gene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows

Essentially all high-yielding dairy cows experience a negative energy balance during early lactation leading to increased lipomobilization, which is a normal physiological response. However, a severe energy deficit may lead to high levels of ketone bodies and, subsequently, to subclinical or clinical ketosis. It has previously been reported that the ratio of glycerophosphocholine to phosphocholine in milk is a prognostic biomarker for the risk of ketosis in dairy cattle. It was hypothesized that this ratio reflects the ability to break down blood phosphatidylcholine as a fatty acid resource. In the current study, 248 animals from a previous study were genotyped with Illumina BovineSNP50 BeadChip, and genome-wide association studies were carried out for the milk levels of phosphocholine, glycerophosphocholine, and the ratio of both metabolites. It was demonstrated that the latter two traits are heritable with h2 = 0.43 and h2 = 0.34, respectively. A major quantitative trait locus was identified on cattle chromosome 25. The APOBR gene, coding for the apolipoprotein B receptor, is located within this region and was analyzed as a candidate gene. The analysis revealed highly significant associations of polymorphisms within the gene with glycerophosphocholine as well as the metabolite ratio. These findings support the hypothesis that differences in the ability to take up blood phosphatidylcholine from low-density lipoproteins play an important role in early lactation metabolic stability of dairy cows and indicate APOBR to contain a causative variant.

Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks.

Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information.

Effect of genetic merit for energy balance on luteal activity and subsequent reproductive performance in primiparous Holstein-Friesian cows

Postpartum energy status is critically important to fertility. However, studies dealing with the relationship between both traits are rare and most refer only to the phenotypic level. In this study, random regression models were used to generate cow-specific lactation curves for daily breeding values (BV) of energy balance (EB) to assess the effect of genetic merit for energy status on different traits derived from progesterone profiles and on subsequent reproductive performance of high-producing dairy cows. Individual feed intake, milk yield, and live weight were recorded for lactation d 11 to 180, and EB was estimated on a daily basis. The results provided the basis for the estimation of BV for 824 primiparous Holstein-Friesian cows. For a subset of these cows (n = 334), progesterone profiles for the resumption of ovarian activity were available. Four different traits describing the genetic merit for EB were defined to evaluate their relationship with fertility. Two EB traits referred to the period in which the average daily EB across all cows was negative (d 11 to 55 postpartum), and 2 parameters were designed considering only daily BV for d 11 to 180 in lactation that were negative. We found that cows with a high genetic merit for EB had a significantly earlier resumption of ovarian activity postpartum. Thus, an EB (indicator) trait should be included in future breeding programs to reduce the currently prolonged anovulatory intervals after parturition.

Genetic analysis of production traits in turbot (Scophthalmus maximus) using random regression models based on molecular relatedness

Funding information Bundesanstalt für Landwirtschaft und Ernährung, Grant/Award Number: 2817301810; German Federal Office for Agriculture and Food; H. Wilhelm Schaumann Stiftung Abstract Reliable estimates of genetic parameters for growth traits as a trajectory of age are needed to optimize existing turbot breeding programmes. To evaluate the potential of early selection strategies, the use of biometric body measurements, length (L), width (W) and area (A), at early ages as alternative indicators for the selection trait at harvest was explored. Random regression model (RRM) based on molecular relatedness (MR) was used to analyse the trajectory of genetic parameters for growth traits in turbot from 162 to 756 days posthatch (dph). Heritability estimates for body weight (BW) ranged from 0.34 to 0.54. Heritability estimates for W, A and L were also moderate to high ranging from 0.18 to 0.43. Estimates for L and W declined with age, while those for A increased towards harvest age. Genetic (rG) and phenotypic (rP) correlations between BW and the three morphometric traits L, A and W were estimated using simple bivariate animal models at young (AC1), medium (AC2) and old (AC3) age classes. Correlations between BW and morphometric body traits were high, ranging from 0.7 to 0.9 in all three age groups. Genetic correlations between traits were highest (>0.9) in AC3. To explore the potential for early selection, genetic correlations were derived from the RRM between all days of measurement for all traits separately. From dph 300 onwards, intratrait estimates of rG were moderate to high (above 0.7 for dph 410 and higher ages for traits BW, L and A). Results showed that genetic selection for BW, L and A is promising and that A and L could be successfully used as alternative indicator traits if measurements of BW are not available. Large BW and A at harvest could be achieved as a correlated response to early selection for these traits at around 500 dph.

Technical note: Analytical refinements of the methane indicator archaeol in bovine feces, rumen fluid, and feedstuffs

Archaeol (1,2-di-O-phytanyl-sn-glycerol) is a cell membrane lipid component of methanogens that has the potential to be used as a biomarker for methane production in ruminants. However, its analysis via gas chromatography-mass spectrometry (GC-MS) is challenging because of its molecular size and structure. In this study, 2 different sample preparation methods were tested, Soxhlet and sonication-aided extraction, and the methods were compared for extraction efficiency using the internal standard (IS; 1,2-di-o-hexadecyl-rac-glycerol). The extraction efficiency of the Soxhlet method for fecal archaeol was twice that of sonication. With the use of a high-temperature GC column, the retention times of IS and archaeol were 17.6 and 19.4 min, respectively, with a total run time of only 25 min. The molecule ions m/z 611.4 (IS) and m/z 725.8 (archaeol), or alternatively the fragment ion of the glycerol moiety m/z 130.0, were used for identification and quantification via GC-MS in positive chemical ionization mode. The intra-assay coefficients of variation for fecal archaeol measurements were 1.3% (m/z 725.8) and 2.1% (m/z 130.0) (n=3), respectively. Fecal archaeol quantifications did not differ between the use of the molecule or glycerol moiety ions (paired t-test, n=156). Archaeol concentrations tended to be 3.3% greater in samples stored at -20°C before drying compared with samples that were immediately dried after collection (paired t-test, n=5). The detection limit of archaeol was 0.5 µg/g of fecal dry matter (DM); no archaeol could be detected in feed samples. In different fractions of rumen fluid, archaeol levels ranged from 1.9 to 24.0 µg/g of DM. In 10 cows fed the same grass and corn silage/hay-based ration, diurnal variations of fecal archaeol levels (5 time points over 2 d) were cow dependent and ranged from 26.2 to 77.2 µg/g of DM (mean 48.4 µg/g of DM). Thus, within-animal variation in cows on the same diet was between 4 and 27%. We suggest that this finding is related to the amount and time of the latest feed intake event before the fecal sampling. Feeding pattern can determine the passage rate of digesta through the alimentary tract and thus the duration of contact time of archaea with their substrate.