Characterization of a novel heat tolerance trait and subsequent haplotype block-based analysis to identify associated...

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 animals that are able to maintain high production levels during heat stress events, commonly referred to as heat tolerance, is crucial for sustainable dairy production. In this study, we develop a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently define individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows were analyzed, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems. 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% as THI increases from 30 to 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically, reaching a loss of 5.0% at a THI of 75. We subsequently define the heat tolerance phenotype of an animal as the slope from a linear regression model of the residuals of the population-wise models against THI for milk yield, concentration of fat, protein, lactose, and specific fatty acids. Compared with reaction-norm models, individual records per cow are combined into one joint record before model fitting, thus reducing computing 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 1 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 with health and resilience. A GWAS using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTL across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the long-term potential of genetic improvement through breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts in the short term.