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Frequency of test-day records per each 5-d hourly average temperature-humidity index THI 5d ( ) value by breed for Holstein and
Source publication
The physiological stress caused by excessive heat affects dairy cattle health and production. This study sought to investigate the impact of heat stress on test-day yields in United States (US) Holstein and Jersey cows and develop single-step genomic predictions to identify heat tolerant animals. Data included 12.8 and 2.1 million test-day records...
Context in source publication
Citations
... Traditionally, the environmental effects associated with climate or heat stress are measured using some function of the THI [1,[17][18][19]. However, evidence suggests that the THI is not the most effective method for accounting for these effects, as THI calculations must be tailored to the region in which they are implemented [20], and even then, temperature often serves just as well at predicting heat stress as the THI [21,22]. ...
... Generally, genetic evaluations that account for heat stress involve finding a THI threshold above which the phenotype begins to decline. This process leads to calculating the heat load (HL) as a function of THI, followed by fitting a single reaction norm model of HL for genetic evaluation [17][18][19][20]. Employing multiple environmental covariates ECs could result in a more flexible approach, facilitating a better characterization of the environment. ...
... The first approach considers records from different environments as distinct traits and assumes a genetic correlation among them [17,[24][25][26]. The second approach requires a continuous environmental gradient and models estimated breeding values (EBV) as a function of the ECs [17][18][19]. Conversely, Lopez-Cruz et al. proposed a third approach that fits two genetic effects in the multiple-trait model [27]: one across environments and another specific to each environment. However this does not allow for ECs to be considered to avoid double-counting environmental effects. ...
Background
In traditional genetic prediction models, environments are typically treated as uncorrelated effects, either fixed or random. Environments can be correlated when they share the same location, management practices, or climate conditions. The temperature-humidity index (THI) is often used to address environmental effects related to climate or heat stress. However, it does not fully describe the complete climate profile of a specific location. Therefore, it is more appropriate to use multiple environmental covariates (ECs), when available, to describe the weather in a specific environment. This raises the question of whether publicly available weather information (such as NASA POWER) is useful for genomic predictions. Genotype-by-environment interaction (GxE) can be modeled using multiple-trait models or reaction norms. However, the former requires a substantial number of records per environment, while the latter can result in over-parametrized models when the number of ECs is large. This study investigated whether using ECs is a suitable strategy to correlate environments (herds) and to model GxE in the genomic prediction of purebred pigs for production traits.
Results
We evaluated different models to account for environmental effects and GxE. When environments were correlated based on ECs, we observed an increase in environmental variance, which was accompanied by an increase in phenotypic variance and a decrease in heritability. Furthermore, including environments as an uncorrelated random effect yielded the same accuracy of estimated breeding values as treating them as correlated based on weather information. All the tested models exhibited the same bias, but the predictions from the multiple-trait models were under-dispersed. Evidence of GxE was observed for both traits; however, there were more genetically unconnected environments for backfat thickness than for average daily gain.
Conclusions
Using outdoor weather information to correlate environments and model GxE offers limited advantages for genomic predictions in pigs. Although it adds complexity to the model and increases computing time without improving accuracy, it does enhance model fit. Including environment information (e.g. herd effect) as an uncorrelated random effect in the model could help address GxE and environmental effects.
... Practical comparisons of heat-tolerant and -intolerant animals in the United States using SNP effects derived in Australia showed only slight improvement in rectal temperature Breeding for improved heat tolerance in dairy cattle: Methods, challenges, and progress* Ignacy Misztal, 1 † Luiz F. Brito, 2 and Daniela Lourenco 1 (0.1°C), with a 5% decline in production; the evaluation used only first-parity cow records (Jensen et al., 2022). In the United States, McWhorter et al. (2023) investigated responses of Holsteins to THI and found a threshold of 69 for milk production traits. Twenty years earlier, Ravagnolo and Misztal (2000a) reported a threshold of 72, indicating slightly increased sensitivity of cows to heat stress over the years. ...
Heat tolerance in dairy cattle has likely deteriorated over time due to unfavorable genetic correlations between milk production level and heat tolerance, with stronger deterioration in later parities. The dairy cattle industry has responded to the challenge of heat stress by implementing managemental modifications. Methodology exists to calculate genetic prediction of breeding values for high production under heat stress conditions, with high reliabilities of breeding values obtained when incorporating genomic information. However, cows that maintain production during the heat stress peak have an increased likelihood of death. One remedy would be selection for better
fertility and survival under heat stress, but with a low volume of data and low heritabilities, corresponding reliabilities may be too low for an efficient selection. In environments where intensive management is too expensive, an ideal cow would maintain production in a favorable climate, would briefly reduce production during heat stress, and would restore production after the heat stress conditions are over. As there are many biological mechanisms involved in heat stress response, in addition to deriving heat tolerance indicators based on variability in performance traits under heat stress conditions, novel traits that capture physiological, behavioral, and anatomical traits related to heat stress response, less dependent on production level, could also contribute to breeding for improved heat tolerance.
... Nevertheless, a decrease in glucose levels also leads to an increase in blood ketone body levels because of increased beta-oxidation of unesterified fatty acids (Buttchereit et al., 2010;Dunning et al., 2014). This corresponds to our observations on the ele- Hagiya et al., 2019;McWhorter et al., 2023;Nguyen et al., 2016). ...
Heat stress (HS) reduces dry‐matter intake and causes negative energy balance (EB) in Holstein cattle, with consequent deterioration in milk production and wellness. Therefore, the effects of HS can be detected more directly from imbalances in EB than from the consequent changes in production or health traits. EB can be monitored by metabolism‐related traits such as predicted EB (PEB), the fat‐to‐protein ratio (FPR), or β‐hydroxybutyrate (BHB) in milk. We examined the days on which HS effects on the test‐day PEB, FPR, or milk BHB were the greatest in first lactation. We collected weather records and test‐day records. We considered the fixed effects of herd‐year, test month, calving age, days in milk, temperature–humidity index (THI) from any one of test day to 14 days prior (15 models per trait), and random effects of animal and residuals in the models and compared the deviance information criterion (DIC) between models for each trait. For PEB, FPR, and milk BHB, the model gave the lowest DIC when including the effect of THI 1, 1, and 0 day before the test day. We observed that HS caused a decrease in PEB and an increase in FPR and milk BHB.
... However, THI recorded on-farm is not yet available at a large scale. Therefore, THI values for this study, as for most large-scale studies, were obtained from the closest weather stations to the farms (Carabaño et al., 2016;Nguyen et al., 2016;McWhorter et al., 2023). Fortunately, it has been shown that weather station THI can be as accurate as on-farm THI when the terrain is geographically stable (Freitas et al., 2006) and weather station data are highly standardized (Mbuthia et al., 2022). ...
... As for cattle, there are no reported values for LR metrics using RNM. The validations accounting for G × E were based on bulls or pseudophenotypes, and mainly focused on milk production traits (Tiezzi et al., 2017;Mota et al., 2020;McWhorter et al., 2023). Our study took a step forward, by comparing the predictive abilities between RNM ho and RNM het for reproductive performance traits and regions. ...
... Heat tolerance of livestock animals has also been evaluated based on regular animal models with approaches such as heat load functions (Bradford et al., 2016;Vitali et al., 2020) or broken-stick regressions (Pinto et al., 2020), which can precisely model the real relationship between animals' performances and environmental gradient level (e.g., THI). The heat load function based on THI has also been fitted in random regression models to predict heat tolerance of production yields in the US cattle populations (McWhorter et al., 2023). As RNM is flexible to adjust the relationship between genetic effects and EG, it is of value to model curves similar to heat load (or brokenstick) in RNM based on various environmental indicators for specific traits of interest in future research. ...
... For example, reported 0.36 and 0.53 for acc of NS0 and NS1 by using regular animal model and the ssGBLUP method. However, these acc are lower than traits with higher heritability estimates, such as milk yield of cows with heritability around 0.25 acc ( > 0.8; McWhorter et al., 2023), and growth traits of pigs with heritability around 0.35 acc ( > 0.65; Song et al., 2020). Therefore, further studies should be conducted on other higher heritable traits to support the strengths of using RNM. ...
Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemi-nation (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.
... In the latter, signs of heat stress are becoming more prominent at a point where USDA stakeholders urged geneticists to take measures to mitigate the adverse effects of heat (Paul VanRaden, AGIL-USDA, Beltsville MD, personal communication). McWhorter et al. (2023) showed that the impact of heat stress in U.S. Holsteins and Jerseys differs, potentially also the former being more sensitive because of the higher production level. In fact, (Guinan et al., 2023) observed that Holstein is the breed in the U.S. harvesting the most benefits from GS because of the amount of data and level of adoption of this technology. ...
... Alternatively, using heat-tolerant males could be an option for fast-growing species, such as pigs and chickens, for progeny that is raised during a hot season. Partly due to an additional challenge of global warming, lately, U.S. companies have become interested in computing GEBV for heat tolerance (McWhorter et al., 2023); however, Australia is currently the only country with an official genomic evaluation for heat stress in dairy cattle (Nguyen et al., 2016). The Australian genetic evaluation was designed for cows on extensive pastures where managemental modifications are unattainable. ...
Initial findings on genomic selection indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under genomic selection, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negative correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under genomic selection. Additional deterioration can occur due to the rapid turnover of genomic selection, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains would not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, genomic selection can lead to an improvement for widely recorded fitness traits. In the context of genomic selection, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.
