Dairy production in Holstein cows in a semiarid environment is challenging due to heat stress. Under such conditions, genetic selection for heat tolerance appears to be a useful strategy. The objective was to validate molecular markers associated with milk production and thermotolerance traits in Holstein cows managed in a hot and humid environment. Lactating cows (n = 300) exposed to a heat stress environment were genotyped using a medium-density array including 53,218 SNPs. A genome-wide association study (GWAS) detected six SNPs associated with total milk yield (MY305) that surpassed multiple testing (p < 1.14 × 10⁻⁶). These SNPs were further validated in 216 Holstein cows from two independent populations that were genotyped using the TaqMan bi-allelic discrimination method and qPCR. In these cows, only the SNPs rs8193046, rs43410971, and rs382039214, within the genes TLR4, GRM8, and SMAD3, respectively, were associated (p < 0.05) with MY305, rectal temperature (RT), and respiratory rate. Interestingly, these variables improved as the number of favorable genotypes of the SNPs increased from 0 to 3. In addition, a regression analysis detected RT as a significant predictor (R² = 0.362) for MY305 in cows with >1 favorable genotype, suggesting this close relationship was influenced by genetic markers. In conclusion, SNPs in the genes TLR4, GRM8, and SMAD3 appear to be involved in the molecular mechanism that regulates milk production in cows under heat-stressed conditions. These SNPs are proposed as thermotolerance genetic markers for a selection program to improve the milk performance of lactating Holstein cows managed in a semiarid environment.

Stayability (STAY) in beef cattle, has been traditionally defined as the probability that a cow will remain in the herd until 6 yr of age, given she first calved as a 2-yr-old ( Brigham et al., 2007). Cows that stay in production longer, benefit profitability of the herd by reducing the need of female replacements, decreasing the incidence of dystocia and increasing the average weaning weight of marketed calves ( Garrick, 2006). Sire genetic evaluations for STAY are challenging due to the low heritability of the trait and the considerably long time (e.g., 6 yr) required to collect observations from the female progeny ( Speidel et al., 2018). This delay in collection of phenotypes reduces the accuracy of sire’s genetic predictions at early ages and therefore slows genetic progress. Traditional evaluations for STAY are typically performed with threshold models that only consider the success or failure of daughters reaching the age of 6 yr, explicitly ignoring the information from the female progeny that are not yet 6 yr of age or that are still producing beyond that age end point. Given these deficiencies, the application of a statistical approach designed to use information from both earlier and later ages is expected to improve the accuracy of sire evaluation. Within this context, random regression models (RRM) have been successfully applied to STAY data, since binary observations can be assigned to any discrete point in time during a cow’s lifetime and expected progeny differences (EPD) with higher accuracies can be generated for any particular age ( Jamrozik et al., 2013). Prior research efforts using RRM have included earlier age end points in the estimation of an aggregated 6-yr STAY genetic prediction ( Sánchez-Castro et al., 2017); however, predictions using regression equations have a tendency to be variable, particularly at the ends of the prediction range. For traits such as STAY, there is potential to extend the age end points beyond 6 yr of age in order to increase the stability of the 6-yr STAY EPD in comparison with the increases in accuracy associated with RRM that finalize their prediction range at the age of 6 yr. Therefore, the objectives of this study were to determine the efficacy associated with extending evaluated age end points beyond 6 yr of age in an effort to stabilize the prediction of 6-yr STAY and evaluate the accuracy gains using various RRM when compared to a traditional threshold evaluation.

The association of single nucleotide polymorphisms (SNPs) within genes of the prolactin (PRL), growth hormone (GH) and insulin-like growth factor-I (IGF1) pathways with 305-day milk yield (MY305) were evaluated in heat-stressed Holstein cows. Ambient temperature and relative humidity were used to calculate a temperature-humidity index (THI), which revealed that heat stress conditions existed in the Yaqui Valley, Sonora, México from May through October of 2011 and 2012. Cows (n = 573) were genotyped for 179 tag SNPs within 43 candidate genes in the PRL and GH/IGF1 pathways. Seven SNPs within 7 genes (AVPR1A, Furin, IGFBP5, IGFBP6, PMCH, PRLR and STAT5B) were found to be associated with MY305 (P<0.05); therefore, their effects were used to estimate a molecular breeding value (MBV). The correlation between the MBV and MY305 was 0.21 (P<0.001) and the adjusted coefficient of determination (R2) was 4%, whereas correlation between MBV and the estimated breeding value for MY305 was 0.25 (P<0.001) with a R2 of 6%. Heritability estimate for MY305 was 0.41 ± 0.12. The MBV estimated was positive, but weakly associated with MY305. The small amount of additive and phenotypic variation explained by the MBV was most likely due to the few number of SNPs and the complexity of the trait, particularly under extreme weather conditions. In conclusion, SNPs within the PRL and GH/IGF1 pathways were associated with MY305; however, the MBV estimated with these SNPs was not yet suitable to use in genetic selection procedures due to the small amount of variation explained for MY305.