Single-step genome-wide association analyses for milk urea concentration in Walloon Holstein cows

The aims of this study were to conduct a single-step genome-wide association to identify genomic regions associated with milk urea (MU) and to estimate genetic correlations between MU and milk yield (MY), milk composition (calcium content [CC], fat percentage [FP], protein percentage [PP], and casein percentage [CNP]), and cheese-making properties (CMP; coagulation time [CT], curd firmness after 30 min from rennet addition [a30], and titratable acidity [TA]). The used data have been collected from 2015 to 2020 on 78,073 first-parity (485,218 test-day records) and 48,766 second-parity (284,942 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 SNP located on 29 BTA of 6,617 animals (1,712 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 kb) was calculated, and the top-3 genomic regions explaining the largest rate of the genetic variance were considered promising regions and used to identify potential candidate genes. Mean (SD) MU was 25.38 (8.02) mg/dL and 25.03 (8.06) mg/dL in the first and second lactation, respectively. Mean heritability estimates for daily MU were 0.21 and 0.23 for the first and second lactation, respectively. The genetic correlations estimated between MU and MY, milk composition, and CMP were quite low (ranged from -0.10 [CC] to 0.10 [TA] and -0.05 [CT] to 0.13 [TA] for the first and second lactations, respectively). The top-3 regions associated with MU were located from 80.61 to 80.74 Mb on BTA6, 103.26 to 103.41 Mb on BTA11, and 1.59 to 2.15 Mb on BTA14. Genes including KCNT1, MROH1, SHARPIN, TSSK5, CPSF1, HSF1, TONSL, DGAT1, SCX, and MAF1 were identified as positional candidate genes for MU. The findings of this study can be used for a better understanding of the genomic architecture underlying MU in Holstein cattle.

Estimation of heritability for milk urea and genetic correlations with milk production traits in 3 Danish dairy breeds

The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and -0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management.

Relationship between milk urea content and important milk traits in Holstein cattle

Breeding cattle with low nitrogen emissions has been proposed as a countermeasure against eutrophication due to dairy production. Milk urea content (MU) could potentially serve as a new readily measured indicator trait for nitrogen emissions by cows. Therefore, we estimated genetic parameters related to MU and its relationship with other milk traits. We analysed 4 178 735 milk samples collected between January 2008 and June 2019 from 261 866 German Holstein dairy cows during their first, second, and third lactations. Restricted maximum likelihood estimation was conducted using univariate and bivariate random regression sire models in WOMBAT. We obtained moderate average daily heritability estimates for the daily MU of 0.24 in first lactation cows, 0.23 in second lactation cows, and 0.21 in third lactation cows with average daily genetic SDs of 25.16 mg/kg, 24.93 mg/kg, and 23.75 mg/kg, respectively. Averaged over days in milk, the repeatability estimates were low at 0.41 in first, second, and third lactation cows. A strong positive genetic correlation was found between MU and milk urea yield (MUY; 0.72 on average). In addition, 305-day heritabilities were estimated as 0.50, 0.52, and 0.50 in first, second, and third lactation cows, respectively, with genetic correlations of 0.94 or higher for MU in different lactations. By contrast, the averaged estimates of the genetic correlations between MU and other milk traits were low (-0.07 to 0.15). Moderate heritability estimates clearly allow the possible selection for MU, and the near-zero estimates of genetic correlations indicate no risk of undesired correlated selection responses in other milk traits. However, a relationship still needs to be established between MU as an indicator trait and the target trait, defined as total individual nitrogen emissions.

Genome-wide association for milk urea concentration in Dual-Purpose Belgian Blue cows

The objectives of this study were to estimate genetic parameters and identify genomic regions associated with milk urea concentration (MU) in Dual-Purpose Belgian Blue (DPBB) cows. The data were 29,693 test-day records of milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP) and MU collected between 2014 and 2020 on 2498 first parity cows (16,935 test-day records) and 1939 second-parity cows (12,758 test-day records) from 49 herds in the Walloon Region of Belgium. Data of 28,266 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), on 1699 animals (639 males and 1060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method using a single chain of 100,000 iterations after a burn-in period of 20,000. SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 consecutive SNPs (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. The mean (SD) of MU was 22.89 (10.07) and 22.35 (10.07) mg/dl for first and second parity, respectively. The mean (SD) heritability estimates for daily MU were 0.18 (0.01) and 0.22 (0.02), for first and second parity, respectively. The mean (SD) genetic correlations between daily MU and MY, FY, PY, FP and PP were -0.05 (0.09), -0.07 (0.11), -0.03 (0.13), -0.05 (0.08) and -0.03 (0.11) for first parity, respectively. The corresponding values estimated for second parity were 0.02 (0.10), -0.02 (0.09), 0.02 (0.08), -0.08 (0.06) and -0.05 (0.05). The genome-wide association analyses identified three genomic regions (BTA2, BTA3 and BTA13) associated with MU. The identified regions showed contrasting results between parities and among different stages within each parity. This suggests that different groups of candidate genes underlie the phenotypic expression of MU between parities and among different lactation stages within a parity. The results of this study can be used for future implementation and use of genomic evaluation to reduce MU in DPBB cows.

Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen

Blood urea nitrogen (BUN) is an indicator trait for urinary nitrogen excretion. Measuring BUN level requires a blood sample, which limits the number of records that can be obtained. Alternatively, BUN can be predicted using mid-infrared (MIR) spectroscopy of a milk sample and thus records become available on many more cows through routine milk recording processes. The genetic correlation between MIR predicted BUN (MBUN) and BUN is 0.90. Hence, genetically, BUN and MBUN can be considered as the same trait. The objective of our study was to perform genome-wide association studies (GWAS) for BUN and MBUN, compare these two GWAS and detect quantitative trait loci (QTL) for both traits, and compare the detected QTL with previously reported QTL for milk urea nitrogen (MUN). The dataset used for our analyses included 2098 and 18,120 phenotypes for BUN and MBUN, respectively, and imputed whole-genome sequence data. The GWAS for MBUN was carried out using either the full dataset, the 2098 cows with records for BUN, or 2000 randomly selected cows, so that the dataset size is comparable to that for BUN. The GWAS results for BUN and MBUN were very different, in spite of the strong genetic correlation between the two traits. We detected 12 QTL for MBUN, on bovine chromosomes 2, 3, 9, 11, 12, 14 and X, and one QTL for BUN on chromosome 13. The QTL detected on chromosomes 11, 14 and X overlapped with QTL detected for MUN. The GWAS results were highly sensitive to the subset of records used. Hence, caution is warranted when interpreting GWAS based on small datasets, such as for BUN. MBUN may provide an attractive alternative to perform a more powerful GWAS to detect QTL for BUN.

GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle

Background

Urinary nitrogen leakage is an environmental concern in dairy cattle. Selection for reduced urinary nitrogen leakage may be done using indicator traits such as milk urea nitrogen (MUN). The result of a previous study indicated that the genetic correlation between MUN in Australia (AUS) and MUN in New Zealand (NZL) was only low to moderate (between 0.14 and 0.58). In this context, an alternative is to select sequence variants based on genome-wide association studies (GWAS) with a view to improve genomic prediction accuracies. A GWAS can also be used to detect quantitative trait loci (QTL) associated with MUN. Therefore, our objectives were to perform within-country GWAS and a meta-GWAS for MUN using records from up to 33,873 dairy cows and imputed whole-genome sequence data, to compare QTL detected in the GWAS for MUN in AUS and NZL, and to use sequence variants selected from the meta-GWAS to improve the prediction accuracy for MUN based on a joint AUS-NZL reference set.

Results

Using the meta-GWAS, we detected 14 QTL for MUN, located on chromosomes 1, 6, 11, 14, 19, 22, 26 and the X chromosome. The three most significant QTL encompassed the casein genes on chromosome 6, PAEP on chromosome 11 and DGAT1 on chromosome 14. We selected 50,000 sequence variants that had the same direction of effect for MUN in AUS and MUN in NZL and that were most significant in the meta-analysis for the GWAS. The selected sequence variants yielded a genetic correlation between MUN in AUS and MUN in NZL of 0.95 and substantially increased prediction accuracy in both countries.

Conclusions

Our results demonstrate how the sharing of data between two countries can increase the power of a GWAS and increase the accuracy of genomic prediction using a multi-country reference population and sequence variants selected based on a meta-GWAS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00707-9.