Genome-wide scan to detect quantitative trait loci for milk urea nitrogen in Dutch Holstein-Friesian cows

Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects 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 about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.

Effect of Temperature and Humidity on Milk Urea Nitrogen Concentration

This study investigated the effect of ambient temperature and humidity on milk urea nitrogen (MUN) concentration in Holstein cows. Meteorological data corresponding to the dates of milk sampling were collected over six years. A linear mixed-effects model including a random effect term for cow identification was used to assess whether temperature and humidity were predictive of MUN concentration. Age, days in milk, temperature humidity index (THI), ration, milk yield, parity and somatic cell count were also evaluated as main effects in the model. A general linear model including all variables as random effects was then fitted to assess the contribution of each variable towards the variability in MUN concentration. Maximum daily temperature and humidity on the sampling day were positively associated with MUN concentration, but their interaction term was negatively associated, indicating that their effects were not independent and additive. Variables that contributed the most to the variability of MUN concentration were dietary crude protein (21%), temperature (18%) and other factors (24%) that were not assessed in the model (error term). Temperature has a significant influence on urea nitrogen concentration and should therefore always be considered when urea nitrogen concentration data are used to make inferences about the dietary management of dairy 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.

Identification of Genomic Regions Influencing N-Metabolism and N-Excretion in Lactating Holstein- Friesians

Excreted nitrogen (N) of dairy cows contribute to environmental eutrophication. The main N-excretory metabolite of dairy cows is urea, which is synthesized as a result of N-metabolization in the liver and is excreted via milk and urine. Genetic variation in milk urea (MU) has been postulated but the complex physiology behind the trait as well as the tremendous diversity of processes regulating the N-metabolism impede the consistent determination of causal regions in the bovine genome. In order to map the genetic determinants affecting N-excretion, MU and eight other N-excretory metabolites in milk and urine were assessed in a genome-wide association study. Therefore phenotypes of 371 Holstein- Friesians were obtained in a trial on a dairy farm under near commercial conditions. Genotype data comprised SNP information of the Bovine 50K MD Genome chip (45,613 SNPs). Significantly associated genomic regions for MU concentration revealed GJA1 (BTA 9), RXFP1, and FRY1 (both BTA 12) as putative candidates. For milk urea yield (MUY) a promising QTL on BTA 17 including SH3D19 emerged, whereas RCAN2, CLIC5, ENPP4, and ENPP5 (BTA 23) are suggested to influence urinary urea concentration. Minor N-fractions in milk (MN) may be regulated by ELF2 and SLC7A11 (BTA 17), whilst ITPR2 and MYBPC1 (BTA 5), STIM2 (BTA 6), SGCD (BTA 7), SLC6A2 (BTA 18), TMCC2 and MFSD4A (BTA 16) are suggested to have an impact on various non-urea-N (NUN) fractions excreted via urine. Our results highlight genomic regions and candidate genes for N-excretory metabolites and provide a deeper insight into the predisposed component to regulate the N-metabolism in dairy cows.

Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10⁻⁶ were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.

Effect of different levels of dietary nitrogen supplementation on the relative blood urea nitrogen concentration of beef cows

The objective of this study was to determine if individual beef cows in a herd have an inherent ability to maintain their blood urea nitrogen (BUN) concentration when exposed to different levels of dietary nitrogen supplementation. Ten Hereford and 12 Nguni cows, aged between 2 and 16 years, were utilized in two crossover experiments. In the first experiment, cows were exposed to two diets: a balanced diet with a crude protein (CP) level of 7.9% and a modified diet with a CP level of 14%, formulated by adding 20 kg of feed grade urea per ton of the balanced diet. At the end of the first crossover experiment, cows received the balanced diet for 1 week. The second component utilized the same cows wherein they were fed the balanced diet in addition to another modified diet containing only 4.4% CP. Blood urea nitrogen concentration was measured 22 times (twice weekly) from each cow during both components of the study. A linear mixed-effects model was used to assess whether baseline BUN concentration (measured 1 week before onset of the study) was predictive of subsequent BUN concentration in individual cows. Breed, cow age, body condition score, and body mass were also evaluated for their effects on BUN concentrations. Albumin, beta hydroxybutyric acid (BHBA), glucose, and total serum protein (TSP) were compared between diets within each breed. Baseline BUN concentration was a significant predictor of subsequent BUN concentration in individual cows (P = 0.004) when evaluated over both components of the study. Breed (P = 0.033), the preceding diet (P < 0.001), current diet (P < 0.001), and the week during which sampling was performed (P < 0.001) were also associated with BUN concentration. Results suggest that beef cattle (within a herd) have an inherent ability to maintain their BUN concentration despite fluctuations in levels of available dietary nitrogen.

Systematic microRNAome profiling reveals the roles of microRNAs in milk protein metabolism and quality: insights on low-quality forage utilization

In this study, we investigated the molecular regulatory mechanisms of milk protein production in dairy cows by studying the miRNAomes of five key metabolic tissues involved in protein synthesis and metabolism from dairy cows fed high- and low-quality diets. In total, 340, 338, 337, 330, and 328 miRNAs were expressed in the rumen, duodenum, jejunum, liver, and mammary gland tissues, respectively. Some miRNAs were highly correlated with feed and nitrogen efficiency, with target genes involved in transportation and phosphorylation of amino acid (AA). Additionally, low-quality forage diets (corn stover and rice straw) influenced the expression of feed and nitrogen efficiency-associated miRNAs such as miR-99b in rumen, miR-2336 in duodenum, miR-652 in jejunum, miR-1 in liver, and miR-181a in mammary gland. Ruminal miR-21-3p and liver miR-2285f were predicted to regulate AA transportation by targeting ATP1A2 and SLC7A8, respectively. Furthermore, bovine-specific miRNAs regulated the proliferation and morphology of rumen epithelium, as well as the metabolism of liver lipids and branched-chain AAs, revealing bovine-specific mechanisms. Our results suggest that miRNAs expressed in these five tissues play roles in regulating transportation of AA for downstream milk production, which is an important mechanism that may be associated with low milk protein under low-quality forage feed.

Pre-breeding blood urea nitrogen concentration and reproductive performance of Bonsmara heifers within different management systems

This study investigated the association between pre-breeding blood urea nitrogen (BUN) concentration and reproductive performance of beef heifers within different management systems in South Africa. Bonsmara heifers (n = 369) from five herds with different estimated levels of nitrogen intake during the month prior to the commencement of the breeding season were sampled in November and December 2010 to determine BUN concentrations. Body mass, age, body condition score (BCS) and reproductive tract score (RTS) were recorded at study enrolment. Trans-rectal ultrasound and/or palpation was performed 4-8 weeks after a 3-month breeding season to estimate the stage of pregnancy. Days to pregnancy (DTP) was defined as the number of days from the start of the breeding season until the estimated conception date. Logistic regression and Cox proportional hazards survival analysis were performed to estimate the association of pre-breeding BUN concentration with subsequent pregnancy and DTP, respectively. After stratifying for herd and adjusting for age, heifers with relatively higher pre-breeding BUN concentration took longer to become pregnant when compared to those with relatively lower BUN concentration (P = 0.011). In the herd with the highest estimated nitrogen intake (n = 143), heifers with relatively higher BUN were less likely to become pregnant (P = 0.013) and if they did, it was only later during the breeding season (P = 0.017), after adjusting for body mass. These associations were not present in the herd (n = 106) with the lowest estimated nitrogen intake (P > 0.500). It is concluded that Bonsmara heifers with relatively higher pre-breeding BUN concentration, might be at a disadvantage because of this negative impact on reproductive performance, particularly when the production system includes high levels of nitrogen intake.