Genomic regions, candidate genes, and pleiotropic variants associated with physiological and anatomical indicators of heat stress response in lactating sows.

BACKGROUND: Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. RESULTS: A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. CONCLUSIONS: Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds.

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, -0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, -0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, -0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

Evaluating phenotypes associated with heat tolerance and identifying moderate and severe heat stress thresholds in lactating sows housed in mechanically or naturally ventilated barns during the summer under commercial conditions.

An accurate understanding of heat stress (HS) temperatures and phenotypes that indicate HS tolerance is necessary to improve swine HS resilience. Therefore, the study objectives were 1) to identify phenotypes indicative of HS tolerance, and 2) to determine moderate and severe HS threshold temperatures in lactating sows. Multiparous (4.10 ± 1.48) lactating sows and their litters (11.10 ± 2.33 piglets/litter) were housed in naturally ventilated (n = 1,015) or mechanically ventilated (n = 630) barns at a commercial sow farm in Maple Hill, NC, USA between June 9 and July 24, 2021. In-barn dry bulb temperatures (TDB) and relative humidity were continuously recorded for naturally ventilated (26.38 ± 1.21 °C and 83.38 ± 5.40%, respectively) and mechanically ventilated (26.91 ± 1.80 °C and 77.13 ± 7.06%, respectively) barns using data recorders. Sows were phenotyped between lactation days 11.28 ± 3.08 and 14.25 ± 3.26. Thermoregulatory measures were obtained daily at 0800, 1200, 1600, and 2000 h and included respiration rate, and ear, shoulder, rump, and tail skin temperatures. Vaginal temperatures (TV) were recorded in 10 min intervals using data recorders. Anatomical characteristics were recorded, including ear area and length, visual and caliper-assessed body condition scores, and a visually assessed and subjective hair density score. Data were analyzed using PROC MIXED to evaluate the temporal pattern of thermoregulatory responses, phenotype correlations were based on mixed model analyses, and moderate and severe HS inflection points were established by fitting TV as the dependent variable in a cubic function against TDB. Statistical analyses were conducted separately for sows housed in mechanically or naturally ventilated barns because the sow groups were not housed in each facility type simultaneously. The temporal pattern of thermoregulatory responses was similar for naturally and mechanically ventilated barns and several thermoregulatory and anatomical measures were significantly correlated with one another (P < 0.05), including all anatomical measures as well as skin temperatures, respiration rates, and TV. For sows housed in naturally and mechanically ventilated facilities, moderate HS threshold TDB were 27.36 and 26.69 °C, respectively, and severe HS threshold TDB were 29.45 and 30.60 °C, respectively. In summary, this study provides new information on the variability of HS tolerance phenotypes and environmental conditions that constitute HS in commercially housed lactating sows.

Climate change and the associated increase in global temperatures have a well-described negative impact on swine production. Therefore, improving swine heat stress resilience is of utmost importance to reduce the deleterious effects of heat stress on swine health, performance, and welfare. Genomic selection for heat stress resilience may be a viable strategy to improve swine productivity in a changing climate. However, identifying environmental conditions that constitute heat stress and deriving novel traits that can be easily collected on farm and provide accurate and precise predictions of heat stress tolerance is a necessary step. The present study demonstrated that housing conditions had a limited influence on heat stress tolerance phenotypes, several anatomical and thermoregulatory measures were correlated, and housing conditions impacted heat stress threshold temperatures. Results from this study may be applied to large-scale phenotyping initiatives to develop or refine genomic selection indexes for heat stress resilience in pigs.

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations.

BACKGROUND: Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows. RESULTS: Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95-69.59% of the total genetic variance. CONCLUSIONS: Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Differential Responses of Physiological Parameters, Production Traits, and Blood Metabolic Profiling between First- and Second-Parity Holstein Cows in the Comparison of Spring versus Summer Seasons.

Heat stress (HS) negatively influences cows' welfare and productivity. Therefore, a better understanding of the physiological and molecular mechanisms of HS responses from multiple parities is paramount for the development of effective management and breeding strategies. In comparison with first-parity cows in the spring (Spring-1), first-parity cows in the summer (Summer-1) had a significantly higher rectal temperature (RT), respiration rate (RR), drooling score (DS), and daily activity (DA), while lower (P < 0.05) daily rumination (DR), seven-day average milk yield (7AMY), milk yield on sampling day (MY_S), milk yield on test day (MY_T), and lactose percentage (LP) were observed. When comparing the spring (Spring-2) and summer (Summer-2) of the second-parity cows, significant differences were also found in RT, RR, DS, DA, and DR (P < 0.05), corresponding to similar trends with the first parity while having smaller changes. Moreover, significantly negative impacts on performance traits were only observed on fat percentage (FP) and LP. These results showed that there were different biological responses between first- and second-parity Holstein cows. Further, 18 and 17 metabolites were involved in the seasonal response of first- and second-parity cows, respectively. Nine differential metabolites were shared between the two parities, and pathway analyses suggested that cows had an inhibited tricarboxylic acid cycle, increased utilization of lipolysis, and a dysregulated gut microbiome during the summer. The metabolites identified exclusively for each parity highlighted the differences in microbial response and host amino acid metabolism between two parities in response to HS. Moreover, glucose, ethanol, and citrate were identified as potential biomarkers for distinguishing individuals between Spring-1 and Summer-1. Ethanol and acetone were better predictors for distinguishing individuals between Spring-2 and Summer-2. Taken together, the present study demonstrated the impact of naturally induced HS on physiological parameters, production traits, and the blood metabolome of Holstein cows. There are different biological responses and regulation mechanisms between first- and second-parity Holstein cows.

Weighted single-step GWAS and RNA sequencing reveals key candidate genes associated with physiological indicators of heat stress in Holstein cattle.

BACKGROUND: The study of molecular processes regulating heat stress response in dairy cattle is paramount for developing mitigation strategies to improve heat tolerance and animal welfare. Therefore, we aimed to identify quantitative trait loci (QTL) regions associated with three physiological indicators of heat stress response in Holstein cattle, including rectal temperature (RT), respiration rate score (RS), and drooling score (DS). We estimated genetic parameters for all three traits. Subsequently, a weighted single-step genome-wide association study (WssGWAS) was performed based on 3200 genotypes, 151,486 phenotypic records, and 38,101 animals in the pedigree file. The candidate genes located within the identified QTL regions were further investigated through RNA sequencing (RNA-seq) analyses of blood samples for four cows collected in April (non-heat stress group) and four cows collected in July (heat stress group). RESULTS: The heritability estimates for RT, RS, and DS were 0.06, 0.04, and 0.03, respectively. Fourteen, 19, and 20 genomic regions explained 2.94%, 3.74%, and 4.01% of the total additive genetic variance of RT, RS, and DS, respectively. Most of these genomic regions are located in the Bos taurus autosome (BTA) BTA3, BTA6, BTA8, BTA12, BTA14, BTA21, and BTA24. No genomic regions overlapped between the three indicators of heat stress, indicating the polygenic nature of heat tolerance and the complementary mechanisms involved in heat stress response. For the RNA-seq analyses, 2627 genes were significantly upregulated and 369 downregulated in the heat stress group in comparison to the control group. When integrating the WssGWAS, RNA-seq results, and existing literature, the key candidate genes associated with physiological indicators of heat stress in Holstein cattle are: PMAIP1, SBK1, TMEM33, GATB, CHORDC1, RTN4IP1, and BTBD7. CONCLUSIONS: Physiological indicators of heat stress are heritable and can be improved through direct selection. Fifty-three QTL regions associated with heat stress indicators confirm the polygenic nature and complex genetic determinism of heat tolerance in dairy cattle. The identified candidate genes will contribute for optimizing genomic evaluation models by assigning higher weights to genetic markers located in these regions as well as to the design of SNP panels containing polymorphisms located within these candidate genes.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat.

Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

Investigating the Short-Term Effects of Cold Stress on Metabolite Responses and Metabolic Pathways in Inner-Mongolia Sanhe Cattle.

Inner-Mongolia Sanhe cattle are well-adapted to low-temperature conditions, but the metabolic mechanisms underlying their climatic resilience are still unknown. Based on the 1H Nuclear Magnetic Resonance platform, 41 metabolites were identified and quantified in the serum of 10 heifers under thermal neutrality (5 °C), and subsequent exposure to hyper-cold temperature (-32 °C) for 3 h. Subsequently, 28 metabolites were pre-filtrated, and they provided better performance in multivariate analysis than that of using 41 metabolites. This indicated the need for pre-filtering of the metabolome data in a paired experimental design. In response to the cold exposure challenge, 19 metabolites associated with cold stress response were identified, mainly enriched in "aminoacyl-tRNA biosynthesis" and "valine, leucine, and isoleucine degradation". A further integration of metabolome and gene expression highlighted the functional roles of the DLD (dihydrolipoamide dehydrogenase), WARS (tryptophanyl-tRNA synthetase), and RARS (arginyl-tRNA synthetase) genes in metabolic pathways of valine and leucine. Furthermore, the essential regulations of SLC30A6 (solute carrier family 30 (zinc transporter), member 6) in metabolic transportation for propionate, acetate, valine, and leucine under severe cold exposure were observed. Our findings presented a comprehensive characterization of the serum metabolome of Inner-Mongolia Sanhe cattle, and contributed to a better understanding of the crucial roles of regulations in metabolites and metabolic pathways during cold stress events in cattle.