A first characterization of the microbiota-resilience link in swine.

BACKGROUND: The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes. RESULTS: This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience. CONCLUSION: Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet.

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 near­infrared 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.

Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle.

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD(where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IAm and IAi were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle.

This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.

Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression models.

BACKGROUND: Automatic and continuous recording of vaginal temperature (TV) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of TV in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for TV in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded TV. RESULTS: Heritability estimates for TV ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, - 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ËšC). Repeatability estimates of TV over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. TV measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording TV for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370-59.998 Mb) and16 (21.548-21.966 Mb) were identified. These regions harbor the genes CDC123, CAMK1d, SEC61A2, and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for TV. For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for TV, respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production. CONCLUSIONS: TV is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for TV exist, indicating potential re-ranking of selection candidates across EG. TV is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms.

Domestication shapes the pig gut microbiome and immune traits from the scale of lineage to population.

Animal ecology and evolution have long been known to shape host physiology, but more recently, the gut microbiome has been identified as a mediator between animal ecology and evolution and health. The gut microbiome has been shown to differ between wild and domestic animals, but the role of these differences for domestic animal evolution remains unknown. Gut microbiome responses to new animal genotypes and local environmental change during domestication may promote specific host phenotypes that are adaptive (or not) to the domestic environment. Because the gut microbiome supports host immune function, understanding the effects of animal ecology and evolution on the gut microbiome and immune phenotypes is critical. We investigated how domestication affects the gut microbiome and host immune state in multiple pig populations across five domestication contexts representing domestication status and current living conditions: free-ranging wild, captive wild, free-ranging domestic, captive domestic in research or industrial settings. We observed that domestication context explained much of the variation in gut microbiome composition, pathogen abundances and immune markers, yet the main differences in the repertoire of metabolic genes found in the gut microbiome were between the wild and domestic genetic lineages. We also documented population-level effects within domestication contexts, demonstrating that fine scale environmental variation also shaped host and microbe features. Our findings highlight that understanding which gut microbiome and immune traits respond to host genetic lineage and/or scales of local ecology could inform targeted interventions that manipulate the gut microbiome to achieve beneficial health outcomes.

Genetic parameters for automatically-measured vaginal temperature, respiration efficiency, and other thermotolerance indicators measured on lactating sows under heat stress conditions.

BACKGROUND: Genetic selection based on direct indicators of heat stress could capture additional mechanisms that are involved in heat stress response and enable more accurate selection for more heat-tolerant individuals. Therefore, the main objectives of this study were to estimate genetic parameters for various heat stress indicators in a commercial population of Landrace × Large White lactating sows measured under heat stress conditions. The main indicators evaluated were: skin surface temperatures (SST), automatically-recorded vaginal temperature (TV), respiration rate (RR), panting score (PS), body condition score (BCS), hair density (HD), body size (BS), ear size, and respiration efficiency (Reff). RESULTS: Traits based on TV presented moderate heritability estimates, ranging from 0.15 ± 0.02 to 0.29 ± 0.05. Low heritability estimates were found for SST traits (from 0.04 ± 0.01 to 0.06 ± 0.01), RR (0.06 ± 0.01), PS (0.05 0.01), and Reff (0.03 ± 0.01). Moderate to high heritability values were estimated for BCS (0.29 ± 0.04 for caliper measurements and 0.25 ± 0.04 for visual assessments), HD (0.25 ± 0.05), BS (0.33 ± 0.05), ear area (EA; 0.40 ± 0.09), and ear length (EL; 0.32 ± 0.07). High genetic correlations were estimated among SST traits (> 0.78) and among TV traits (> 0.75). Similarly, high genetic correlations were also estimated for RR with PS (0.87 ± 0.02), with BCS measures (0.92 ± 0.04), and with ear measures (0.95 ± 0.03). Low to moderate positive genetic correlations were estimated between SST and TV (from 0.25 ± 0.04 to 0.76 ± 0.07). Low genetic correlations were estimated between TV and BCS (from - 0.01 ± 0.08 to 0.06 ± 0.07). Respiration efficiency was estimated to be positively and moderately correlated with RR (0.36 ± 0.04), PS (0.56 ± 0.03), and BCS (0.56 ± 0.05 for caliper measurements and 0.50 ± 0.05 for the visual assessments). All other trait combinations were lowly genetically correlated. CONCLUSIONS: A comprehensive landscape of heritabilities and genetic correlations for various thermotolerance indicators in lactating sows were estimated. All traits evaluated are under genetic control and heritable, with different magnitudes, indicating that genetic progress is possible for all of them. The genetic correlation estimates provide evidence for the complex relationships between these traits and confirm the importance of a sub-index of thermotolerance traits to improve heat tolerance in pigs.

Dietary Prebiotic Oligosaccharides and Arachidonate Alter the Fecal Microbiota and Mucosal Lipid Composition of Suckling Pigs.

BACKGROUND: Early intestinal development is important to infant vitality, and optimal formula composition can promote gut health. OBJECTIVES: The objectives were to evaluate the effects of arachidonate (ARA) and/or prebiotic oligosaccharide (PRE) supplementation in formula on the development of the microbial ecosystem and colonic health parameters. METHODS: Newborn piglets were fed 4 formulas containing ARA [0.5 compared with 2.5% of dietary fatty acids (FAs)] and PRE (0 compared with 8 g/L, containing a 1:1 mixture of galactooligosaccharides and polydextrose) in a 2 x 2 factorial design for 22 d. Fecal samples were collected weekly and analyzed for relative microbial abundance. Intestinal samples were collected on day 22 and analyzed for mucosal FAs, pH, and short-chain FAs (SCFAs). RESULTS: PRE supplementation significantly increased genera within Bacteroidetes and Firmicutes, including Anaerostipes, Mitsuokella, Prevotella, Clostridium IV, and Bulleidia, and resulted in progressive separation from controls as determined by Principal Coordinates Analysis. Concentrations of SCFA increased from 70.98 to 87.37 mM, with an accompanying reduction in colonic pH. ARA supplementation increased the ARA content of the colonic mucosa from 2.35-5.34% of total FAs. PRE supplementation also altered mucosal FA composition, resulting in increased linoleic acid (11.52-16.33% of total FAs) and ARA (2.35-5.16% of total FAs). CONCLUSIONS: Prebiotic supplementation during the first 22 d of life altered the gut microbiota of piglets and increased the abundance of specific bacterial genera. These changes correlated with increased SCFA, which may benefit intestinal development. Although dietary ARA did not alter the microbiota, it increased the ARA content of the colonic mucosa, which may support intestinal development and epithelial repair. Prebiotic supplementation also increased unsaturation of FAs in the colonic mucosa. Although the mechanism requires further investigation, it may be related to altered microbial ecology or biohydrogenation of FA.

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

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

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

MicroRNA and mRNA sequencing analyses reveal key hepatic metabolic and signaling pathways responsive to maternal undernutrition in full-term fetal pigs.

Maternal undernutrition is highly prevalent in developing countries, leading to severe fetus/infant mortality, intrauterine growth restriction, stunting, and severe wasting. However, the potential impairments of maternal undernutrition to metabolic pathways in offspring are not defined completely. In this study, 2 groups of pregnant domestic pigs received nutritionally balanced gestation diets with or without 50% feed intake restriction from 0 to 35 gestation days and 70% from 35 to 114 gestation days. Full-term fetuses were collected via C-section on day 113/114 of gestation. MicroRNA and mRNA deep sequencing were analyzed using the Illumina GAIIx system on fetal liver samples. The mRNA-miRNA correlation and associated signaling pathways were analyzed via CLC Genomics Workbench and Ingenuity Pathway Analysis Software. A total of 1189 and 34 differentially expressed mRNA and miRNAs were identified between full-nutrition (F) and restricted-nutrition (R) groups. The correlation analyses showed that metabolic and signaling pathways such as oxidative phosphorylation, death receptor signaling, neuroinflammation signaling pathway, and estrogen receptor signaling pathways were significantly modified, and the gene modifications in these pathways were associated with the miRNA changes induced by the maternal undernutrition. For example, the upregulated (P<.05) oxidative phosphorylation pathway in R group was validated using RT-qPCR, and the correlational analysis indicated that miR-221, 103, 107, 184, and 4497 correlate with their target genes NDUFA1, NDUFA11, NDUFB10 and NDUFS7 in this pathway. These results provide the framework for further understanding maternal malnutrition's negative impacts on hepatic metabolic pathways via miRNA-mRNA interactions in full-term fetal pigs.

SLEMM: million-scale genomic predictions with window-based SNP weighting.

MOTIVATION: The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging. RESULTS: We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge. SLEMM builds on an efficient implementation of the stochastic Lanczos algorithm for REML in a framework of mixed models. We further implement SNP weighting in SLEMM to improve its predictions. Extensive analyses on seven public datasets, covering 19 polygenic traits in three plant and three livestock species, showed that SLEMM with SNP weighting had overall the best predictive ability among a variety of genomic prediction methods including GCTA's empirical BLUP, BayesR, KAML, and LDAK's BOLT and BayesR models. We also compared the methods using nine dairy traits of ∼300k genotyped cows. All had overall similar prediction accuracies, except that KAML failed to process the data. Additional simulation analyses on up to 3 million individuals and 1 million SNPs showed that SLEMM was advantageous over counterparts as for computational performance. Overall, SLEMM can do million-scale genomic predictions with an accuracy comparable to BayesR. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/jiang18/slemm.

Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle.

Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Runs of homozygosity (ROH) of 2 Mb or longer in length were identified in each animal. The within-breed means for number and the combined length of ROH were highest in Jerseys (62.66 ± 8.29 ROH and 426.24 ± 83.40 Mb, respectively; mean ± SD) and lowest in Ayrshires (37.24 ± 8.27 ROH and 265.05 ± 85.00 Mb, respectively). Short ROH were the most abundant, but moderate to large ROH made up the largest proportion of genome autozygosity in all breeds. In addition, we identified ROH islands in each breed. This revealed selection patterns for milk production, productive life, health, and reproduction in most breeds and evidence for parallel selective pressure for loci on chromosome 6 between Ayrshire and Brown Swiss and for loci on chromosome 20 between Holstein and Jersey. We calculated inbreeding coefficients using 3 different approaches, pedigree-based (FPED), marker-based using a genomic relationship matrix (FGRM), and segment-based using ROH (FROH). The average inbreeding coefficient ranged from 0.06 in Ayrshires and Brown Swiss to 0.08 in Jerseys and Holsteins using FPED, from 0.22 in Holsteins to 0.29 in Guernsey and Jerseys using FGRM, and from 0.11 in Ayrshires to 0.17 in Jerseys using FROH. In addition, the effective population size at past generations (5-100 generations ago), the yearly rate of inbreeding, and the effective population size in 3 recent periods (2000-2009, 2010-2014, and 2015-2018) were determined in each breed to ascertain current and historical trends of genetic diversity. We found a historical trend of decreasing effective population size in the last 100 generations in all breeds and breed differences in the effect of the recent implementation of genomic selection on inbreeding accumulation.

Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet.

BACKGROUND: Breeding pigs that can efficiently digest alternative diets with increased fiber content is a viable strategy to mitigate the feed cost in pig production. This study aimed at determining the contribution of the gut microbiota and host genetics to the phenotypic variability of digestive efficiency (DE) traits, such as digestibility coefficients of energy, organic matter and nitrogen, feed efficiency (FE) traits (feed conversion ratio and residual feed intake) and growth traits (average daily gain and daily feed intake). Data were available for 791 pigs fed a conventional diet and 735 of their full-sibs fed a high-fiber diet. Fecal samples were collected at 16 weeks of age to sequence the V3-V4 regions of the 16S ribosomal RNA gene and predict DE with near-infrared spectrometry. The proportions of phenotypic variance explained by the microbiota (microbiability) were estimated under three OTU filtering scenarios. Then, microbiability and heritability were estimated independently (models Micro and Gen) and jointly (model Micro+Gen) using a Bayesian approach for all traits. Breeding values were estimated in models Gen and Micro+Gen. RESULTS: Differences in microbiability estimates were significant between the two extreme filtering scenarios (14,366 and 803 OTU) within diets, but only for all DE. With the intermediate filtering scenario (2399 OTU) and for DE, microbiability was higher (> 0.44) than heritability (< 0.32) under both diets. For two of the DE traits, microbiability was significantly higher under the high-fiber diet (0.67 ± 0.06 and 0.68 ± 0.06) than under the conventional diet (0.44 ± 0.06). For growth and FE, heritability was higher (from 0.26 ± 0.06 to 0.44 ± 0.07) than microbiability (from 0.17 ± 0.05 to 0.35 ± 0.06). Microbiability and heritability estimates obtained with the Micro+Gen model did not significantly differ from those with the Micro and Gen models for all traits. Finally, based on their estimated breeding values, pigs ranked differently between the Gen and Micro+Gen models, only for the DE traits under both diets. CONCLUSIONS: The microbiota explained a significant proportion of the phenotypic variance of the DE traits, which was even larger than that explained by the host genetics. Thus, the use of microbiota information could improve the selection of DE traits, and to a lesser extent, of growth and FE traits. In addition, our results show that, at least for DE traits, filtering OTU is an important step and influences the microbiability.

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine.

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.

Gut microbiota has received significant research attention in farm animals because of its close relationship with host performance. We chose eight approaches to create a square covariance matrix that characterizes the relationship among animals based on their gut microbiota composition. Then, we fitted this information with linear models to evaluate the proportion of phenotypic variance explained by gut microbiota composition and predict host growth and body composition traits in three pig breeds. We found that different matrices had varying performance in predicting host phenotypes, but the results highly depended on the trait and breed considered in the prediction. Our findings highlight possible alternative approaches to incorporate gut microbiome data in regression models and emphasize the value of gut microbiome data in better understanding complex traits in pigs with diverse genetic backgrounds.

Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine.

BACKGROUND: Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement. METHODS: Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive-to-record traits measured on crossbred individuals. Estimation of multi-trait variance components set the basis for comparing the different scenarios, together with a fourfold cross-validation approach to validate the phenotyping schemes under four genotyping strategies. RESULTS: The benefit of including genomic information for multi-trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi-trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness). CONCLUSIONS: Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi-trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are phenotyped for the breeding objective.

Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine.

The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine.

A network modeling approach provides insights into the environment-specific yield architecture of wheat.

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how quantitative trait loci generate yield variation. The objectives of this experiment were to identify quantitative trait loci affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 recombinant inbred line population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in 2 replications at 5 locations over the 2018 and 2019 seasons. The parents were 2 soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model to characterize the relationships between quantitative trait loci, yield components, and overall spike yield. The effects of major quantitative trait loci on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified quantitative trait loci was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual quantitative trait loci varied dramatically. Path analysis based on coefficients estimated through structural equation model demonstrated that these variations in effects resulted from both different effects of quantitative trait loci on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.

Exploring the role of gut microbiota in host feeding behavior among breeds in swine.

BACKGROUND: The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing. RESULTS: Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia. CONCLUSIONS: This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies.

Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity.

In the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds, six breeds with distribution limited to Tuscany (Calvana, Mucca Pisana, Pontremolese) or Sardinia (Sarda, Sardo Bruna and Sardo Modicana) and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (~ 15.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component and multidimensional scaling analyses, and resulted in a clear separation among the breeds, with clusters related to productive purposes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g. MYOG, CHI3L1, CHIT1 (BTA16), TIMELESS, APOF, OR10P1, OR6C4, OR2AP1, OR6C2, OR6C68, CACNG2 (BTA5), COL5A2 and COL3A1 (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago, with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.

Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs.

BACKGROUND: The role of the microbiome in livestock production has been highlighted in recent research. Currently, little is known about the microbiome's impact across different systems of production in swine, particularly between selection nucleus and commercial populations. In this paper, we investigated fecal microbial composition in nucleus versus commercial systems at different time points. RESULTS: We identified microbial OTUs associated with growth and carcass composition in each of the two populations, as well as the subset common to both. The two systems were represented by individuals with sizeable microbial diversity at weaning. At later times microbial composition varied between commercial and nucleus, with species of the genus Lactobacillus more prominent in the nucleus population. In the commercial populations, OTUs of the genera Lactobacillus and Peptococcus were associated with an increase in both growth rate and fatness. In the nucleus population, members of the genus Succinivibrio were negatively correlated with all growth and carcass traits, while OTUs of the genus Roseburia had a positive association with growth parameters. Lactobacillus and Peptococcus OTUs showed consistent effects for fat deposition and daily gain in both nucleus and commercial populations. Similarly, OTUs of the Blautia genus were positively associated with daily gain and fat deposition. In contrast, an increase in the abundance of the Bacteroides genus was negatively associated with growth performance parameters. CONCLUSIONS: The current study provides a first characterization of microbial communities' value throughout the pork production systems. It also provides information for incorporating microbial composition into the selection process in the quest for affordable and sustainable protein production in swine.