Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

Assessment of Genetic and Health Management of Tunisian Holstein Dairy Herds with a Focus on Longevity

In Tunisia, the recognition of the possibility of including longevity and disease resistance in dairy cattle selection objectives has been hypothesized as a useful strategy by both researchers and producers. However, in this paper, the state of the art, with a focus on health and longevity, is reviewed. Along the same lines, the heritability for the milk traits, fertility traits, and longevity of Tunisian Holstein dairy cows complies with the literature. Therefore, the influence of genetics on some diseases of the dairy cow was investigated. In addition, a decreasing efficiency in cow fertility has been observed over the last few years. The results showed that the risk of culling increased with common diseases. When analyzed with the Weibull model, functional lifespan was strongly influenced by milk yield; therefore, the risk increased with a reduced milk yield. In her first three lactations, the relative risk of selection increased gradually with lactation. Thus, the risk of thinning is highest at the beginning and end of the first feeding and the end of her second feeding. In conclusion, the risk of culling was reduced in parity. The factors that influence the life of the herd, such as health, husbandry, environmental conditions, and management, are often ignored when evaluating longevity.

Vulnerability assessments in dairy cattle farms based on individual sensitivity to heat stress

Climate change (CC) is expected to increase temperatures and the frequency of extreme weather events, which renewed interest in heat stress (HS) effects on dairy cattle farms. Resilience is a key concept that should be considered to better understand the dairy farms exposure to HS and to combat CC-related risks. Thus, this study aimed to investigate the aspects of HS vulnerability for Tunisian dairy cattle farming systems. Historical milk test-day records from official milk recording were merged with temperature and humidity data provided by public weather stations. Firstly, different models relying in two heat load indices were applied for HS exposure assessment. Secondly, broken line models were used to estimate HS thresholds, milk losses, and rates of decline of milk production associated with temperature-humidity index (THI) across parities. Thirdly, individual cow responses to HS estimated using random regression model were considered as key measures of dairy farming system sensitivity assessment to HS. Dairy farms are annually exposed for 5 months to high THI values above 72 in Tunisia. The tipping points, at which milk yield started to decline over parities with 3-day average THI, ranged between 65 and 67. The largest milk decline per unit of THI above threshold values was 0.135 ± 0.01 kg for multiparous cows. The milk losses estimated due to HS in the Holstein breed during the summer period (June to August) ranged between 110 and 142 kg/cow in north and south, respectively. A high HS sensitivity was proved especially in dairy farms characterized by large herd size and high milk production level. Hence, providing knowledge of dairy farms vulnerability to HS may provide the basis for developing strategies to reduce HS effects and plan for CC adaptation.

Comparison and estimation of different linear and nonlinear lactation curve submodels in random regression analyses on dairy cattle

In the random regression model (RRM) for milk yield, by replacing empirical lactation curves with the five-order Legendre polynomial to fit fixed groups, the RRM can be transformed to a hierarchical model that consisted of a RRM in the first hierarchy with Legendre polynomials as individuals’ lactation curves resolved by restricted maximum likelihood (REML) software, and a multivariate animal model for phenotypic regression coefficients in the second hierarchy resolved by DMU software. Some empirical lactation functions can be embedded into the RRM at the first hierarchy to well fit phenotypic lactation curve of the average observations across all animals. The functional relationship between each parameter and time can be described by a Legendre polynomial or an empirical curve usually called submodel, and according to three commonly used criteria, the optimal submodels were picked from linear and nonlinear submodels except for polynomials. The so-called hierarchical estimation for the RRMs in dairy cattle indicated that more biologically meaningful models were available to fit the lactation curves; moreover, with the same number of parameters, the empirical lactation curves (MIL1, MIL5, and MK1 for 3, 4, and 5 parameters, respectively) performed higher goodness of fit than Legendre polynomial when modelling individuals’ phenotypic lactation curves.

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle

Simple Summary

Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle.

Abstract

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10⁻⁷), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.