Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.

Understanding the biological mechanisms of climatic adaptation is of paramount importance for the optimization of breeding programs and conservation of genetic resources. The aim of this study was to investigate genetic diversity and unravel genomic regions potentially under selection for heat and/or cold tolerance in thirty-two worldwide cattle breeds, with a focus on Chinese local cattle breeds adapted to divergent climatic conditions, Datong yak (Bos grunniens; YAK), and Bali (Bos javanicus) based on dense SNP data. In general, moderate genetic diversity levels were observed in most cattle populations. The proportion of polymorphic SNP ranged from 0.197 (YAK) to 0.992 (Mongolian cattle). Observed and expected heterozygosity ranged from 0.023 (YAK) to 0.366 (Sanhe cattle; SH), and from 0.021 (YAK) to 0.358 (SH), respectively. The overall average inbreeding (±SD) was: 0.118 ± 0.028, 0.228 ± 0.059, 0.194 ± 0.041, and 0.021 ± 0.004 based on the observed versus expected number of homozygous genotypes, excess of homozygosity, correlation between uniting gametes, and runs of homozygosity (ROH), respectively. Signatures of selection based on multiple scenarios and methods (F ST, HapFLK, and ROH) revealed important genomic regions and candidate genes. The candidate genes identified are related to various biological processes and pathways such as heat-shock proteins, oxygen transport, anatomical traits, mitochondrial DNA maintenance, metabolic activity, feed intake, carcass conformation, fertility, and reproduction. This highlights the large number of biological processes involved in thermal tolerance and thus, the polygenic nature of climatic resilience. A comprehensive description of genetic diversity measures in Chinese cattle and YAK was carried out and compared to 24 worldwide cattle breeds to avoid potential biases. Numerous genomic regions under positive selection were detected using three signature of selection methods and candidate genes potentially under positive selection were identified. Enriched function analyses pinpointed important biological pathways, molecular function and cellular components, which contribute to a better understanding of the biological mechanisms underlying thermal tolerance in cattle. Based on the large number of genomic regions identified, thermal tolerance has a complex polygenic inheritance nature, which was expected considering the various mechanisms involved in thermal stress response.

Over the past 40 years New Zealand (NZ) aquaculture has grown into a significant primary industry. Tonnage is small on a global scale, but the industry has built an international reputation for the supply of high quality seafood to many overseas markets. Since the early 1990s the industry has recognized the potential gains from selective breeding and the challenge has been to develop programs that can overcome biological obstacles (such as larval rearing and mortality) and operate cost-effectively on a relatively small scale while still providing significant gains in multiple traits of economic value. This paper provides an overview of the current status, and a perspective on genomic technology implementation, for the family based genetic improvement programs established for the two main species farmed in NZ: Chinook (king) salmon (Oncorhynchus tshawytscha) and GreenshellTM mussel (Perna canaliculus). These programs have provided significant benefit to the industry in which we are now developing genomic resources based on genotyping-by-sequencing to complement the breeding programs, enable evaluation of the genetic diversity and identify the potential benefits of genomic selection. This represents an opportunity to increase genetic gain and more effectively utilize the potential for within family selection.

Both natural and artificial selection are among the main driving forces shaping genetic variation across the genome of livestock species. Selection typically leaves signatures in the genome, which are often characterized by high genetic differentiation across breeds and/or a strong reduction in genetic diversity in regions associated with traits under intense selection pressure. In this study, we evaluated selection signatures and genomic inbreeding coefficients, FROH, based on runs of homozygosity (ROH), in six Ugandan goat breeds: Boer (n = 13), and the indigenous breeds Karamojong (n = 15), Kigezi (n = 29), Mubende (n = 29), Small East African (n = 29), and Sebei (n = 29). After genotyping quality control, 45,294 autosomal single nucleotide polymorphisms (SNPs) remained for further analyses. A total of 394 and 6 breed-specific putative selection signatures were identified across all breeds, based on marker-specific fixation index (FST-values) and haplotype differentiation (hapFLK), respectively. These regions were enriched with genes involved in signaling pathways associated directly or indirectly with environmental adaptation, such as immune response (e.g., IL10RB and IL23A), growth and fatty acid composition (e.g., FGF9 and IGF1), and thermo-tolerance (e.g., MTOR and MAPK3). The study revealed little overlap between breeds in genomic regions under selection and generally did not display the typical classic selection signatures as expected due to the complex nature of the traits. In the Boer breed, candidate genes associated with production traits, such as body size and growth (e.g., GJB2 and GJA3) were also identified. Furthermore, analysis of ROH in indigenous goat breeds showed very low levels of genomic inbreeding (with the mean FROH per breed ranging from 0.8% to 2.4%), as compared to higher inbreeding in Boer (mean FROH = 13.8%). Short ROH were more frequent than long ROH, except in Karamojong, providing insight in the developmental history of these goat breeds. This study provides insights into the effects of long-term selection in Boer and indigenous Ugandan goat breeds, which are relevant for implementation of breeding programs and conservation of genetic resources, as well as their sustainable use and management.

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

Understanding heat stress physiology and identifying reliable biomarkers are paramount for developing effective management and mitigation strategies. However, little is known about the molecular mechanisms underlying thermal tolerance in animals. In an experimental model of Sprague-Dawley rats subjected to temperatures of 22 ± 1°C (control group; CT) and 42°C for 30 min (H30), 60 min (H60), and 120 min (H120), RNA-sequencing (RNA-Seq) assays were performed for blood (CT and H120), liver (CT, H30, H60, and H120), and adrenal glands (CT, H30, H60, and H120). A total of 53, 1,310, and 1,501 differentially expressed genes (DEGs) were significantly identified in the blood (P < 0.05 and |fold change (FC)| >2), liver (P < 0.01, false discovery rate (FDR)-adjusted P = 0.05 and |FC| >2) and adrenal glands (P < 0.01, FDR-adjusted P = 0.05 and |FC| >2), respectively. Of these, four DEGs, namely Junb, P4ha1, Chordc1, and RT1-Bb, were shared among the three tissues in CT vs. H120 comparison. Functional enrichment analyses of the DEGs identified in the blood (CT vs. H120) revealed 12 biological processes (BPs) and 25 metabolic pathways significantly enriched (FDR = 0.05). In the liver, 133 BPs and three metabolic pathways were significantly detected by comparing CT vs. H30, H60, and H120. Furthermore, 237 BPs were significantly (FDR = 0.05) enriched in the adrenal glands, and no shared metabolic pathways were detected among the different heat-stressed groups of rats. Five and four expression patterns (P < 0.05) were uncovered by 73 and 91 shared DEGs in the liver and adrenal glands, respectively, over the different comparisons. Among these, 69 and 73 genes, respectively, were proposed as candidates for regulating heat stress response in rats. Finally, together with genome-wide association study (GWAS) results in cattle and phenome-wide association studies (PheWAS) analysis in humans, five genes (Slco1b2, Clu, Arntl, Fads1, and Npas2) were considered as being associated with heat stress response across mammal species. The datasets and findings of this study will contribute to a better understanding of heat stress response in mammals and to the development of effective approaches to mitigate heat stress response in livestock through breeding.

Fertility and reproductive performance are key drivers of dairy farm profitability. Hence, reproduction traits have been included in a large majority of worldwide dairy cattle selection indexes. The reproductive traits are lowly heritable but can be improved through direct genetic selection. However, most scientific studies and dairy cattle breeding programs have focused solely on the genetic effects of the dam (GED) on reproductive performance and, therefore, ignored the contribution of the service sire in the phenotypic outcomes. This study aimed to investigate the service sire effects on female reproductive traits in Holstein cattle from a genomic perspective. Genetic parameter estimation and genome-wide association studies (GWAS) were performed for the genetic effect of service sire (GESS) on conception rate (CR), 56-day non-return rate (NRR56), calving ease (CE), stillbirth (SB), and gestation length (GL). Our findings indicate that the additive genetic effects of both sire and dam contribute to the phenotypic variance of reproductive traits measured in females (0.0196 vs. 0.0109, 0.0237 vs. 0.0133, 0.0040 vs. 0.0289, 0.0782 vs. 0.0083, and 0.1024 vs. 0.1020 for GESS and GED heritability estimates for CR, NRR56, CE, SB, and GL, respectively), and these two genetic effects are positively correlated for SB (0.1394) and GL (0.7871). Interestingly, the breeding values for GESS on insemination success traits (CR and NRR56) are unfavorably and significantly correlated with some production, health, and type breeding values (ranging from -0.449 to 0.274), while the GESS values on calving traits (CE, SB, and GL) are usually favorably associated with those traits (ranging from -0.493 to 0.313). One hundred sixty-two significant single-nucleotide polymorphisms (SNPs) and their surrounding protein-coding genes were identified as significantly associated with GESS and GED, respectively. Six genes overlapped between GESS and GED for calving traits and 10 genes overlapped between GESS for success traits and calving traits. Our findings indicate the importance of considering the GESS when genetically evaluating the female reproductive traits in Holstein cattle.

As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (G -1) and pedigree ( A 22 - 1 ) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71-0.99). The scenario mimicking a completely polygenic trait ( h Q T L 2 = 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and h Q T L 2 = h 2 resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine G -1 and A 22 - 1 had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71-0.99) genetic correlations between purebred and crossbred populations].

The Montana Tropical® Composite is a recently developed beef cattle population that is rapidly expanding in Brazil and other tropical countries. This is mainly due to its improved meat quality and adaptation to tropical climate conditions compared to Zebu and Taurine cattle breeds, respectively. This study aimed to investigate the genetic architecture of ultrasound-based carcass and meat quality traits in Montana Tropical® Composite beef cattle. Therefore, we estimated variance components and genetic parameters and performed genome-wide association studies using the weighted single-step Genomic Best Linear Unbiased Prediction (GBLUP) approach. A pedigree dataset containing 28,480 animals was used, in which 1,436 were genotyped using a moderate-density Single Nucleotide Polymorphism panel (30K; 30,105 SNPs). A total of 9,358, 5,768, 7,996, and 1,972 phenotypic records for the traits Longissimus muscle area (LMA), backfat thickness (BFT), rump fat thickness (RFT), and for marbling score (MARB), respectively, were used for the analyses. Moderate to high heritability estimates were obtained and ranged from 0.16 ± 0.03 (RFT) to 0.33 ± 0.05 (MARB). A high genetic correlation was observed between BFT and RFT (0.97 ± 0.02), suggesting that a similar set of genes affects both traits. The most relevant genomic regions associated with LMA, BFT, RFT, and MARB were found on BTA10 (5.4-5.8 Mb), BTA27 (25.2-25.5 Mb), BTA18 (60.6-61.0 Mb), and BTA21 (14.8-15.4 Mb). Two overlapping genomic regions were identified for RFT and MARB (BTA13:47.9-48.1 Mb) and for BFT and RFT (BTA13:61.5-62.3 Mb). Candidate genes identified in this study, including PLAG1, LYN, WWOX, and PLAGL2, were previously reported to be associated with growth, stature, skeletal muscle growth, fat thickness, and fatty acid composition. Our results indicate that ultrasound-based carcass and meat quality traits in the Montana Tropical® Composite beef cattle are heritable, and therefore, can be improved through selective breeding. In addition, various novel and already known genomic regions related to these traits were identified, which contribute to a better understanding of the underlying genetic background of LMA, BFT, RFT, and MARB in the Montana Tropical Composite population.

The Norwegian White sheep (NWS) and New Zealand Terminal Sire Composite (NZC) sheep breeds have been developed based on crossing of multiple breeds, mainly of Northern European origin. A close genetic relationship between these populations could enable across-country genomic evaluations. The main objectives of this study were to assess the genetic connectedness between Norwegian and New Zealand sheep populations and estimate numerous genetic diversity metrics for these two populations. A total of 792 NWS and 16,912 NZC animals were genotyped using a high-density Illumina SNP chip panel (∼606K SNPs). The NZC animals were grouped based on their breed composition as: Finn, Lamb Supreme, Primera, Texel, "Other Dual Purpose", and "Other Terminal Sire". The average level of linkage disequilibrium ranged from 0.156 (for Primera) to 0.231 (for Finn). The lowest consistency of gametic phase was estimated between NWS and Finn (0.397), and between NWS and Texel (0.443), respectively. Similar consistency of gametic phase was estimated between NWS and the other NZC populations (∼ 0.52). For all composite sheep populations analyzed in this study, the majority of runs of homozygosity (ROH) segments identified had short length (<2,500 kb), indicating ancient (instead of recent) inbreeding. The variation in the number of ROH segments observed in the NWS was similar to the variation observed in Primera and Lamb Supreme. There was no clear discrimination between NWS and NZC based on the first few principal components. In addition, based on admixture analyses, there seems to be a significant overlap of the ancestral populations that contributed to the development of both NWS and NZC. There were no evident signatures of selection in these populations, which might be due to recent crossbreeding. In conclusion, the NWS composite breed was shown to be moderately related to NZC populations, especially Primera and Lamb Supreme. The findings reported here indicate a promising opportunity for collaborative genomic analyses involving NWS and NZC sheep populations.