Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BACKGROUND

Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.

RESULTS

The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.

CONCLUSIONS

Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

Associations between serum health biomarker concentrations and reproductive performance, accounting for milk yield, in pasture-based Holstein cows in southeastern Australia

In this single cohort study, we investigated associations between the concentrations of a suite of serum biomarkers measured in the first 30 d of lactation and subsequent reproductive performance measured as mating start date to conception intervals, in pasture-based Holstein cows. A secondary objective was to examine associations between biomarker concentrations and 305-d milk yield to assess whether any positive associations between biomarker concentration and reproductive performance were explained by reduced milk production. The data used had been collected as part of an ongoing project from 2017 to 2020 to compile a data set from a large population of lactating dairy cows. Biomarkers measured were those associated with energy balance (β-hydroxybutyrate [BHB] and nonesterified fatty acids [NEFA]), protein nutritional status (urea and albumin), immune status (globulin, albumin to globulin ratio and haptoglobin), and macromineral status (calcium and magnesium). Associations between biomarker concentrations and mating start date to conception interval were investigated using Cox proportional hazard models, using between 634 and 1,121 lactations (varying by biomarker) from 632 to 1,103 cows and 11 to 17 mating periods from 10 to 13 herds. Based on hazard ratio (HR) estimates and associated 95% confidence intervals (CI), hazard of conception on any particular day of the herds' mating periods was positively associated with the concentrations of albumin (HR = 1.09; 95% CI: 1.05-1.12), albumin to globulin ratio (HR = 2.82; 95% CI: 1.66-4.79), calcium (HR = 2.01; 95% CI: 1.18-3.43), and magnesium (HR = 2.17; 95% CI: 1.01-4.66), and negatively associated with globulin concentration (HR = 0.98; 95% CI: 0.97 to 1.00). There was also some evidence that NEFA concentration was negatively associated (HR = 0.76; 95% CI: 0.57 to 1.01), and urea concentration positively associated (HR = 1.05; 95% CI: 0.99 to 1.11), with reproductive performance, but no evidence that BHB and haptoglobin concentrations were associated with reproductive performance. Except for NEFA, presence and direction of the associations between the biomarker and milk yield were not discordant with that for reproductive performance. Also, except for NEFA, we found no substantial evidence of nonlinear relationships between biomarker concentration and either reproductive performance or milk yield. Correlations between biomarker concentrations were generally weak, indicating that multibiomarker panels may collectively predict reproductive performance better than any single biomarker. We noted substantial variation in the concentrations of all biomarkers within, and for some biomarkers, between herd-year groups. Collectively, these results indicate that there may be scope to improve biomarker concentrations through nutritional, management, and genetic interventions, and by association, reproductive performance and milk yield may also improve.

Investigation of β-hydroxybutyrate in early lactation of Simmental cows: Genetic parameters and genomic predictions

Genomic information allows for a more accurate calculation of relationships among animals than the pedigree information, leading to an increase in accuracy of breeding values. Here, we used pedigree-based and single-step genomic approaches to estimate variance components and breeding values for β-hydroxybutyrate milk content (BHB). Additionally, we performed a genome-wide association study (GWAS) to depict its genetic architecture. BHB concentrations within the first 90 days of lactation, estimated from milk medium infrared spectra, were available for 30,461 cows (70,984 records). Genotypes at 42,152 loci were available for 9,123 animals. Low heritabilities were found for BHB using pedigree-based (0.09 ± 0.01) and genomic (0.10 ± 0.01) approaches. Genetic correlation between BHB and milk traits ranged from -0.27 ± 0.06 (BHB and protein percentage) to 0.13 ± 0.07 (BHB and fat-to-protein ratio) using pedigree and from -0.26 ± 0.05 (BHB and protein percentage) to 0.13 ± 0.06 (BHB and fat-to-protein ratio) using genomics. Breeding values were validated for 344 genotyped cows using linear regression method. The genomic EBV (GEBV) had greater accuracy (0.51 vs. 0.45) and regression coefficient (0.98 vs. 0.95) compared to EBV. The correlation between two subsequent evaluations, without and with phenotypes for validation cows, was 0.85 for GEBV and 0.82 for EBV. Predictive ability (correlation between (G)EBV and adjusted phenotypes) was greater when genomic information was used (0.38) than in the pedigree-based approach (0.31). Validation statistics in the pairwise two-trait models (milk yield, fat and protein percentage, urea, fat/protein ratio, lactose and logarithmic transformation of somatic cells count) were very similar to the ones highlighted for the single-trait model. The GWAS allowed discovering four significant markers located on BTA20 (57.5-58.2 Mb), where the ANKH gene is mapped. This gene has been associated with lactose, alpha-lactalbumin and BHB. Results of this study confirmed the usefulness of genomic information to provide more accurate variance components and breeding values, and important insights about the genomic determination of BHB milk content.

Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows

The onset of lactation results in a sudden irreversible loss of Ca for colostrum and milk synthesis. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, whereas a larger proportion of cows develop subclinical hypocalcemia that predisposes them to other peripartum diseases. The objective of this study was to perform a comprehensive genomic analysis of blood total Ca concentration in periparturient Holstein cows. We first performed a genomic scan and a subsequent gene-set analysis to identify candidate genes, biological pathways, and molecular mechanisms affecting postpartum Ca concentration. Then, we assessed the prediction of postpartum Ca concentration using genomic information. Data consisted of 7,691 records of plasma or serum concentrations of Ca measured in the first, second, and third day after parturition of 959 primiparous and 1,615 multiparous cows that calved between December 2015 and June 2020 in 2 dairy herds. All cows were genotyped with 80k SNPs. The statistical model included lactation (1 to 5+), calf category (male, females, twins), and day as fixed effects, and season-treatment-experiment, animal, and permanent environmental as random effects. Model predictive ability was evaluated using 10-fold cross-validation. Heritability and repeatability estimates were 0.083 (standard error = 0.017) and 0.444 (standard error = 0.028). The association mapping identified 2 major regions located on Bos taurus autosome (BTA)6 and BTA16 that explained 1.2% and 0.7% of additive genetic variance of Ca concentration, respectively. Interestingly, the region on BTA6 harbors the GC gene, which encodes the vitamin D binding protein, and the region on BTA16 harbors LRRC38, which is actively involved in K transport. Other sizable peaks were identified on BTA5, BTA2, BTA7, BTA14, and BTA9. These regions harbor genes associated with Ca channels (CACNA1S, CRACR2A), K channels (KCNK9), bone remodeling (LRP6), and milk production (SOCS2). The gene-set analysis revealed terms related to vitamin transport, calcium ion transport, calcium ion binding, and calcium signaling. Genomic predictions of phenotypic and genomic estimated breeding values of Ca concentration yielded predictive correlations up to 0.50 and 0.15, respectively. Overall, the present study contributes to a better understanding of the genetic basis of postpartum blood Ca concentration in Holstein cows. In addition, the findings may contribute to the development of novel selection and management strategies for reducing periparturient hypocalcemia in dairy cattle.

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R²) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R² values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R² remained comparable, however, the root mean squared errors increased substantially (4-10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.

Genetic parameters of blood urea nitrogen and milk urea nitrogen concentration in dairy cattle managed in pasture-based production systems of New Zealand and Australia

Context Urinary nitrogen excretion by grazing cattle causes environmental pollution. Selecting for cows with a lower concentration of urinary nitrogen excretion may reduce the environmental impact. While urinary nitrogen excretion is difficult to measure, blood urea nitrogen (BUN), mid-infrared spectroscopy (MIR)-predicted BUN (MBUN), which is predicted from MIR spectra measured on milk samples, and milk urea nitrogen (MUN) are potential indicator traits. Australia and New Zealand have increasing datasets of cows with urea records, with 18 120 and 15 754 cows with urea records in Australia and New Zealand respectively. A collaboration between Australia and New Zealand could further increase the size of the dataset by sharing data. Aims Our aims were to estimate genetic parameters for urea traits within country, and genetic correlations between countries to gauge the benefit of having a joint reference population for genomic prediction of an indicator trait that is potentially suitable for selection to reduce urinary nitrogen excretion for both countries. Methods Genetic parameters were estimated within country (Australia and New Zealand) in Holstein, Jersey and a multibreed population, for BUN, MBUN and MUN in Australia and MUN in New Zealand, using high-density genotypes. Genetic correlations were also estimated between the urea traits recorded in Australia and MUN in New Zealand. Analyses used the first record available for each cow or within days-in-milk (DIM) intervals. Key results Heritabilities ranged from 0.08 to 0.32 for the various urea traits. Higher heritabilities were obtained for Jersey than for Holstein, and for the New Zealand cows than for the Australian cows. While urea traits were highly correlated within Australia (0.71–0.94), genetic correlations between Australia and New Zealand were small to moderate (0.08–0.58). Conclusions Our results showed that the heritability for urea traits differs among trait, breed, and country. While urea traits are highly correlated within country, genetic correlations between urea traits in Australia and MUN in New Zealand were only low to moderate. Implications Further study is required to identify the underlying causes of the difference in heritabilities observed, to compare the accuracies of different reference populations, and to estimate genetic correlations between urea traits and other traits such as fertility and feed intake. Larger datasets may help estimate genetic correlations more accurately between countries.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.

Genome-Wide Association Studies for the Concentration of Albumin in Colostrum and Serum in Chinese Holstein

Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips (containing 140,668 single nucleotide polymorphisms; SNPs). After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs (false discovery rate (FDR) at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.

A Tale of Two Biomarkers: Untargeted 1H NMR Metabolomic Fingerprinting of BHBA and NEFA in Early Lactation Dairy Cows

Disorders of energy metabolism, which can result from a failure to adapt to the period of negative energy balance immediately after calving, have significant negative effects on the health, welfare and profitability of dairy cows. The most common biomarkers of energy balance in dairy cows are β-hydroxybutyrate (BHBA) and non-esterified fatty acids (NEFA). While elevated concentrations of these biomarkers are associated with similar negative health and production outcomes, the phenotypic and genetic correlations between them are weak. In this study, we used an untargeted ¹H NMR metabolomics approach to investigate the serum metabolomic fingerprints of BHBA and NEFA. Serum samples were collected from 298 cows in early lactation (calibration dataset N = 248, validation N = 50). Metabolomic fingerprinting was done by regressing ¹H NMR spectra against BHBA and NEFA concentrations (determined using colorimetric assays) using orthogonal partial least squares regression. Prediction accuracies were high for BHBA models, and moderately high for NEFA models (R² of external validation of 0.88 and 0.75, respectively). We identified 16 metabolites that were significantly (variable importance of projection score > 1) correlated with the concentration of one or both biomarkers. These metabolites were primarily intermediates of energy, phospholipid, and/or methyl donor metabolism. Of the significant metabolites identified; (1) two (acetate and creatine) were positively correlated with BHBA but negatively correlated with NEFA, (2) nine had similar associations with both BHBA and NEFA, (3) two were correlated with only BHBA concentration, and (4) three were only correlated with NEFA concentration. Overall, our results suggest that BHBA and NEFA are indicative of similar metabolic states in clinically healthy animals, but that several significant metabolic differences exist that help to explain the weak correlations between them. We also identified several metabolites that may be useful intermediate phenotypes in genomic selection for improved metabolic health.

Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows

At the beginning of lactation, high-producing cows commonly experience an unbalanced energy status that is often responsible for the onset of metabolic disorders and impaired health and performance. Blood β-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are indicators of excessive fat mobilization and circulating ketone bodies. Recently, prediction models based on mid-infrared (MIR) spectroscopy have been developed to assess blood BHB and NEFA from routinely collected individual milk samples. This study aimed to estimate genetic parameters of blood BHB and NEFA predicted from milk MIR spectra and to assess their phenotypic and genetic correlations with milk production and composition traits in early-lactation Holstein cows. The data set comprised the first test-day record within lactation and spectra of individual milk samples (n = 22,718) of 13,106 Holstein cows collected from 5 to 35 d in milk (DIM). Blood BHB and NEFA were predicted from milk MIR spectra using previously developed prediction models. Genetic parameters of blood metabolites and milk traits were estimated for the whole observational period (5-35 DIM) and within 6 classes of DIM. Blood BHB and NEFA showed similar genetic variation across DIM, with the highest heritability in the first 10 d after calving (0.31 ± 0.06 and 0.19 ± 0.05 for BHB and NEFA, respectively). The genetic correlation between BHB and NEFA was moderate (0.51 ± 0.05). Genetic correlations of BHB with milk yield, SCS, protein percentage, lactose percentage, and urea nitrogen content were similar to, or at least in the same direction as, the correlations of NEFA with the same traits, whereas opposite correlations were observed with fat percentage and fat-to-protein ratio. Results of the current study suggest that blood BHB and NEFA predicted from milk MIR spectra have genetic variation that is potentially exploitable for breeding purposes. Therefore, they could be used as indicator traits of hyperketonemia in a selection index aimed to reduce the susceptibility of dairy cows to metabolic disorders in early lactation.