Search

Background: In recent years, interest has grown in quantifying resilience in livestock by examining deviations in target phenotypes. This method is based on the idea that variability in these phenotypes reflects an animal's ability to adapt to external factors. By utilizing routinely collected time-series feed intake data in pigs, researchers can obtain a broad measure of resilience. This measure extends beyond specific conditions, capturing the impact of various unknown external factors that influence phenotype variations. Importantly, this method does not require additional phenotyping investments. Despite growing interest, the relationship between resilience indicators-calculated as deviations from longitudinally recorded target traits-and the mean of those traits remains largely unexplored. This gap raises the risk of inadvertently selecting for the mean rather than accurately capturing true resilience. Additionally, distinguishing between random phenotype fluctuations (white noise) and structural variations linked to resilience poses a challenge. With the aim of developing general resilience indicators applicable to commercial swine populations, we devised four resilience indicators utilizing daily feed consumption as the target trait. These include a canonical resilience indicator (BALnVar) and three novel ones (BAMaxArea, SPLnVar, and SPMaxArea), designed to minimize noise and ensure independence from daily feed consumption. We subsequently integrated these indicators with Whole Genome Sequencing using SLEMM algorithm, data from 1,250 animals to assess their efficacy in capturing resilience and their independence from the mean of daily feed consumption., Results: Our findings revealed that conventional resilience indicators failed to differentiate from the mean of daily feed consumption, underscoring potential limitations in accurately capturing true resilience. Notably, significant associations involving conventional resilience indicators were identified on chromosome 1, which is commonly linked to body weight., Conclusion: We observed that deviations in feed consumption can effectively serve as indicators for selecting resilience in commercial pig farming, as confirmed by the identification of genes such as PKN1 and GYPC. However, the identification of other genes, such as RNF152, related to growth, suggests that common resilience quantification methods may be more closely related to the mean of daily feed consumption rather than capturing true resilience., Competing Interests: Declarations. Animal Ethics and Consent to Participate declarations: Not applicable. All data used for this study came from an existing dataset, generated in nucleus farms belonging to Smithfield Premium Genetics Consent for publication: Not applicable. Competing interests: At the time of submission, Yijian Huang was an employee of Smithfield. The results from this study do not relate to the company’s marketing strategy., (© 2024. The Author(s).)

The feed consumption and feeding patterns of group-housed livestock and their productive outputs can be affected by interactions among group members, such as competition for feeder access and aggression. The impact of competition is especially significant when feed restriction is implemented during the fattening stage, a common practice in rabbit farms to reduce mortality from digestive disorders during this period. The overall aim was to quantify the importance of social interaction effects at the genetic and environmental level on feed efficiency, feeding behaviour and social ranking traits in rabbit populations having access to electronic feeders whilst they were housed in groups during fattening. This was done by quantifying novel feeding behaviour (FB) and social ranking (SR) traits and by estimating the genetic parameters and genetic correlations between all combinations of direct and social effects of these traits and residual feed intake (RFI), an indicator of feed efficiency. These effects were estimated in animals fed either ad libitum (AL) or on a time-based feed restriction (R) and were selected for feed efficiency on these feeding regimes for six generations. We found that there were substantial social genetic and social litter effects on RFI and FB traits, manifesting as antagonistic relationships between direct and social genetic effects. These effects were stronger amongst the R, as opposed to the AL animals. Despite these antagonistic relationships, the large social genetic effects resulted in substantial heritable variation available for selection of these traits, and therefore feeding behaviour could be changed by genetic selection, especially for R animals. The indicators of an animal's SR were also heritable and subject to social genetic effects and therefore may respond to selection. However, the phenotypic population mean would not change, because such traits are context-specific. The animals that took most of the feeding resources and had priority access to the feeder had a higher RFI, that is, they were less efficient, but they made their cage mates more efficient. This is the first time that the genetic correlations between direct effects of RFI and direct effects of FB and SR traits, as well as between social effects of RFI and direct effects of FB traits and SR traits, have been quantified. Results could help to understand the mechanisms by which an animal exerts its influence on the feed efficiency of its cage mates under different feeding regimes and assist in the development of breeding strategies., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The study aimed at evaluating how sires, classified for their additive genetic effects on residual feed intake (RFI) of ad libitum-fed progeny, influence growth performance, tissue accretion, and gain composition in restricted-fed offspring (96-168 kg body weight, BW). A total of 416 purebred C21 Goland pigs, offspring of 23 sires, were randomly allocated to three feeding groups: ad libitum, restricted medium-protein, or restricted low-protein. Empty BW, body lipid mass and body protein mass were estimated from individual BW and backfat measures using literature equations. Residuals of a linear regression of average daily feed intake on average empty BW, body lipid and protein daily gains were used as estimates of individual RFI in ad libitum-fed pigs. Additive genetic effects of sires on RFI of ad libitum-fed pigs were estimated with a linear animal model and solutions of the model were used to allocate the sires to low- (LRFI), medium- (MRFI), or high-RFI (HRFI) groups. Restricted-fed progeny of LRFI sires exhibited reduced average daily feed intake (-3%) compared to MRFI and HRFI progeny. This indicates that LRFI progeny make a more efficient use of energy intake and implies that variation in RFI across families, assessed under ad libitum feeding, is related to the across-family variation in feed efficiency detected under restricted feeding. LRFI progeny exhibited also a lower feed conversion ratio (-11%), partially resulting from of a 3% increase in growth rate compared with HRFI. Thus, LRFI progeny consumed less feed, while growing at a similar or slightly higher rate than MRFI or HRFI. No significant differences across sire classes were observed for daily tissue accretion and gain composition. Hence, we can hypothesise that efficient sires would not affect carcass leanness of heavy pig progeny fed restricted., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mondin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first dataset consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear-tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second dataset consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; SAE Afikim, Kibbutz Afikim, Israel) and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included DMI, change in BW, metabolic BW, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) and lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed-efficient cows within the herd., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Large datasets allow estimation of feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included DIM, age-parity subclass, trial date, management group, and BW change during 28- and 42-d feeding trials in mid lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the ECM formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine based on NE L equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with BW and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein., (Copyright © 2024 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The coordination of food intake, energy storage, and expenditure involves complex interactions between hypothalamic neurons and peripheral tissues including pancreatic islets, adipocytes, muscle, and liver. Previous research shows that deficiency of the transcription factor Alx3 alters pancreatic islet-dependent glucose homeostasis. In this study we carried out a comprehensive assessment of metabolic alterations in Alx3 deficiency. We report that Alx3-deficient mice exhibit decreased food intake without changes in body weight, along with reduced energy expenditure and altered respiratory exchange ratio. Magnetic resonance imaging reveals increased adiposity and decreased muscle mass, which was associated with markers of motor and sympathetic denervation. By contrast, Alx3-deficient mice on a high-fat diet show attenuated weight gain and improved insulin sensitivity, compared to control mice. Gene expression analysis demonstrates altered lipogenic and lipolytic gene profiles. In wild type mice Alx3 is expressed in hypothalamic arcuate nucleus neurons, but not in major peripheral metabolic organs. Functional diffusion-weighted magnetic resonance imaging reveals selective hypothalamic responses to fasting in the arcuate nucleus of Alx3-deficient mice. Additionally, altered expression of proopiomelanocortin and melanocortin-3 receptor mRNA in the hypothalamus suggests impaired regulation of feeding behavior. This study highlights the crucial role for Alx3 in governing food intake, energy homeostasis, and metabolic nutrient partitioning, thereby influencing body mass composition., (© 2024. The Author(s).)

The GHRL, LEAP2, and GHSR system have recently been identified as important regulators of feed intake in mammals and chickens. However, the complete cloning of the quail GHRL (qGHRL) and quail LEAP2 (qLEAP2) genes, as well as their association with feed intake, remains unclear. This study cloned the entire qGHRL and qLEAP2 cDNA sequence in Chinese yellow quail (Coturnix japonica), including the 5' and 3' untranslated regions. Sanger sequencing analysis revealed no missense mutations in the coding region of qGHRL and qLEAP2. Subsequently, phylogenetic analysis and protein homology alignment were conducted on the qGHRL and qLEAP2 in major poultry species. The findings of this research indicated that the qGHRL and qLEAP2 sequences exhibit a high degree of similarity with those of chicken and turkey. Specifically, the N-terminal 6 amino acids of GHRL mature peptides and all the mature peptide sequence of LEAP2 exhibited consistent patterns across all species examined. The analysis of tissue gene expression profiles indicated that qGHRL was primarily expressed in the proventriculus and brain tissue, whereas qLEAP2 exhibited higher expression levels in the intestinal tissue, kidney, and liver tissue, differing slightly from previous studies conducted on chicken. It is necessary to investigate the significance of elevated expression of qGHRL in brain and qLEAP2 in kidney in the future. Further research has shown that the expression of qLEAP2 can quickly respond to changes in different energy states, whereas qGHRL does not exhibit the same capability. Overall, this study successfully cloned the complete cDNA sequences of qGHRL and qLEAP2, and conducted a comprehensive examination of their tissue expression profiles and gene expression levels in the main expressing organs across different energy states. Our current findings suggested that qLEAP2 is highly expressed in the liver, intestine, and kidney, and its expression level is regulated by feed intake., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Heterosis has been widely utilized in chickens. The nonadditive inheritance of genes contributes to this biological phenomenon. However, the role of circRNAs played in the heterosis is poorly determined. In this study, we observed divergent heterosis for residual feed intake (RFI) between 2 crossbreds derived from a reciprocal cross between White Leghorns and Beijing You chickens. Then, circRNA landscape for 120 samples covering the hypothalamus, liver, duodenum mucosa and ovary were profiled to elucidate the regulatory mechanisms of heterosis. We detected that a small proportion of circRNAs (7.83-20.35%) were additively and non-additively expressed, in which non-additivity was a major inheritance of circRNAs in the crossbreds. Tissue-specific expression of circRNAs was prevalent across 4 tissues. Weighted gene co-expression network analysis revealed circRNA-mRNA co-expression modules associated with feed intake and RFI in the hypothalamus and liver, and the co-expressed genes were enriched in oxidative phosphorylation pathway. We further identified 8 nonadditive circRNAs highly correlated with 16 nonadditive genes regulating negative heterosis for RFI in the 2 tissues. Circ-ITSN2 was validated in the liver tissue for its significantly positive correlation with PGPEP1L. Moreover, the bioinformatic analysis indicated that candidate circRNAs might be functioned by binding the microRNAs and interacting with the RNA binding proteins. The integration of multi-tissue transcriptome firstly linked the association between tissue-specific circRNAs and the heterosis for feed intake and efficiency in chicken, which provide novel insights into the molecular mechanism underlying heterosis for feed efficiency. The validated circRNAs can act as potential biomarkers for predicting RFI and its heterosis., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

BMAL2 (ARNTL2) is a paralog of BMAL1 that can form heterodimers with the other circadian factors CLOCK and NPAS2 to activate transcription of clock and clock-controlled genes. To assess a possible role of Bmal2 in the circadian regulation of metabolism, we investigated daily variations of energy metabolism, feeding behavior, and locomotor behavior, as well as ability to anticipate restricted food access in male mice knock-out for Bmal2 (B2KO). While their amount of food intake and locomotor activity were normal compared with wild-type mice, B2KO mice displayed increased adiposity (1.5-fold higher) and fasted hyperinsulinemia (fourfold higher) and tended to have lower energy expenditure at night. Impairment of the master clock in the suprachiasmatic nuclei was evidenced by the shorter free-running period (-14 min/cycle) of B2KO mice compared with wild-type controls and by a loss of daily rhythmicity in expression of intracellular metabolic regulators (e.g., Lipoprotein lipase and Uncoupling protein 2 ). The circadian window of eating was longer in B2KO mice. The circadian patterns of food intake and meal numbers were bimodal in control mice but not in B2KO mice. In response to restricted feeding, food-anticipatory activity was almost prevented in B2KO mice, suggesting altered food clock that controls anticipation of food availability. In the mediobasal hypothalamus of B2KO mice, expression of genes coding orexigenic neuropeptides (including Neuropeptide y and Agouti-Related Peptide ) was downregulated, while Lipoprotein lipase expression lost its rhythmicity. Together, these data highlight that BMAL2 has major impacts on brain regulation of metabolic rhythms, sleep-wake cycle, and food anticipation., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Food intake and energy balance are tightly regulated by a group of hypothalamic arcuate neurons expressing the proopiomelanocortin ( POMC) gene. In mammals, arcuate-specific POMC expression is driven by two cis -acting transcriptional enhancers known as nPE1 and nPE2. Because mutant mice lacking these two enhancers still showed hypothalamic Pomc mRNA, we searched for additional elements contributing to arcuate Pomc expression. By combining molecular evolution with reporter gene expression in transgenic zebrafish and mice, here, we identified a mammalian arcuate-specific Pomc enhancer that we named nPE3, carrying several binding sites also present in nPE1 and nPE2 for transcription factors known to activate neuronal Pomc expression, such as ISL1, NKX2.1, and ERα. We found that nPE3 originated in the lineage leading to placental mammals and remained under purifying selection in all mammalian orders, although it was lost in Simiiformes (monkeys, apes, and humans) following a unique segmental deletion event. Interestingly, ablation of nPE3 from the mouse genome led to a drastic reduction (>70%) in hypothalamic Pomc mRNA during development and only moderate (<33%) in adult mice. Comparison between double (nPE1 and nPE2) and triple (nPE1, nPE2, and nPE3) enhancer mutants revealed the relative contribution of nPE3 to hypothalamic Pomc expression and its importance in the control of food intake and adiposity in male and female mice. Altogether, these results demonstrate that nPE3 integrates a tripartite cluster of partially redundant enhancers that originated upon a triple convergent evolutionary process in mammals and that is critical for hypothalamic Pomc expression and body weight homeostasis., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: Provision of feed is a major determinant of overall profitability in beef production systems, accounting for up to 75% of the variable costs. Thus, improving cattle feed efficiency, by way of determining the underlying genomic control and subsequently selecting for feed efficient cattle, provides a method through which feed input costs may be reduced. The objective of this study was to undertake gene co-expression network analysis using RNA-Sequence data generated from Longissimus dorsi and liver tissue samples collected from steers of two contrasting breeds (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI), across two consecutive distinct dietary phases (zero-grazed grass and high-concentrate). Categories including differentially expressed genes (DEGs) based on the contrasts of RFI phenotype, breed and dietary source, as well as key transcription factors and proteins secreted in plasma were utilised as nodes of the gene co-expression network., Results: Of the 2,929 DEGs within the network analysis, 1,604 were reported to have statistically significant correlations (≥ 0.80), resulting in a total of 43,876 significant connections between genes. Pathway analysis of clusters of co-expressed genes revealed enrichment of processes related to lipid metabolism (fatty acid biosynthesis, fatty acid β-oxidation, cholesterol biosynthesis), immune function, (complement cascade, coagulation system, acute phase response signalling), and energy production (oxidative phosphorylation, mitochondrial L-carnitine shuttle pathway) based on genes related to RFI, breed and dietary source contrasts., Conclusions: Although similar biological processes were evident across the three factors examined, no one gene node was evident across RFI, breed and diet contrasts in both liver and muscle tissues. However within the liver tissue, the IRX4, NR1H3, HOXA13 and ZNF648 gene nodes, which all encode transcription factors displayed significant connections across the RFI, diet and breed comparisons, indicating a role for these transcription factors towards the RFI phenotype irrespective of diet and breed. Moreover, the NR1H3 gene encodes a protein secreted into plasma from the hepatocytes of the liver, highlighting the potential for this gene to be explored as a robust biomarker for the RFI trait in beef cattle., (© 2024. The Author(s).)

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Improving pigs' ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects., Results: Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P < 0.001)., Conclusions: The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results., (© 2024. The Author(s).)

Background: Feeding costs represent the largest expenditures in beef production. Therefore, the animal efficiency in converting feed in high-quality protein for human consumption plays a major role in the environmental impact of the beef industry and in the beef producers' profitability. In this context, breeding animals for improved feed efficiency through genomic selection has been considered as a strategic practice in modern breeding programs around the world. Copy number variation (CNV) is a less-studied source of genetic variation that can contribute to phenotypic variability in complex traits. In this context, this study aimed to: (1) identify CNV and CNV regions (CNVRs) in the genome of Nellore cattle (Bos taurus indicus); (2) assess potential associations between the identified CNVR and weaning weight (W210), body weight measured at the time of selection (WSel), average daily gain (ADG), dry matter intake (DMI), residual feed intake (RFI), time spent at the feed bunk (TF), and frequency of visits to the feed bunk (FF); and, (3) perform functional enrichment analyses of the significant CNVR identified for each of the traits evaluated., Results: A total of 3,161 CNVs and 561 CNVRs ranging from 4,973 bp to 3,215,394 bp were identified. The CNVRs covered up to 99,221,894 bp (3.99%) of the Nellore autosomal genome. Seventeen CNVR were significantly associated with dry matter intake and feeding frequency (number of daily visits to the feed bunk). The functional annotation of the associated CNVRs revealed important candidate genes related to metabolism that may be associated with the phenotypic expression of the evaluated traits. Furthermore, Gene Ontology (GO) analyses revealed 19 enrichment processes associated with FF., Conclusions: A total of 3,161 CNVs and 561 CNVRs were identified and characterized in a Nellore cattle population. Various CNVRs were significantly associated with DMI and FF, indicating that CNVs play an important role in key biological pathways and in the phenotypic expression of feeding behavior and growth traits in Nellore cattle., (© 2024. The Author(s).)

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20)., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Improving the feeding efficiency of dairy cows is a key component to improve the utilization of land resources and meet the demand for high-quality protein. Advances in genomic methods and omics techniques have made it possible to breed more efficient dairy cows through genomic selection. The aim of this review is to obtain a comprehensive understanding of the biological background of feed efficiency (FE) complex traits in purebred Holstein dairy cows including heritability estimate, and genetic markers, genes, and pathways participating in FE regulation mechanism. Through a literature search, we systematically reviewed the heritability estimation, molecular genetic markers, genes, biomarkers, and pathways of traits related to feeding efficiency in Holstein dairy cows. A meta-analysis based on a random-effects model was performed to combine reported heritability estimates of FE complex. The heritability of residual feed intake, dry matter intake, and energy balance was 0.20, 0.34, and 0.22, respectively, which proved that it was reasonable to include the related traits in the selection breeding program. For molecular genetic markers, a total of 13 single-nucleotide polymorphisms and copy number variance loci, associated genes, and functions were reported to be significant across populations. A total of 169 reported candidate genes were summarized on a large scale, using a higher threshold (adjusted P value < 0.05). Then, the subsequent pathway enrichment of these genes was performed. The important genes reported in the articles were included in a gene list and the gene list was enriched by gene ontology (GO):biological process (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. Three GO:BP terms and four KEGG terms were statistically significant, which mainly focused on adenosine triphosphate (ATP) synthesis, electron transport chain, and OXPHOS pathway. Among these pathways, involved genes such as ATP5MC2, NDUFA, COX7A2, UQCR, and MMP are particularly important as they were previously reported. Twenty-nine reported biological mechanisms along with involved genes were explained mainly by four biological pathways (insulin-like growth factor axis, lipid metabolism, oxidative phosphorylation pathways, tryptophan metabolism). The information from this study will be useful for future studies of genomic selection breeding and genetic structures influencing animal FE. A better understanding of the underlying biological mechanisms would be beneficial, particularly as it might address genetic antagonism., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

High resilience against diseases, changing environmental conditions, and other stress factors and the ability to efficiently recover to normal status, is becoming increasingly important in pig production. Finding new phenotypes that relate to resilience is a crucial step for improving the resilience of pigs through selection. The objective of this study was to extract resilience-related phenotypes based on fluctuations in daily feed intake (DFI, g) and time spent in feeding per day (TPD, min) and to estimate the heritability of these traits and genetic correlations with production traits (PT). Resilience-related traits with high enough heritability and with either favorable or neutral genetic correlation with PT could be used in the selection program to improve the productivity and welfare of pigs. In this study, we used data from 7,347 Finnish Yorkshire, Landrace, and crossbred pigs raised at the test station. Six pig-specific resilience-related phenotypes were extracted from the individual DFI and TPD: root mean square error (RMSE), quantile regression (QR), and coefficient of variation (CV). RMSE was calculated from the differences between the actual DFI (or TPD) and the pig-specific predicted values. QR was based on the number of days that a pig belonged to the group with the lowest 5% of pigs based on DFI (or TPD), and CV was calculated over the daily observations of DFI (or TPD). PT included average daily gain (ADG, g), backfat thickness (BF, mm), and feed conversion rate (FCR, g/g). The heritability estimates for resilience-related traits varied between 0.07 ± 0.02 (QRDFI) and 0.20 ± 0.03 (RMSETPD). The genetic correlations between resilience-related traits and PT were mostly neutral, but for example, RMSEDFI had a favorable genetic correlation with FCR and BF but an unfavorable correlation with ADG. Lastly, we observed that pigs belonging to the lowest 10% group based on their breeding value (BV) for QRTPD had a lower proportion (10% incidence) of sick days compared to the highest 10% BV group (30% incidence). Therefore, pigs exhibiting small TPD variation (related to high resilience) tend to be less susceptible to sickness than pigs with large TPD variation (related to low resilience). Given its moderate heritability, neutral genetic correlation with PT, and positive effect on health, QRTPD can be considered the most promising resilience-related trait in the Finnish production system., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Background: Selecting animals for feed efficiency directly impacts the profitability of the beef cattle industry, which contributes to minimizing the environmental footprint of beef production. Genetic and environmental factors influence animal feed efficiency, leading to phenotypic variability when exposed to different environmental conditions (i.e., temperature and nutritional level). Thus, our aim was to assess potential genotype-by-environment (G × E) interactions for dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle (Bos taurus indicus) based on bi-trait reaction norm models (RN) and evaluate the genetic association between RFI and DMI across different environmental gradient (EG) levels. For this, we used phenotypic information on 12,958 animals (young bulls and heifers) for DMI and RFI recorded during 158 feed efficiency trials., Results: The heritability estimates for DMI and RFI across EG ranged from 0.26 to 0.54 and from 0.07 to 0.41, respectively. The average genetic correlations (± standard deviation) across EG for DMI and RFI were 0.83 ± 0.19 and 0.81 ± 0.21, respectively, with the lowest genetic correlation estimates observed between extreme EG levels (low vs. high) i.e. 0.22 for RFI and 0.26 for DMI, indicating the presence of G × E interactions. The genetic correlation between RFI and DMI across EG levels decreased as the EG became more favorable and ranged from 0.79 (lowest EG) to 0.52 (highest EG). Based on the estimated breeding values from extreme EG levels (low vs. high), we observed a moderate Spearman correlation of 0.61 (RFI) and 0.55 (DMI) and a selection coincidence of 53.3% and 40.0% for RFI and DMI, respectively., Conclusions: Our results show evidence of G × E interactions on feed efficiency traits in Nellore cattle, especially in feeding trials with an average daily gain (ADG) that is far from the expected of 1 kg/day, thus increasing reranking of animals., (© 2023. The Author(s).)

In this study a 3-dimensional (3D) camera system was set up to measure individual feed intake of dairy cows in a commercial in-house setting. The system was developed to identify the cows while eating, predict body weight based on the curvature of the back of the cow, and quantify the amount of feed eaten by the cow at each visit of eating. The identification of the cow was based on recognizing the patterns, colors, and curvatures of the back from a reference database obtained in a corridor after milking, where images were taken of all cows with a simultaneous reading of the electronic ear tag. Body weight is predicted using the curvatures of the back of the cow. Feed intake is quantified as the difference in surface of the feed a cow can reach before and after a visit is initiated. This estimate is in liters but converted to kilograms, using the density of the feed in the specific herd. A total of 9,142 cows were measured in 19 herds across 3 breeds: Jersey (2,513 cows), Red Dairy Cattle (2,813 cows), and Holstein (3,816 cows). Mean daily feed intake was higher for Red Dairy Cattle (61.72 kg) and Holstein (64.59 kg) than for Jersey (55.74 kg). Repeatability estimates for daily feed intake as a weekly average was 0.62, 0.65, and 0.63 for Jersey, Red Dairy, and Holstein cattle, respectively. Mean body weight was higher for Red Dairy (647.9 kg) and Holstein (683.8 kg) than for Jersey (469.6 kg). Repeatability estimates for body weight as a weekly average was 0.83, 0.85, and 0.88 for Jersey, Red Dairy, and Holstein, respectively. The perspectives in having such records available is huge both for the farmer and for the dairy industry. The records can both be used for improving management in farms on an individual cow level and herd level, but also for genetic evaluation and selection as well as testing feeding regimens. Feed intake can be measured on an individual level using a 3D camera system., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Recording complex phenotypes on a large scale is becoming possible with the incorporation of recently developed new technologies. One of these new technologies is the use of 3-dimensional (3D) cameras on commercial farms to measure feed intake and body weight (BW) daily. Residual feed intake (RFI) has been proposed as a proxy for feed efficiency in several species, including cattle, pigs, and poultry. Dry matter intake (DMI) and BW records are required to calculate RFI, and the use of this new technology will help increase the number of individual records more efficiently. The aim of this study was to estimate genetic parameters (including genetic correlations) for DMI and BW obtained by 3D cameras from 6,000 cows in commercial farms from the breeds Danish Holstein, Jersey, and Nordic Red. Additionally, heritabilities per parity and genetic correlations among parities were estimated for DMI and BW in the 3 breeds. Data included 158,000 weekly records of DMI and BW obtained between 2019 and 2022 on 17 commercial farms. Estimated heritability for DMI ranged from 0.17 to 0.25, whereas for BW they ranged from 0.44 to 0.58. The genetic correlations between DMI and BW were moderately positive (0.58-0.65). Genetic correlations among parities in both traits were highly correlated in the 3 breeds, except for DMI between first parity and late parities in Holstein where they were down to 0.62. Based on these results, we conclude that DMI and BW phenotypes measured by 3D cameras are heritable for the 3 dairy breeds and their heritabilities are comparable to those obtained by traditional methods (scales and feed bins). The high heritabilities and correlations of 3D measurements with the true trait in previous studies demonstrate the potential of this new technology for measuring feed intake and BW in real time. In conclusion, 3D camera technology has the potential to become a valuable tool for automatic and continuous recording of feed intake and BW on commercial farms., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Feed costs can amount to 75 percent of the total overhead cost of raising cows for milk production. Meanwhile, the livestock industry is considered a significant contributor to global climate change due to the production of greenhouse gas emissions, such as methane. Indeed, the genetic basis of feed efficiency (FE) is of great interest to the animal research community. Here, we explore the epigenetic basis of FE to provide base knowledge for the development of genomic tools to improve FE in cattle. The methylation level of 37,554 CpG sites was quantified using a mammalian methylation array (HorvathMammalMethylChip40) for 48 Holstein cows with extreme residual feed intake (RFI). We identified 421 CpG sites related to 287 genes that were associated with RFI, several of which were previously associated with feeding or digestion issues. Activator of transcription and developmental regulation ( AUTS2 ) is associated with digestive disorders in humans, while glycerol-3-phosphate dehydrogenase 2 ( GPD2 ) encodes a protein on the inner mitochondrial membrane, which can regulate glucose utilization and fatty acid and triglyceride synthesis. The extensive expression and co-expression of these genes across diverse tissues indicate the complex regulation of FE in cattle. Our study provides insight into the epigenetic basis of RFI and gene targets to improve FE in dairy cattle.

Background: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR)., Results: The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm., Conclusions: Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs., (© 2023. The Author(s).)

Background: Feed efficiency is a research priority to support a sustainable meat production. It is recognized as a complex trait that integrates multiple biological pathways orchestrated in and by various tissues. This study aims to determine networks between biological entities to explain inter-individual variation of feed efficiency in growing pigs., Results: The feed conversion ratio (FCR), a measure of feed efficiency, and its two component traits, average daily gain and average daily feed intake, were obtained from 47 growing pigs from a divergent selection for residual feed intake and fed high-starch or high-fat high-fiber diets during 58 days. Datasets of transcriptomics (60 k porcine microarray) in the whole blood and metabolomics (1H-NMR analysis and target gas chromatography) in plasma were available for all pigs at the end of the trial. A weighted gene co-expression network was built from the transcriptomics dataset, resulting in 33 modules of co-expressed molecular probes. The eigengenes of eight of these modules were significantly ([Formula: see text]) or tended to be ([Formula: see text]) correlated to FCR. Great homogeneity in the enriched biological pathways was observed in these modules, suggesting co-expressed and co-regulated constitutive genes. They were mainly enriched in genes participating to immune and defense-related processes, and to a lesser extent, to translation, cell development or learning. They were also generally associated with growth rate and percentage of lean mass. In the whole network, only one module composed of genes participating to the response to substances, was significantly associated with daily feed intake and body adiposity. The plasma profiles in circulating metabolites and in fatty acids were summarized by weighted linear combinations using a dimensionality reduction method. Close association was thus found between a module composed of co-expressed genes participating to T cell receptor signaling and cell development process in the whole blood and related to FCR, and the circulating concentrations of polyunsaturated fatty acids in plasma., Conclusion: These systemic approaches have highlighted networks of entities driving key biological processes involved in the phenotypic difference in feed efficiency between animals. Connecting transcriptomics and metabolic levels together had some additional benefits., (© 2023. The Author(s).)

Background: Heterosis is routinely exploited to improve animal performance. However, heterosis and its underlying molecular mechanism for feed intake and efficiency have been rarely explored in chickens. Feed efficiency continues to be an important breeding goal trait since feed accounts for 60 to 70% of the total production costs in poultry. Here, we profiled the mRNA-lncRNA landscape of 96 samples of the hypothalamus, liver and duodenum mucosa from White Leghorn (WL), Beijing-You chicken (YY), and their reciprocal crosses (WY and YW) to elucidate the regulatory mechanisms of heterosis., Results: We observed negative heterosis for both feed intake and residual feed intake (RFI) in YW during the laying period from 43 to 46 weeks of age. Analysis of the global expression pattern showed that non-additivity was a major component of the inheritance of gene expression in the three tissues for YW but not for WY. The YW-specific non-additively expressed genes (YWG) and lncRNA (YWL) dominated the total number of non-additively expressed genes and lncRNA in the hypothalamus and duodenum mucosa. Enrichment analysis of YWG showed that mitochondria components and oxidation phosphorylation (OXPHOS) pathways were shared among the three tissues. The OXPHOS pathway was enriched by target genes for YWL with non-additive inheritance of expression in the liver and duodenum mucosa. Weighted gene co-expression network analysis revealed divergent co-expression modules associated with feed intake and RFI in the three tissues from WL, YW, and YY. Among the negatively related modules, the OXPHOS pathway was enriched by hub genes in the three tissues, which supports the critical role of oxidative phosphorylation. Furthermore, protein quantification of ATP5I was highly consistent with ATP5I expression in the liver, which suggests that, in crossbred YW, non-additive gene expression is down-regulated and decreases ATP production through oxidative phosphorylation, resulting in negative heterosis for feed intake and efficiency., Conclusions: Our results demonstrate that non-additively expressed genes and lncRNA involved in oxidative phosphorylation in the hypothalamus, liver, and duodenum mucosa are key regulators of the negative heterosis for feed intake and RFI in layer chickens. These findings should facilitate the rational choice of suitable parents for producing crossbred chickens., (© 2023. ’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE).)

The adipose-derived hormone leptin acts via its receptor (LepRb) in the brain to control energy balance. A potentially unidentified population of GABAergic hypothalamic LepRb neurons plays key roles in the restraint of food intake and body weight by leptin. To identify markers for candidate populations of LepRb neurons in an unbiased manner, we performed single-nucleus RNA-Seq of enriched mouse hypothalamic LepRb cells, identifying several previously unrecognized populations of hypothalamic LepRb neurons. Many of these populations displayed strong conservation across species, including GABAergic Glp1r-expressing LepRb (LepRbGlp1r) neurons, which expressed more Lepr than other LepRb cell populations. Ablating Lepr from LepRbGlp1r cells provoked hyperphagic obesity without impairing energy expenditure. Similarly, improvements in energy balance caused by Lepr reactivation in GABA neurons of otherwise Lepr-null mice required Lepr expression in GABAergic Glp1r-expressing neurons. Furthermore, restoration of Glp1r expression in LepRbGlp1r neurons in otherwise Glp1r-null mice enabled food intake suppression by the GLP1R agonist, liraglutide. Thus, the conserved GABAergic LepRbGlp1r neuron population plays crucial roles in the suppression of food intake by leptin and GLP1R agonists.

There is growing interest in improving feed efficiency traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of residual feed intake (RFI) and its component traits [dry matter intake (DMI), metabolic body weight (MBW), and average daily gain (ADG)] in Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial body weight = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, OH) as part of the EcoFeed program, which aims to improve feed efficiency by genetic selection. The RFI was estimated as the difference between a heifer's actual feed intake and expected feed intake, which was determined by regression of DMI against midpoint MBW, age, and ADG across each trial. A total of 61,283 SNPs were used in genomic analyses. Animals with phenotypes and genotypes were used as training population, and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBV), respectively. Breeding values of the prediction population were estimated by using the 2-step approach: deriving the prediction equation of GEBV from the training population for estimation of GEBV of prediction population with only genotypes. Reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of training population GEBV and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the trial period, with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTA) of the training population (-0.94 to 0.75) was higher compared with the range of gPTA (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from the training population was 58%, and that of prediction population was 39%. The genomic prediction of RFI provides new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

The Korat chicken (KR) is a slow-growing Thai chicken breed with relatively poor feed efficiency (FE) but very tasty meat with high protein and low fat contents, and a unique texture. To enhance the competitiveness of KR, its FE should be improved. However, selecting for FE has an unknown effect on meat characteristics. Thus, understanding the genetic basis underlying FE traits and meat characteristics is needed. In this study, 75 male KR birds were raised up to 10 wk of age. For each bird, the feed conversion ratio (FCR), residual feed intake (RFI), and physicochemical properties, flavor precursors, and biological compounds in the thigh meat were evaluated. At 10 wk of age, thigh muscle samples from 6 birds (3 with high FCR and 3 with low FCR values) were selected, and their proteomes were investigated using a label-free proteomic method. Weighted gene coexpression network analysis (WGCNA) was used to screen the key protein modules and pathways. The WGCNA results revealed that FE and meat characteristics significantly correlated with the same protein module. However, the correlation was unfavorable; improving FE may result in a decrease in meat quality through the alteration in biological processes including glycolysis/gluconeogenesis, metabolic pathway, carbon metabolism, biosynthesis of amino acids, pyruvate metabolism, and protein processing in the endoplasmic reticulum. The hub proteins of the significant module (TNNT1, TNNT3, TNNI2, TNNC2, MYLPF, MYH10, GADPH, PGK1, LDHA, and GPI) were also identified to be associated with energy metabolism, and muscle growth and development. Given that the same proteins and pathways are present in FE and meat characteristics but in opposite directions, selection practices for KR should simultaneously consider both trait groups to maintain the high meat quality of KR while improving FE., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Improving cattle feed efficiency through selection of residual feed intake (RFI) is a widely accepted approach to sustainable beef production. A greater understanding of the molecular control of RFI in various breeds offered contrasting diets is necessary for the accurate identification of feed efficient animals and will underpin accelerated genetic improvement of the trait. The aim of this study was to determine genes and biological processes contributing to RFI across varying breed type and dietary sources in skeletal muscle tissue. Residual feed intake was calculated in Charolais and Holstein-Friesian steers across multiple dietary phases (phase-1: high concentrate (growing-phase); phase-2: zero-grazed grass (growing-phase); phase-3: high concentrate (finishing-phase). Steers divergent for RFI within each breed and dietary phase were selected for muscle biopsy collection, and muscle samples subsequently subjected to RNAseq analysis. No gene was consistently differentially expressed across the breed and diet types examined. However, pathway analysis revealed commonality across breeds and diets for biological processes including fatty acid metabolism, immune function, energy production and muscle growth. Overall, the lack of commonality of individual genes towards variation in RFI both within the current study and compared to the published literature, suggests other genomic features warrant further evaluation in relation to RFI., (© 2023. The Author(s).)

A total of 197 nulliparous rabbits (from three paternal lines) were used to test potential strategies to overcome the consequences on reproduction associated with the selection for high growth rate. The R line was selected for growth rate during the growing period for 37 generations, the RF line was founded through a high selection intensity of elite animals of the R line, and the RFLP line, which was obtained by backcrossing RF animals with the LP line (a long-lived productive maternal line, characterised by high resilience). BW, perirenal fat thickness, fertility, daily feed intake, milk yield and blood metabolites of females were controlled from 1st artificial insemination to 3rd parturition. Litter size, litter weight, individual weight and feed ingestion of kits were controlled from birth to weaning. Our results show that RF females were significantly lighter than R and RFLP females throughout the trial (-5.0%; P < 0.05). Furthermore, RF animals had a higher fertility rate than RFLP females, at first cycle (+10.5 percentage points; P < 0.05). However, RFLP had a higher fertility rate than RF females at second cycle (+21.5 percentage points; P < 0.01). On average, RFLP females had higher perirenal fat thickness than R females at parturition (+3.0%; P < 0.05) and higher daily feed intake than of R and RF females during gestation and late lactation (+9.7 and +8.7%, respectively; P < 0.05). RFLP females produced more milk than R and RF females in the two first lactations (+18.5%; P < 0.001). In the first three parturitions, R females delivered fewer kits born alive (-1.7 kits than RF and RFLP; P < 0.05). In addition, R females' blood had a higher concentration of glutamine and glutamate than RFLP (+24 and +22.7%, respectively; P < 0.05). RFLP litters were heavier than both R and RF litters throughout lactation. However, R kits were heavier at birth than RF and RFLP (+7.9%). Results suggest that the foundation of a paternal line using elite animals could generate females with better early reproductive performance. In addition, backcrossing the RF line with a maternal LP line resulted in a genetic line whose females had a different resource allocation strategy to foster reproduction during the studied period., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Triglyceride (TG) metabolism is a key factor that affects residual feed intake (RFI); however, few studies have been conducted on the related gene expression in poultry. The aim of the present study was to investigate the expression of genes and their associations with RFI in meat-type ducks. Weight gain and feed intake (FI) at an age 21-42 days were measured and the RFI was calculated. Quantitative PCR was used to test the expression of the six identified genes, namely peroxisome proliferator activated receptor γ (PPARγ), glycerol kinase 2 (GK2), glycerol-3-phosphate dehydrogenase 1 (GPD1), glycerol kinase (GYK), lipase E (LIPE), and lipoprotein lipase (LPL) in the duodenum in the high RFI (HRFI) and low RFI (LRFI) groups. The results demonstrated that daily feed intake, feed conversion ratio (FCR), and RFI were markedly higher in HRFI ducks than those in LRFI ducks. Moreover, the levels of expression of PPARγ, GK2, and LIPE were significantly higher in the LRFI group than those in the HRFI group. Correlation analysis showed that PPARγ, GK2, and LIPE were significantly negatively associated with FCR and RFI. Furthermore, gene expression levels were negatively associated with the measured phenotype. The association of GK2 with PPARγ, GPD1, LPL, and LIPE was positive. The relationship between the TG related gene and RFI was further verified to potentially develop pedigree poultry breeding programs. The results of this study suggested that the expression of genes correlated with TG metabolism and transport is up-regulated in the duodenum of ducks with high feed efficiency. PPARγ, GK2, and LIPE are important genes that affect RFI. The results of the present study provide information that could facilitate further explorations of the mechanism of RFI and potential markers at the molecular and cellular levels., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Shui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Some common single-nucleotide polymorphisms of the brain-derived neurotrophic factor (BDNF) gene have been associated not only with the neurodegenerative diseases but also with some eating disorders. The aim of this study was to assess the possible differences in the obesity-related and glucose metabolism parameters between some BDNF genotypes', that may depend on the daily energy and macronutrients intake. In 484 adult participants we performed the anthropometric measurements, body composition analysis, and body fat distribution. The daily dietary intake was assessed using the 3-day food intake diaries. Blood glucose and insulin concentrations were measured at fasting and during oral glucose tolerance tests. Moreover, the visceral adipose tissue/subcutaneous adipose tissue (VAT/SAT) ratio and homeostatic model assessment of insulin resistance were calculated. We noted that participants carrying the GG genotype had lower skeletal muscle mass and fat free mass (FFM) when carbohydrate intake was > 48%, whereas they presented higher fat-free mass (FFM), and surprisingly higher total cholesterol and LDL-C concentrations when daily fiber intake was > 18 g. Moreover, in these subjects we noted higher waist circumference, BMI, and fasting glucose and insulin concentrations, when > 18% of total daily energy intake was delivered from proteins, and higher VAT content and HDL-C concentrations when > 30% of energy intake was derived from dietary fat. Our results suggest that glucose homeostasis and obesity-related parameters in carriers of some common variants of BDNF gene, especially in the GG (rs10835211) genotype carriers, may differ dependently on daily energy, dietary macronutrients and fiber intake., (© 2023. The Author(s).)

Improving feed efficiency is an important target for poultry breeding. Feed efficiency is affected by host genetics and the gut microbiota, but many of the mechanisms remain elusive in laying hens, especially in the late laying period. In this study, we measured feed intake, body weight, and egg mass of 714 hens from a pedigreed line from 69 to 72 wk of age and calculated the residual feed intake (RFI) and feed conversion ratio (FCR). In addition, fecal samples were also collected for 16S ribosomal RNA gene sequencing (V4 region). Genetic analysis was then conducted in DMU packages by using AI-REML with animal model. Moderate heritability estimates for FCR (h 2  = 0.31) and RFI (h 2  = 0.52) were observed, suggesting that proper selection programs can directly improve feed efficiency. Genetically, RFI was less correlated with body weight and egg mass than that of FCR. The phenotypic variance explained by gut microbial variance is defined as the microbiability (m 2 ). The microbiability estimates for FCR (m 2  = 0.03) and RFI (m 2  = 0.16) suggested the gut microbiota was also involved in the regulation of feed efficiency. In addition, our results showed that the effect of host genetics on fecal microbiota was minor in three aspects: 1) microbial diversity indexes had low heritability estimates, and genera with heritability estimates more than 0.1 accounted for only 1.07% of the tested fecal microbiota; 2) the genetic relationship correlations between host genetics and different microbial distance were very weak, ranging from -0.0057 to -0.0003; 3) the microbial distance between different kinships showed no significant difference. Since the RFI has the highest microbiability, we further screened out three genera, including Anaerosporobacter, Candidatus Stoquefichus, and Fournierella, which were negatively correlated with RFI and played positive roles in improving the feed efficiency. These findings contribute to a great understanding of the genetic background and microbial influences on feed efficiency., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Neuromedin U (NMU) has a critical function on the regulation of food intake in mammals, while the information is little in teleost. To investigate the function of NMU on appetite regulation of Siberian sturgeon ( Acipenser baerii ), this study first cloned nmu cDNA sequence that encoded 154 amino acids including NMU-25 peptide. Besides, the results showed that nmu mRNA was widely distributed in various tissues especially in the hypothalamus and telencephalon. The results of nutritional status (pre-feeding and post-feeding, fasting and re-feeding) experiments showed that nmu mRNA expression was significantly decreased at 1 and 3 h after feeding in different brain regions. Similarly, after feeding, the expression of nmu significantly decreased in peripheral tissues. Moreover, nmu expression in the hypothalamus was significantly increased after fasting 1 d, but decreased after fasting 17 d, which was significantly reversed after re-feeding. However, other brain regions like telencephalon and peripheral tissues like oesophagus, intestinum valvula and liver have different change patterns. Further study showed that acute i.c.v. and i.p. injection of NMU and chronic i.p. injection of NMU significantly reduced the food intake in a dose-dependent mode. In addition, the expressions of several critical appetite factors ( nmu , aplein , cart , cck , ghrelin , npy , nucb2 , pyy and ucn3 ) were significantly affected by acute NMU-25 administration in the hypothalamus, intestinum valvula and liver. These results indicate that NMU-25 has the anorexigenic function on food intake by affecting different appetite factors in Siberian sturgeon, which provides a foundation for further exploring the appetite regulation networks in fish.

Residual feed intake (RFI) is crucial economic indicator used for calculating the feed efficiency of growing beef cattle. circRNA plays an important biological role in gene transcriptional regulation, but little is known about its potential functional regulation underlying RFI phenotypic variation. As the core center of regulation of animal feeding, the hypothalamus is closely associated with RFI. Therefore, the present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing from hypothalamic tissue samples, in order to gain insight into the potential regulatory role of circRNAs in bovine RFI phenotypic variation. Differentially expressed genes were detected by RNA sequencing for beef cattle in the high and low RFI groups, followed by GO, KEGG enrichment, and circRNA-miRNA co-expression network analysis. A total of 257 circRNAs were differentially expressed between the two groups, with 128 significantly upregulated and 129 significantly downregulated genes in H group compared to L group. Among them, 9 unique circRNAs were present in group L and 4 unique circRNAs were present in group H. GO and KEGG enrichment analysis of the source genes of the differentially expressed circRNAs revealed that they were mainly involved in metabolic processes, such as cellular metabolic processes, cellular macromolecular metabolic processes, and regulatory pathways related to nutrient metabolism, including protein and amino acid metabolism, as well as vitamin metabolism and pancreatic secretion associated with the animal feeding behavior. The circRNAs detected in this study were mostly novel, and have not been investigated directly to be associated with the RFI phenotype. Interestingly, most miRNAs of differentially expressed circRNAs predicted based on the circRNA-miRNA co-expression network analysis by using top 50 differentially expressed circRNAs and 13 unique circRNAs, have been reported to be related to animal RFIs, implying that circRNAs in bovine hypothalamic tissue may regulate phenotypic variation in RFI through miRNAs. The study results illustrate the complex biological functions of the hypothalamus in regulating feed efficiency and showing the potential role of circRNAs in the feeding behavior regulation of livestock, which would contributing to expanding the understanding of circRNA., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Feed and water intake are two important aspects of cattle production that greatly impact the profitability, efficiency, and sustainability of producers. Feed and, to a lesser degree, water intake have been studied previously; however, there is little research on their associated animal behaviors and there is a lack of standardized phenotypes for these behaviors. Feed and water intakes obtained with an Insentec system (Hokofarm Group, The Netherlands) from 830 crossbred steers were used to compute five intake behaviors for both feed and water: daily sessions (DS), intake rate (IR), session size (SS), time per session (TS), and session interval (SI). Variance components and heritabilities were estimated for each trait. Heritabilities for feed intake behaviors were 0.50 ± 0.12, 0.63 ± 0.12, 0.40 ± 0.13, 0.35 ± 0.12, and 0.60 ± 0.12 for DS, IR, SS, TS, and SI, respectively. Heritabilities for water intake behaviors were 0.56 ± 0.11, 0.88 ± 0.07, 0.70 ± 0.11, 0.54 ± 0.12, and 0.80 ± 0.10 for NS, IR, SS, TS, and SI, respectively. Daily dry matter intake (DDMI) and daily water intake (DWI) had heritabilities of 0.57 ± 0.11 and 0.44 ± 0.11. Phenotypic correlations varied between pairs of traits (-0.83 to 0.82). Genetic correlations between DDMI and feed intake behaviors were moderate to high, while genetic correlations between DWI and water intake behaviors were low to moderate. Several significant single nucleotide polymorphisms (SNP) were identified for the feed and water intake behaviors. Genes and previously reported quantitative trait loci near significant SNPs were evaluated. The results indicated that feed and water intake behaviors are influenced by genetic factors and are heritable, providing one additional route to evaluate or manipulate feed and water intake., (Published by Oxford University Press on behalf of the American Society of Animal Science 2023.)

Most of the metabolizable energy that a cow uses during a production year is for maintenance; however, less is known about the heritability of maintenance compared to other traits that can be measured directly. Feed intake is a heritable trait in the mature cow and most of the feed consumed is used for maintenance. We hypothesized that maintenance energy was a heritable trait. Individual feed intake was measured for 84 or 85 d on 5 yr old pregnant cows (N = 887) from a pedigreed population of cattle that represent prominent breeds in the United States. Phenotypic mean (± SD) values were 654 ± 68 kg for cow body weight, 0.21 ± 0.24 kg/d for average daily gain, and 175 ± 17 d for midpoint fetal age. Dry matter intake averaged (± SD) 10.84 ± 1.41 kg/d. Metabolizable energy for maintenance was estimated by subtracting the metabolizable energy used for conceptus growth and tissue accretion from metabolizable energy intake. Metabolizable energy for maintenance averaged (± SD) 139 ± 18 ME kcal/d/BW kg0.75 and had a heritability of 0.31 ± 0.11. Cows have a moderate heritability for maintenance suggesting an opportunity for selection., (Published by Oxford University Press on behalf of the American Society of Animal Science 2023.)

Improvement of feed efficiency (FE) in pigs is an important milestone in order to reduce the economic and environmental impact of pig production. The goal of finding biomarkers for FE has persisted for decades. However, due to the complexity of the FE trait, these goals have still not been met. Here, we search for quantitative trait loci (QTL), candidate genes, and biological pathways associated with FE using both genotype and RNA-seq data. We obtained genotype and colon epithelium RNA-seq data for 375 and 96 pigs, respectively. In total, a genome-wide association study (GWAS) and differential expression (DE) analysis led to detection of three QTL on SSC9 and 17 DE-genes associated with FE. Possible intersection points between genes located in QTL and DE-genes were found on levels of transcription factor-target interaction. Moreover, cis-eQTL analysis revealed associations between genotype and expression levels of three DE-genes and three genes located in the GWAS QTLs, which may establish the connection between genotype and phenotype through DE. Finally, single nucleotide polymorphism calling using RNA-seq data for genes located in GWAS QTLs revealed 53 polymorphisms of which eleven were missense variants., (© 2022. The Author(s).)

Background: Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs., Results: The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10)., Conclusion: Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes., (© 2022. The Author(s).)

Selection for feed efficiency (FE) is a hot topic in dairy cow breeding. Dry matter intake (DMI) and residual energy intake (REI) are mostly discussed as new selection traits. Selection for lower DMI or REI seems to increase FE if other traits, such as milk yield or health, are not affected negatively. However, genetic relationships with other traits have not been adequately investigated because of the difficulties in recording sufficient feed intake data for genetic evaluations. The aim of this study was to examine the genetic relationships between FE-related traits and liability to diseases throughout lactation. First, heritabilities for all traits are presented. Subsequently, genetic correlations between DMI, energy-corrected milk yield, energy balance (EB), and REI on the one hand and 3 disease categories (mastitis, claw and leg diseases, and all diseases) on the other throughout lactation in German Holstein (GH) dairy cows are illustrated. Production and health data from the projects optiKuh and eMissionCow were used. Data consisted of weekly observations recorded over a 325-wk period in 2,387 GH and over a 300-wk period in 632 Fleckvieh (FV) primiparous and multiparous dairy cows from 13 dairy research farms in Germany. Variance and covariance components were estimated univariately or bivariately with linear random regression models for production data and threshold random regression models for health data. Heritabilities for DMI, EB, and REI were on average 0.17 and 0.15, 0.14 and 0.15, as well as 0.11 and 0.14 in GH and FV, respectively. Heritabilities on the underlying scale for mastitis, claw and leg diseases, and all diseases were on average 0.17 and 0.16, 0.18 and 0.12, as well as 0.15 and 0.11 in GH and FV, respectively. In GH, almost all genetic correlations were negative, especially in early lactation. Within the first 50 d in milk, genetic correlations between DMI and REI on the one hand and disease categories on the other ranged from -0.25 to -0.14 for mastitis, from -0.31 to -0.13 for claw and leg diseases, and from -0.58 to -0.30 for all diseases. Consequently, selection for lower DMI or REI could lead to a higher liability to diseases, especially in early lactation. A possibility to mitigate these undesirable side effects could be lactation stage-specific selection for FE. For FV, further studies with more data are needed to assess genetic relationships., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Pig breeding is changing rapidly due to technological progress and socio-ecological factors. New precision livestock farming technologies such as computer vision systems are crucial for automated phenotyping on a large scale for novel traits, as pigs' robustness and behavior are gaining importance in breeding goals. However, individual identification, data processing and the availability of adequate (open source) software currently pose the main hurdles. The overall goal of this study was to expand pig weighing with automated measurements of body dimensions and activity levels using an automated video-analytic system: DeepLabCut. Furthermore, these data were coupled with pedigree information to estimate genetic parameters for breeding programs. We analyzed 7428 recordings over the fattening period of 1556 finishing pigs (Piétrain sire x crossbred dam) with two-week intervals between recordings on the same pig. We were able to accurately estimate relevant body parts with an average tracking error of 3.3 cm. Body metrics extracted from video images were highly heritable (61-74%) and significantly genetically correlated with average daily gain (r g  = 0.81-0.92). Activity traits were low to moderately heritable (22-35%) and showed low genetic correlations with production traits and physical abnormalities. We demonstrated a simple and cost-efficient method to extract body dimension parameters and activity traits. These traits were estimated to be heritable, and hence, can be selected on. These findings are valuable for (pig) breeding organizations, as they offer a method to automatically phenotype new production and behavioral traits on an individual level., (© 2022. The Author(s).)

Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen-level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. The animals were video recorded for 24 h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with hot carcass weight, and for both betweenness and closeness centrality with test daily gain, final body weight, and hot carcass weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms.

Growth differentiation factor 15 (GDF15) is a mitochondrial stress-induced cytokine that modulates energy balance in an endocrine manner. However, the importance of its brainstem-restricted receptor GDNF family receptor alpha-like (GFRAL) to mediate endocrine GDF15 signaling to the brain upon mitochondrial dysfunction is still unknown. Using a mouse model with muscle-specific mitochondrial dysfunction, we here show that GFRAL is required for activation of systemic energy metabolism via daytime-restricted anorexia but not responsible for muscle wasting. We further find that muscle mitochondrial stress response involves a GFRAL-dependent induction of hypothalamic corticotropin-releasing hormone, without elevated corticosterone levels. Finally, we identify that GFRAL signaling governs an anxiety-like behavior in male mice with muscle mitochondrial dysfunction, with females showing a less robust GFRAL-dependent anxiety-like phenotype. Together, we here provide novel evidence of a mitochondrial stress-induced muscle-brain crosstalk via the GDF15-GFRAL axis to modulate food intake and anxiogenic behavior., (© 2022 Igual Gil et al.)

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Feed cost is the largest expense of mink production systems, and, therefore, improvement of feed efficiency (FE) through selection for high feed-efficient mink is a practical way to increase the mink industry's sustainability. In this study, we estimated the heritability, phenotypic, and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038 to 1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28 ± 0.06, 0.23 ± 0.06, 0.28 ± 0.10, 0.27 ± 0.11, 0.25 ± 0.09, 0.26 ± 0.09, 0.20 ± 0.09, 0.23 ± 0.09, 0.21 ± 0.10, 0.25 ± 0.10, and 0.26 ± 0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62 ± 0.24) and ADG (0.58 ± 0.21), and nonsignificant (P > 0.05) genetic correlations with FBW (0.14 ± 0.31) and FBL (-0.15 ± 0.31). These results revealed that RIG might be a superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88 ± 0.11 with FBW, 0.68 ± 0.17 with FBL, and 0.97 ± 0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg 0.75 for primiparous and 0.12 to 0.14 kg/kg 0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Feed occupies a significant proportion in the production cost of pigs, and the feed efficiency (FE) in pigs is of utmost economic importance. Hence, the objective of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes associated with FE-related traits, including feed conversion ratio (FCR) and residual feed intake (RFI). A genome-wide association study was conducted for FCR and RFI in 169 Yorkshire pigs using whole-genome sequencing data. A total of 23 and 33 suggestive significant SNPs ( P < 1 × 10 -6 ) were detected for FCR and RFI, respectively. However, none of SNPs achieved the genome-wide significance threshold ( P < 5 × 10 -8 ). Importantly, three common SNPs (SSC7:7987268, SSC13:42350250, and SSC13:42551718) were associated with both FCR and RFI. Additionally, the NEDD9 gene related to FCR and RFI traits was overlapped. This study detected novel SNPs on SSC7 and SSC13 common for FCR and RFI. These results provide new insights into the genetic mechanisms and candidate genes of FE-related traits in pigs.

Background: Feed efficiency (FE) related traits play a key role in the economy and sustainability of beef cattle production systems. The accurate knowledge of the physiologic background for FE-related traits can help the development of more efficient selection strategies for them. Hence, multi-trait weighted GWAS (MTwGWAS) and meta-analyze were used to find genomic regions associated with average daily gain (ADG), dry matter intake (DMI), feed conversion ratio (FCR), feed efficiency (FE), and residual feed intake (RFI). The FE-related traits and genomic information belong to two breeding programs that perform the FE test at different ages: post-weaning (1,024 animals IZ population) and post-yearling (918 animals for the QLT population)., Results: The meta-analyze MTwGWAS identified 14 genomic regions (-log10(p -value) > 5) regions mapped on BTA 1, 2, 3, 4, 7, 8, 11, 14, 15, 18, 21, and 29. These regions explained a large proportion of the total genetic variance for FE-related traits across-population ranging from 20% (FCR) to 36% (DMI) in the IZ population and from 22% (RFI) to 28% (ADG) in the QLT population. Relevant candidate genes within these regions (LIPE, LPL, IGF1R, IGF1, IGFBP5, IGF2, INS, INSR, LEPR, LEPROT, POMC, NPY, AGRP, TGFB1, GHSR, JAK1, LYN, MOS, PLAG1, CHCD7, LCAT, and PLA2G15) highlighted that the physiological mechanisms related to neuropeptides and the metabolic signals controlling the body's energy balance are responsible for leading to greater feed efficiency. Integrated meta-analysis results and functional pathway enrichment analysis highlighted the major effect of biological functions linked to energy, lipid metabolism, and hormone signaling that mediates the effects of peptide signals in the hypothalamus and whole-body energy homeostasis affecting the genetic control of FE-related traits in Nellore cattle., Conclusions: Genes and pathways associated with common signals for feed efficiency-related traits provide better knowledge about regions with biological relevance in physiological mechanisms associated with differences in energy metabolism and hypothalamus signaling. These pleiotropic regions would support the selection for feed efficiency-related traits, incorporating and pondering causal variations assigning prior weights in genomic selection approaches., (© 2022. The Author(s).)