Genome-wide association studies of beef cow terrain-use traits using Bayesian multiple-SNP regression

Stress-induced epigenetic effects driven by maternal lactation in dairy cattle: a comethylation network approach

Epigenetic marks do not follow the Mendelian laws of inheritance. The environment can alter the epigenotype of an individual when exposed to different external stressors. In lactating cows, the first stages of gestation overlap with the lactation peak, creating a negative energy balance that is difficult to overcome with diet. This negative energy balance could affect early embryo development that must compete with the mammary tissue for nutrients. We hypothesize that the methylation profiles of calves born to nonlactating heifers are different from those of calves born to lactating cows. We found 50,277 differentially methylated cytosines and 2,281 differentially methylated regions between these two groups of animals. A comethylation network was constructed to study the correlation between the phenotypes of the mothers and the epigenome of the calves, revealing 265 regions associated with the phenotypes. Our study revealed the presence of DMCs and DMRs in calves gestated by heifers and lactating cows, which were linked to the dam's lactation and the calves' ICAP and milk EBV. Gene-specific analysis highlighted associations with vasculature and organ morphogenesis and cell communication and signalling. These finding support the hypothesis that calves gestated by nonlactating mothers have a different methylation profile than those gestated by lactating cows.

The interaction of genes and environment on percent of juniper in the diet of goats divergently selected for high or low juniper consumption

Diet selection and preference by grazing animals are determined by genetic and environmental factors that interact and affect their efficacy for managing vegetation as targeted grazers and developing animals adapted to local grazing environments. The effect of the rearing environments on the consumption of juniper (Juniperus spp.) by goats that for 15 years were divergently selected for high (J+) or low (J-) percent juniper in their diet was investigated. To test the effect of rearing environment, at the end of the breeding season, pregnant does from both selection lines were grazed on either juniper-infested (JIR) or juniper-free (JFR) rangelands until their kids were weaned at about 75 days of age. Fecal samples were analyzed with fecal near-IR spectroscopy to determine the percent juniper in the diet. Fecal samples were collected from does on JIR when their offspring were 30 days of age and at weaning. Then, does that raised kids in both rearing environments grazed a common JIR pasture for a 28-day adaptation period before collecting fecal samples. After weaning, kids from both rearing environments grazed JIR for 22 days before collecting fecal samples. The J+ does always consumed more (P < 0.001) juniper than J- does, demonstrating different maternal role models for kids reared in the JIR environment. There was no effect of rearing environment (P = 0.488) or rearing environment × selection line interaction (P = 0.096) when J- and J+ does grazed a common JIR pasture. The percentage of juniper in J- kid diets (7%) was the same regardless of the rearing environment. However, the rearing environment did affect the percentage of juniper in the diet of J+ kids, resulting in a gene-environment interaction (P = 0.022). The percentage of juniper in the diet of J+ kids reared in JFR (16%) and JIR (24%) were about two and three times higher than J- kids, respectively, indicating that genetics and the rearing environment contributed about equally to the increase in the percentage of juniper in the J+ kid diets. Regardless of the rearing environment, the J+ kids had a higher percentage of juniper in their diets than J- kids (P < 0.001). Compared to males, female kids had a higher percentage of juniper in their diets (12 vs 17%, respectively; P = 0.002). The ability to select animals with specific dietary preferences holds promise for targeted grazing strategies to restore degraded rangelands, with potential applications in conservation and ecosystem management.

Key Grazing Behaviours of Beef Cattle Identify Specific Genotypes of the Glutamate Metabotropic Receptor 5 Gene (GRM5)

Genotype-phenotype associations between the bovine genome and grazing behaviours measured over time and across contexts have been reported in the past decade, with these suggesting the potential for genetic control over grazing personalities in beef cattle. From the large array of metrics used to describe grazing personality behaviours (GP-behaviours), it is still unclear which ones are linked to specific genes. Our prior observational study has reported associations and trends towards associations between genotypes of the glutamate metabotropic receptor 5 gene (GRM5) and four GP-behaviours, yet the unbalanced representation of GRM5 genotypes occurring in observational studies may have limited the ability to detect associations. Here, we applied a subsampling technique to create a genotypically-balanced dataset in a quasi-manipulative experiment with free ranging cows grazing in steep and rugged terrain of New Zealand's South Island. Using quadratic discriminant analysis, two combinations of eleven GP-behaviours (and a total of fifteen behaviours) were selected to build an exploration model and an elevation model, respectively. Both models achieved ∼ 86% accuracy in correctly discriminating cows' GRM5 genotypes with the training dataset, and the exploration model achieved 85% correct genotype prediction of cows from a testing dataset. Our study suggests a potential pleiotropic effect, with GRM5 controlling multiple grazing behaviours, and with implications for the grazing of steep and rugged grasslands. The study highlights the importance of grazing behavioural genetics in cattle and the potential use of GRM5 markers to select individuals with desired grazing personalities and built herds that collectively utilize steep and rugged rangelands sustainably.

The glutamate metabotropic receptor 5 (GRM5) gene is associated with beef cattle home range and movement tortuosity

Background

The grazing behaviour of herbivores and their grazing personalities might in part be determined genetically, but there are few studies in beef cattle illustrating this. In this study, we investigated for first time the genetic variation within a candidate ‘grazing gene’, the glutamate metabotropic receptor 5 gene (GRM5), and tested associations between variation in that gene and variation in grazing personality behaviours (GP-behaviours) displayed by free-ranging cows during winter grazing in the steep and rugged rangelands of New Zealand. Mature beef cows (n = 303, from 3 to 10 years of age) were tracked with global positioning system (GPS) and, with 5-minutes (min) relocation frequency, various GP-behaviours were calculated. These included horizontal and vertical distances travelled, mean elevation, elevation range, elevation gain, slope, home range and movement tortuosity, variously calculated using daily relocation trajectories with repeated measurements (i.e., 7 to 24 days (d)) and satellite-derived digital elevation models (DEM). The different GP-behaviours were fitted into mixed models to ascertain their associations with variant sequences and genotypes of GRM5.

Results

We discovered three GRM5 variants (A, B and C) and identified the six possible genotypes in the cattle studied. The mixed models revealed that A was significantly associated with elevation range, home range and movement tortuosity. Similarly, GRM5 genotypes were associated (P < 0.05) to home range and movement tortuosity, while trends suggesting association (P <  0.1) were also revealed for elevation range and horizontal distance travelled. Most GP-behaviour models were improved by correcting for cow age-class as a fixed factor. The analysis of GP-behaviours averaged per cow age-class suggests that grazing personality is fully established as beef cows reached 4 years of age. Home range and movement tortuosity were not only associated with GRM5 variation, but also negatively correlated with each other (r = − 0.27, P < 0.001).

Conclusions

There seems to be a genetically determined trade-off between home range and movement tortuosity that may be useful in beef cattle breeding programmes aiming to improve the grazing distribution and utilisation of steep and rugged rangelands.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00755-7.

Genetic Variations and Differential DNA Methylation to Face Contrasted Climates in Small Ruminants: An Analysis on Traditionally-Managed Sheep and Goats

The way in which living organisms mobilize a combination of long-term adaptive mechanisms and short-term phenotypic plasticity to face environmental variations is still largely unknown. In the context of climate change, understanding the genetic and epigenetic bases for adaptation and plasticity is a major stake for preserving genomic resources and the resilience capacity of livestock populations. We characterized both epigenetic and genetic variations by contrasting 22 sheep and 21 goats from both sides of a climate gradient, focusing on free-ranging populations from Morocco. We produced for each individual Whole-Genome Sequence at 12X coverage and MeDIP-Seq data, to identify regions under selection and those differentially methylated. For both species, the analysis of genetic differences (F_ST) along the genome between animals from localities with high vs. low temperature annual variations detected candidate genes under selection in relation to environmental perception (5 genes), immunity (4 genes), reproduction (8 genes) and production (11 genes). Moreover, we found for each species one differentially methylated gene, namely AGPTA4 in goat and SLIT3 in sheep, which were both related, among other functions, to milk production and muscle development. In both sheep and goats, the comparison between genomic regions impacted by genetic and epigenetic variations suggests that climatic variations impacted similar biological pathways but different genes.

Opportunities to Apply Precision Livestock Management on Rangelands

Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review

Understanding the behaviour of grazing animals at pasture is crucial in order to develop management strategies that will increase the potential productivity of grazing systems and simultaneously decrease the negative impact on the environment. The objective of this review was to summarize and analyse the scientific literature that has addressed the site use preference of grazing cattle using global positioning systems (GPS) collars in the past 21 years (2000–2020) to aid the development of more sustainable grazing livestock systems. The 84 studies identified were undertaken in several regions of the world, in diverse production systems, under different climate conditions and with varied methodologies and animal types. This work presents the information in categories according to the main findings reviewed, covering management, external and animal factors driving animal movement patterns. The results showed that some variables, such as stocking rate, water and shade location, weather conditions and pasture (terrain and vegetation) characteristics, have a significant impact on the behaviour of grazing cattle. Other types of bio-loggers can be deployed in grazing ruminants to gain insights into their metabolism and its relationship with the landscape they utilise. Changing management practices based on these findings could improve the use of grasslands towards more sustainable and productive livestock systems.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

On the Search for Grazing Personalities: From Individual to Collective Behaviors

While grazing lands can offer a diverse range of forages, individuals within herds prefer to graze some habitats and not others. They can have consistent differences in grazing patterns and occupy specific spatial domains, whilst developing tactics and strategies for foraging that are specific to their grazing personalities. In this review, we explore the development of our understanding of grazing personalities, as we move away from the search for an "optimal animal" toward designing behavior-customized herds with an arrangement of individual grazing personalities that enhance ecosystem services and productivity. We present a "grazing personality model" that accounts for the personality of individual animals and for collective behaviors of herds. We argue that grazing personalities of grazing ruminants and other large herbivores are in part genetically determined, and that they can act at the individual and collective level. The social and biophysical environments as well as the emotional state of animals regulate the expression of "grazing genes" that are observed phenotypically as distinct grazing personalities. The reproductive and sexual successes of individuals and herds filter for allele variants of grazing genes and in turn determines their relative frequency. While the selection of one grazing personality may be adequate for homogeneous pastoral systems, the design of herds with a range of grazing personalities that are matched to the habitat diversity may be a better approach to improving the distribution of grazing animals, enhancing ecosystem services, and maximizing productivity.