Somatic Cells Count and Its Genetic Association with Milk Yield in Dairy Cattle Raised under Thai Tropical Environmental Conditions

Polymorphisms of the IL-17A Gene Influence Milk Production Traits and Somatic Cell Score in Chinese Holstein Cows

The cow’s milk production characteristics are a significant economic indicator in the livestock industry. Serum cytokines such as interleukin-17 (IL-17) may be potential indicators for bovine mastitis concerning the milk somatic cell count (SCC) and somatic cell score (SCS). The current study aims to find previously undiscovered single nucleotide polymorphisms in the bovine (IL-17A) gene and further investigates their associations with milk production traits in Chinese Holstein cows. Twenty Chinese Holstein cows were randomly chosen from six farms in Jiangsu Province, China. The DNA was extracted from selected samples of bloods for PCR amplification Sequence analyses were used to find SNPs in the bovine (IL-17A) gene. The discovered five SNPs are g-1578A>G, g-1835G>A, and g-398T>A in the 5′UTR; g3164T>C and g3409G>C in the exon region. The genotyping of Holstein cows (n = 992) was performed based on Sequenom Mass ARRAY and SNP data. The connection between SNPs, milk production variables, and the somatic cell score was investigated using the least-squares method. Based on the results, SNP g-398T>A had a significant linkage disequilibrium with g3164T>C. SNPs were found to have significant (p < 0.05) correlations with the test-day milk yield. In conclusion, IL-17A affects cow’s milk production traits significantly.

Polymorphisms of the ACSL1 Gene Influence Milk Production Traits and Somatic Cell Score in Chinese Holstein Cows

Simple Summary

Milk production traits of cows are important economic indicators of the livestock industry. Many dairy farms strive to improve the quality of their milk. Long-chain acyl-CoA synthetase 1 (ACSL1) is a gene related to lipid metabolism. It is widely found in various organisms and can affect fat content and protein content in milk. Single nucleotide polymorphisms (SNP) refers to the polymorphism of DNA sequence caused by a single nucleotide variation at the gene level, which plays a vital function in the genetic study of milk production traits in dairy cows. Our study identified six SNPs of the ACSL1 gene in Chinese Holstein cows, which were related to milk yield, milk fat content, milk protein content and somatic cell score (SCS) to some extent. In summary, the pleiotropic effects of bovine ACSL1 for milk production traits were found in this paper, which will provide a reference for Chinese Holstein cow breeding selection and high economic benefits.

Abstract

Improving the quality of milk is a challenge for zootechnicians and dairy farms across the globe. Long-chain acyl-CoA synthetase 1 (ACSL1) is a significant member of the long-chain acyl-CoA synthetase gene family. It is widely found in various organisms and influences the lactation performance of cows, including fat percentage, milk protein percentage etc. Our study was aimed to investigate the genetic effects of single nucleotide polymorphisms (SNPs) in ACSL1 on milk production traits. Twenty Chinese Holstein cows were randomly selected to extract DNA from their blood samples for PCR amplification and sequencing to identify SNPs of the bovine ACSL1 gene, and six SNPs (5’UTR-g.20523C>G, g.35446C>T, g.35651G>A, g.35827C>T, g.35941G>A and g.51472C>T) were discovered. Then, Holstein cow genotyping (n = 992) was performed by Sequenom MassARRAY based on former SNP information. Associations between SNPs and milk production traits and somatic cell score (SCS) were analyzed by the least-squares method. The results showed that SNP g.35827C>T was in high linkage disequilibrium with g.35941G>A. Significant associations were found between SNPs and test-day milk yield (TDMY), fat content (FC), protein content (PC) and SCS (p < 0.05). Among these SNPs, SNP 5’UTR-g.20523C>G showed an extremely significant effect on PC and SCS (p < 0.01). The SNP g.35446C>T showed a statistically significant effect on FC, PC, and SCS (p < 0.01), and also TDMY (p < 0.05). The SNP g.35651G>A had a statistically significant effect on PC (p < 0.01). The SNP g.35827C>T showed a highly significant effect on TDMY, FC, and SCS (p < 0.01) and significantly influenced PC (p < 0.05). Lastly, SNP g.51472C>T was significantly associated with TDMY, FC, and SCS (p < 0.05). In summary, the pleiotropic effects of bovine ACSL1 for milk production traits were found in this paper, but further investigation will be required on the intrinsic correlation to provide a theoretical basis for the research on molecular genetics of milk quality traits of Holstein cows.

The use of dijkstra mechanistic model for genetic analysis of the lactation curve characteristics and their relationships with age at first calving and somatic cell score of Iranian dairy cows

The objective of this study was to estimate lactation curve parameters with Dijkstra mechanistic model and to evaluate genetic and phenotypic relationships between the parameters and the average somatic cell count in primiparous cows. The finding indicated that heritability estimates for partial milk yield (PMY1, PMY2 and PMY3), total 305-day milk yield (TMY305), decay parameter (λ2), age at first calving (AFC) and peak yield (PY) were moderate while the heritability of persistency (PS%), average somatic cell score (AVGSCS), time to peak yield (TP), initial milk production (λ0), specific rate of cell proliferation at parturition (λ1), and specific rate of cell death (λ3) were quite low. Genetic correlations between both AFC and PS% traits with average somatic cell scores was negative (-0.047 and -0.060) but low positive genetic correlation were between partial milk yields (PMY1 and PMY3) while negative genetic correlation (-0.06) was obtained between TMY305 and AVGSCS. Differences between TMY305 of cows with less than 100000 cells mL-1 and cows with >1,500,000 cells mL-1 was approximately 708 Kg and is equivalent to 8% loss of milk yield/cow during lactation period and also loss of persistency (11.1 %( was shown for the extreme classes of SCC in this study.

Dynamics of somatic cell count and intramammary infection in lactating dairy cows

Objective

The influence of intramammary infection (IMI) and types of bacteria was assessed on somatic cell count (SCC) in dairy cows' milk with respect to breed, age, parity, stage of lactation, milk production, and mammary quarter location.

Materials and methods

After recording data in a structured questionnaire, 360 samples of quarter milk were collected. The samples were subjected to SCC and isolation and identification of bacteria. The data were analyzed to find out the significant influence of independent factors on SCC and IMI.

Results

The infected quarters had a significantly higher mean SCC (210.52 × 10³ cells/ml) compared to uninfected ones (32.72 × 10³ cells/ml). The mean SCC was the highest for IMI with Enterobacter spp. (338.00 × 10³ cells/ml) followed by Bacillus spp. (319.20 × 10³ cells/ml), coagulase-negative Staphylococci (CNS) (268.17 × 10³ cells/ml), Staphylococcus aureus (218.31 × 10³ cells/ml), and Escherichia coli (200.75 × 10³ cells/ml) and the lowest for Pseudomonas aeruginosa (66.33 × 10³ cells/ml). Milk of rear quarters had a significantly higher SCC than the front quarters. SCC increased with increasing age, parity, and lactation stage regardless of whether cows are infected or not. The IMI was more prevalent in rear quarters (42.2%) and cows at early (≤7 days) lactation (100.0%). Cows having a parity of ≥5 and crossbred and high yielding (>5 l) cows had also a higher rate of IMI of 38.2%, 36.7%, and 38.2%, respectively.

Conclusion

The IMI and type of bacteria were the principal factors for SCC variation. Besides, mammary quarter location, age, and parity should be taken into consideration during the interpretation of SCC.

Accuracy of Genomic-Polygenic and Polygenic Breeding Values for Age at First Calving and Milk Yield in Thai Multibreed Dairy Cattle

Single-nucleotide polymorphisms (SNPs) have been used in genomic prediction and shown to increase prediction accuracy and selection responses for economic traits in dairy cattle. The successful report in genomic prediction for improving age at first calving (AFC) and 305-d milk yield (MY) in multibreed dairy population is limited. Therefore, the objective of this research was to compare estimates of variance components, genetic parameters, and prediction accuracies for AFC and MY using a genomic-polygenic model (GPM) and a polygenic model (PM). The AFC and MY records of 9,106 first-lactating multibreed dairy cows, calved between 1991 and 2014, were collected from 1,012 Thai dairy farms. The SNP genotyped individuals were selected from cows that had completed pedigree and phenotypes information. The total genomic DNA samples of 2,661 dairy cattle were genotyped using various GeneSeek Genomic Profiler low-density bead chips (9K, 20K, and 80K). The 2-trait GPM and PM contained herd-year-season and heterosis as fixed effects, and animal additive genetic and residual as random effects. Variance components and genetic parameters were estimated using the procedure of average information-restricted maximum likelihood (AI-REML). Estimates of additive genetic variance components and heritabilities from GPM were higher than PM for AFC and MY. Correlations between AFC and MY were near zero for both models. Mean EBV accuracies were higher for GPM (32.95% for AFC and 38.24% for MY) than for PM (32.65% for AFC, and 32.99% for MY). Mean sire EBV accuracies were higher for GPM (31.35% for AFC and 36.25% for MY) than for PM (28.37% for AFC and 28.80% for MY). Thus, the GPM should be considered the model of choice to increase accuracy of genetic predictions for AFC and MY in the Thai multibreed dairy population.