Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, αs1-casein, αs2-casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between αs1-casein and αs2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Heterogeneity of variance and genetic parameters for milk production in cattle, using Bayesian inference

The goal of this study was to verify the effect of heterogeneity of variance (HV) on milk production in up to 305 days of lactation (L305) of daughters of Girolando, Gir and Holstein sires, as well as in the genetic evaluation of these sires and their progenies. in Brazil. The model included contemporary groups (consisting of herd, year and calving season) as a fixed effect, cow age at calving (linear and quadratic effects) and heterozygosity (linear effect) as covariates, in addition to the random effects of direct additive genetic and environmental, permanent and residual. The first analysis consisted of the single-trait animal model, with L305 records (disregarding HV). The second considered classes of standard deviations (SD): two-trait model including low and high classes (considering HV), according to the standardized means of L305 for herd-year of calving. The low SD class was composed of herds with SD equal to or less than zero and the high class with positive SD values. Estimates of (co)variance components and breeding values were obtained separately for each scenario using Bayesian inference via Gibbs sampling. Different heritability was estimated. Higher for the high DP class in the Gir (0.20) and Holstein (0.15) breeds, not occurring the same in the Girolando breed, with a lower value among the classes for the high DP (0.10). High values of genetic correlations were also found between low and high SD classes (0.88; 0.85 and 0.79) for the Girolando, Gir and Holstein breeds, respectively. Like the order correlations (Spearman) which were also high for the three breeds analyzed (equal to or above 0.92). Thus, the presence of HV had a smaller impact for L305 and did not affect the genetic evaluation of sires.

Foreword: ADSS 2020 special edition

An introduction to the special issue for the 2020 Australasian Dairy Science Symposium.