Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations.
ABSTRACT: The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull's progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r a ) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.
Project description:Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows. Methods:Heat production was computed based on the animal's consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30?h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression. Results:Measured heat production of studied animals ranged between 712 and 1470?kJ/kg BW0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5?kJ/kg BW0.75. The ratio of performance to deviation (RPD) was 1.56. Conclusion:The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.
Project description:Modern genetic improvement in dairy cattle is directed towards improvement of fertility; however, reproduction traits generally exhibit a genetic antagonism with milk yield. Herein, we aimed to clarify the effects of sire predicted transmitting ability (PTA) for daughter pregnancy rate (DPR) on the reproductive performance and milk yield of daughters in Japanese dairy herds. We conducted a retrospective cohort study on four dairy herds in eastern Hokkaido, Japan, using 1,612 records from 1,018 cows with first, second, or third calvings between March 2015 and September 2018. First, we classified sires into three groups based on the tertile value of their DPR estimate: ? -2.2 (low), -2.1 to -0.4 (intermediate), and ? -0.3 (high). Subsequently, we compared the sire PTA estimates, reproductive performance, and milk production among DPR groups for each parity of the daughters. In the first and second parity, the hazard of pregnancy by 200 days postpartum was highest in cows from the high-DPR group (P < 0.05); in the third parity, it was unaffected by DPR group. Although sire PTA for milk production in cows from the low-DPR group was highest, actual milk production was unaffected by DPR group regardless of parity. Our findings demonstrate that using sires with PTA for high fertility can enable farmers to improve reproductive performance without decreasing milk yield in Japanese dairy herds. However, it should be noted that sires with PTA for high fertility are at risk for reducing the genetic merit for milk production.
Project description:OBJECTIVE:This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. METHODS:A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. RESULTS:The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. CONCLUSION:More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.
Project description:This experiment was conducted to evaluate the impact of yeast and lactic acid bacteria (LAB) on mastitis and milk microbiota composition of dairy cows. Thirty lactating Holstein cows with similar parity, days in milk were randomly assigned to five treatments, including: (1) Health cows with milk SCC?<?500,000 cells/mL, no clinical signs of mastitis were found, fed basal total mixed ration (TMR) without supplementation (H); (2) Mastitis cows with milk SCC?>?500,000 cells/mL, fed basal TMR without supplementation (M); (3) Mastitis cows fed basal TMR supplemented with 8 g day-1 yeast (M?+?Y); (4) Mastitis cows fed basal TMR supplemented with 8 g day-1 LAB (M?+?L); (5) Mastitis cows (milk SCC?>?500,000 cells/mL) fed basal TMR supplemented with 4 g day-1 yeast and 4 g day-1 LAB (M?+?Y?+?L). Blood and milk sample were collected at day 0, day 20 and day 40. The results showed efficacy of probiotic: On day 20 and day 40, milk SCC in H, M?+?Y, M?+?L, M?+?Y?+?L was significantly lower than that of M (P?<?0.05). Milk concentration of TNF-?, IL-6 and IL-1? in M?+?Y?+?L were significantly reduced compared with that of M on day 40 (P?<?0.05). Milk Myeloperoxidase (MPO) and N-Acetyl-?-D-Glucosaminidase (NAG) activity of M?+?Y, M?+?L, M?+?L?+?Y were lower than that of M on day 40 (P?<?0.05). At genus level, Staphylococcus, Chryseobacterium and Lactococcus were dominant. Supplementation of LAB decreased abundance of Enterococcus and Streptococcus, identified as mastitis-causing pathogen. The results suggested the potential of LAB to prevent mastitis by relieving mammary gland inflammation and regulating milk microorganisms.
Project description:BACKGROUND: Substantial gene substitution effects on milk production traits have formerly been reported for alleles at the K232A and the promoter VNTR loci in the bovine acylCoA-diacylglycerol-acyltransferase 1 (DGAT1) gene by using data sets including sires with accumulated phenotypic observations of daughters (breeding values, daughter yield deviations). However, these data sets prevented analyses with respect to dominance or parent-of-origin effects, although an increasing number of reports in the literature outlined the relevance of non-additive gene effects on quantitative traits. RESULTS: Based on a data set comprising German Holstein cows with direct trait measurements, we first confirmed the previously reported association of DGAT1 promoter VNTR alleles with milk production traits. We detected a dominant mode of effects for the DGAT1 K232A and promoter VNTR alleles. Namely, the contrasts between the effects of heterozygous individuals at the DGAT1 loci differed significantly from the midpoint between the effects for the two homozygous genotypes for several milk production traits, thus indicating the presence of dominance. Furthermore, we identified differences in the magnitude of effects between paternally and maternally inherited DGAT1 promoter VNTR - K232A haplotypes indicating parent-of-origin effects on milk production traits. CONCLUSION: Non-additive effects like those identified at the bovine DGAT1 locus have to be accounted for in more specific QTL detection models as well as in marker assisted selection schemes. The DGAT1 alleles in cattle will be a useful model for further investigations on the biological background of non-additive effects in mammals due to the magnitude and consistency of their effects on milk production traits.
Project description:Our initial RNA sequencing (RNA-seq) revealed that the Serum amyloid A1 (SAA1) gene was differentially expressed in the mammary glands of lactating Holstein cows with extremely high versus low phenotypic values of milk protein and fat percentage. To further validate the genetic effect and potential molecular mechanisms of SAA1 gene involved in regulating milk production traits in dairy cattle, we herein performed a study through genotype-phenotype associations. Six identified SNPs were significantly associated with one or more milk production traits (0.00002< P < 0.0025), providing additional evidence for the potential role of SAA1 variants in milk production traits in dairy cows. Subsequently, both luciferase assay and electrophoretic mobility shift assay (EMSA) clearly demonstrated that the allele A of g.-963C>A increased the promoter activity by binding the PARP factor while allele C did not. Bioinformatics analysis indicated that the secondary structure of SAA protein changed by the substitution A/G in the locus c. +2510A>G. Our findings were the first to reveal the significant associations of the SAA1 gene with milk production traits, providing basis for further biological function validation, and two identified SNPs, g.-963C>A and c. +2510A>G, may be considered as genetic markers for breeding in dairy cattle.
Project description:Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long.Here, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies.The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.
Project description:BACKGROUND: Milkability is a complex trait that is characterized by milk flow traits including average milk flow rate, maximum milk flow rate and total milking time. Milkability has long been recognized as an economically important trait that can be improved through selection. By improving milkability, management costs of milking decrease through reduced labor and improved efficiency of the automatic milking system, which has been identified as an important factor affecting net profit. The objective of this study was to identify markers associated with electronically measured milk flow traits, in the Italian Brown Swiss population that could potentially improve selection based on genomic predictions. RESULTS: Sires (n = 1351) of cows with milk flow information were genotyped for 33,074 single nucleotide polymorphism (SNP) markers distributed across 29 Bos taurus autosomes (BTA). Among the six milk flow traits collected, ascending time, time of plateau, descending time, total milking time, maximum milk flow and average milk flow, there were 6,929 (time of plateau) to 14,585 (maximum milk flow) significant SNP markers identified for each trait across all BTA. Unique regions were found for each of the 6 traits providing evidence that each individual milk flow trait offers distinct genetic information about milk flow. This study was also successful in identifying functional processes and genes associated with SNPs that influences milk flow. CONCLUSIONS: In addition to verifying the presence of previously identified milking speed quantitative trait loci (QTL) within the Italian Brown Swiss population, this study revealed a number of genomic regions associated with milk flow traits that have never been reported as milking speed QTL. While several of these regions were not associated with a known gene or QTL, a number of regions were associated with QTL that have been formerly reported as regions associated with somatic cell count, somatic cell score and udder morphometrics. This provides further evidence of the complexity of milk flow traits and the underlying relationship it has with other economically important traits for dairy cattle. Improved understanding of the overall milking pattern will aid in identification of cows with lower management costs and improved udder health.
Project description:A widely used method for prediction of complex traits in animal and plant breeding is "genomic best linear unbiased prediction" (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information available. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple (relative to a fully Bayesian analysis) to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo sampling and model hyper-parameters, viewed here as regularization "knobs," are tuned via some cross-validation. Uncertainty of predictions are evaluated by employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation and generalization.
Project description:Aim:The study aimed to identify fatty acid synthase (FASN), LOC514211, and fat mass and obesity-associated (FTO) gene polymorphisms and to investigate their associations with milk traits in an Indonesian-Holstein dairy cow population. Materials and Methods:A total of 100 Indonesian-Holstein cows consisting of 50 heads (0th generation; G0) and 50 heads of their daughters (1st generation; G1) were used. Polymerase chain reaction-restriction fragment length polymorphism was performed to genotype three single nucleotide polymorphisms: rs41919985 in the FASN gene, rs42688595 in the LOC514211 gene, and g.1371T>A in the FTO gene. Results:FASN rs41919985 was associated with milk protein percentage (p<0.05), FTO g.1371T>A was associated with milk fat percentage (p<0.05), and LOC514211 rs42688595 was not associated with any trait (p>0.05). Heterozygote variants showed a higher protein percentage for FASN and the highest fat percentage for FTO. These associations were consistent in the G0 and G1 populations. Conclusion:Our results indicate that the milk protein and fat percentages can be improved by increasing the frequency of the AG genotype of FASN and the AT genotype of FTO, respectively.