Characterization of a novel heat tolerance trait and subsequent haplotype block-based analysis to identify causal regions in Dutch Holstein cattle
Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying heat tolerant animals is crucial for sustainable dairy production. This study develops a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently defines individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems, were analyzed. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature humidity index (THI) with an absolute reduction of 0.3% from THI = 30 to THI = 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically reaching 5.0% at a THI of 75. The phenotype of an animal is subsequently defined as the slope in a linear regression model of the residuals of the population-wise models against THI for milk yield and concentration of fat, protein, lactose, and specific fatty acids. Compared to reaction-norm models, individual records per cow are combined before model fitting, thereby reducing computation times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by one genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations to health and resilience. A genome-wide association study using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTLs across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the potential of breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts. Interpretive summaryThis study introduced a novel, computationally efficient method to quantify the impact of heat stress in dairy cattle and define novel heat tolerance traits based on milk production data from automated milking systems. Our results indicate quadratically increasing losses in milk yield with increasing heat load. The identified heat tolerance traits show substantial genetic variance, moderate heritabilities, and favorable correlation to key production traits. These findings highlight the potential for incorporating heat tolerance into dairy breeding goals to mitigate climate change impacts, improve animal welfare, and enhance sustainable milk production.