Search

Heat stress affects pig metabolism, health and welfare, resulting in reduced growth and important economic losses. The present experiment aimed to evaluate the effects of two climatic environments [temperate (TEMP) vs. tropical humid (TROP)] on feeding behaviour in growing pigs. The feeding behaviour traits were measured with automated feeders and included: daily feed intake, daily eating time, feeding rate, daily number of meals, feed intake per meal, and feeding time per meal. Pigs came from a backcross population between Large White (LW, heat sensitive) and Creole (CR, heat tolerant) pigs. The same 10 F1 LW × CR boars (sire families [SF]) were mated with related LW sows in each environment. Feeding behaviour was recorded for a total of 1,296 pigs (n = 634 pigs for TEMP and n = 662 pigs for TROP) between 11 and 23 weeks of age. Growth performance and thermoregulatory responses (rectal and skin temperatures) were also measured. Results show that TROP conditions affect feeding behaviour traits: animals had more meals per day but these meals were smaller both in duration and in size, resulting in lower daily feed intake and less time eating per day. Significant SF by environment (GxE) interactions were found for all feeding behaviour traits. When SF were distributed into robust and sensitive groups (previously defined according to performance and thermoregulatory traits), results showed group by environment interactions for all feeding traits, except meal frequency. Moreover, a significant difference in feeding rate between robust and sensitive group was detected in TEMP, suggesting that feeding rate may be a good candidate to evaluate heat tolerance., (© 2022. The Author(s).)

Background: Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI)., Results: We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines., Conclusions: In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

Non-genetic information (epigenetic, microbiota, behaviour) that results in different phenotypes in animals can be transmitted from one generation to the next and thus is potentially involved in the inheritance of traits. However, in livestock species, animals are selected based on genetic inheritance only. The objective of the present study was to determine whether non-genetic inherited effects play a role in the inheritance of residual feed intake (RFI) in two species: pigs and rabbits. If so, the path coefficients of the information transmitted from sire and dam to offspring would differ from the expected transmission factor of 0.5 that occurs if inherited information is of genetic origin only. Two pigs (pig1, pig2) and two rabbits (rabbit1, rabbit2) datasets were used in this study (1,603, 3,901, 5,213 and 4,584 records, respectively). The test of the path coefficients to 0.5 was performed for each dataset using likelihood ratio tests (null model: transmissibility model with both path coefficients equal to 0.5, full model: unconstrained transmissibility model). The path coefficients differed significantly from 0.5 for one of the pig datasets (pig2). Although not significant, we observed, as a general trend, that sire path coefficients of transmission were lower than dam path coefficients in three of the datasets (0.46 vs 0.53 for pig1, 0.39 vs 0.44 for pig2 and 0.38 vs 0.50 for rabbit1). These results suggest that phenomena other than genetic sources of inheritance explain the phenotypic resemblance between relatives for RFI, with a higher transmission from the dam's side than from the sire's side., (© 2020 The Authors. Journal of Animal Breeding and Genetics published by Wiley-VCH GmbH.)

Background: Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits., Results: Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses., Conclusions: Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input-high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input-reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co-products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co-product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on 'protein from livestock animals' to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Heat stress affects pig health, welfare, and production, and thus the economic viability of the pig sector in many countries. Breeding for heat tolerance is a complex issue, increasingly important due to climate change and the development of pig production in tropical areas. Characterizing genetic determinism of heat tolerance would help building selection schemes dedicated to high performance in tropical areas. The main objective of our study was to estimate the genetic parameters for production and thermoregulation traits in two highly related growing pig populations reared in temperate (TEMP) or tropical humid (TROP) environment. Pigs came from a backcross population between Large White (LW, heat sensitive) and Creole (CR, heat tolerant) pigs. Phenotypic data were obtained on a total of 1,297 pigs using the same procedures in both environments, for body weight (BW, at weeks 11 and 23), daily feed intake (ADFI), backfat thickness (BFT, at weeks 19 and 23), cutaneous temperature (CT, at weeks 19 and 23), and rectal temperature (RT, at weeks 19, 21, and 23). Feed conversion ratio (FCR) and residual feed intake (RFI) were computed for the whole test period (11 to 23 wk). Criteria comparing the fits to the data revealed genotype × environment (G × E) interactions for most traits but not for FCR. The variance components were obtained using two different methods, a restricted maximum likelihood method and a Bayesian Markov chain Monte Carlo method, considering that traits are either similar or different in each environment. Regardless of the method, heritability estimates for production traits were moderate to high, except for FCR (lower than 0.18). Heritability estimates for RT were low to moderate, ranging from 0.04 to 0.34. The genetic correlations of each trait between environments generally differed from 1, except for FCR and ADG. For most thermoregulation traits, they also did not differ significantly from zero, suggesting that the main genetic bases of heat tolerance may vary in different environment. Within environments, the unfavorable genetic correlations between production traits and RT suggest an antagonism between the ability to maintain inner temperature and the ability to increase ADFI and ADG. However, greater RT were also associated to leaner pigs and better feed efficiency. Nevertheless, due to large inaccuracies of these estimations, larger cohorts would be needed to decide about the best breeding schemes to choose for tropical pig production., (© The Author(s) 2019. 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.)

The present study aimed at investigating the impact of heat challenges on gut microbiota composition in growing pigs and its relationship with pigs' performance and thermoregulation responses. From a total of 10 F1 sire families, 558 and 564 backcross Large White × Créole pigs were raised and phenotyped from 11 to 23 wk of age in temperate (TEMP) and in tropical (TROP) climates, respectively. In TEMP, all pigs were subjected to an acute heat challenge (3 wk at 29 °C) from 23 to 26 wk of age. Feces samples were collected at 23 wk of age both in TEMP and TROP climate (TEMP23 and TROP23 samples, respectively) and at 26 wk of age in TEMP climate (TEMP26 samples) for 16S rRNA analyses of fecal microbiota composition. The fecal microbiota composition significantly differed between the 3 environments. Using a generalized linear model on microbiota composition, 182 operational taxonomic units (OTU) and 2 pathways were differentially abundant between TEMP23 and TEMP26, and 1,296 OTU and 20 pathways between TEMP23 and TROP23. Using fecal samples collected at 23 wk of age, pigs raised under the 2 climates were discriminated with 36 OTU using a sparse partial least square discriminant analysis that had a mean classification error-rate of 1.7%. In contrast, pigs in TEMP before the acute heat challenge could be discriminated from the pigs in TEMP after the heat challenge with 32 OTU and 9.3% error rate. The microbiota can be used as biomarker of heat stress exposition. Microbiota composition revealed that pigs were separated into 2 enterotypes. The enterotypes were represented in both climates. Whatever the climate, animals belonging to the Turicibacter-Sarcina-Clostridium sensu stricto dominated enterotype were 3.3 kg heavier (P < 0.05) at 11 wk of age than those belonging to the Lactobacillus-dominated enterotype. This latter enterotype was related to a 0.3 °C lower skin temperature (P < 0.05) at 23 wk of age. Following the acute heat challenge in TEMP, this enterotype had a less-stable rectal temperature (0.34 vs. 0.25 °C variation between weeks 23 and 24, P < 0.05) without affecting growth performance (P > 0.05). Instability of the enterotypes was observed in 34% of the pigs, switching from an enterotype to another between 23 and 26 wk of age after heat stress. Despite a lower microbial diversity, the Turicibacter-Sarcina-Clostridium sensu stricto dominated enterotype was better adapted to heat stress conditions with lower thermoregulation variations., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Background: Improving feed efficiency (FE) is a major challenge in pig production. This complex trait is characterized by a high variability. Therefore, the identification of predictors of FE may be a relevant strategy to reduce phenotyping efforts in breeding and selection programs. The aim of this study was to investigate the suitability of expressed muscle genes in prediction of FE traits in growing pigs. The approach considered different transcriptomics experiments to cover a large range of FE values and identify reliable predictors., Results: Microarrays data were obtained from longissimus muscles of two lines divergently selected for residual feed intake (RFI). Pigs (n = 71) from three experiments belonged to generations 6 to 8 of selection, were fed either a diet with a standard composition or a diet rich in fiber and lipids, received feed ad libitum or at restricted level, and weighed between 80 and 115 kg at slaughter. For each pig, breeding value for RFI was estimated (RFI-BV), and feed conversion ratio (FCR) and energy-based feed conversion ratio (FCRe) were calculated during the test periods. Gradient boosting algorithms were used on the merged muscle transcriptomes to identify very important predictors of FE traits. About 20,405 annotated molecular probes were commonly expressed in longissimus muscle across experiments. Six to 267 expressed muscle genes covering a variety of biological processes were found as important predictors for RFI-BV (R 2  = 0.63-0.65), FCR (R 2  = 0.61-0.70) and FCRe (R 2  = 0.49-0.52). The error of prediction was less than 8% for FCR. Altogether, 56 predictors were common to RFI-BV and FCR. Expression levels of 24 target genes were further measured by qPCR. Linear regression confirmed the good accuracy of combining mRNA levels of these genes to fit FE traits (RFI-BV: R 2  = 0.73, FRC: R 2  = 0.76; FCRe: R 2  = 0.75). Stepwise regression procedure highlighted 10 genes (FKBP5, MUM1, AKAP12, FYN, TMED3, PHKB, TGF, SOCS6, ILR4, and FRAS1) in a linear combination predicting FCR and FCRe. In addition, FKBP5 and expression levels of five other genes (IGF2, SERINC3, CSRNP3, EZR and RPL16) significantly contributed to RFI-BV., Conclusion: It was possible to identify few genes expressed in muscle that might be reliable predictors of feed efficiency.

Weaning is a stress every piglet has to face. It is a main cause of antibiotic uses due to digestive disorders. In this study, response to weaning was analyzed in pigs from two lines divergently selected for residual feed intake (RFI) during growth. A total of 132 pigs from each line, housed per line and diet in conventional postweaning units of 12 castrated males and 12 females, were fed either a conventional control (two successive diets) or a complex (three successive diets) dietary sequence during the postweaning period (4 to 10 wk of age). BWs were recorded at weaning (days 0 and 28 of age), days 1, 2, 6, 12, 19, 26, and 42 (10 wk of age), and at 23 wk of age. Feces texture was examined before weaning (day -1), at day 1, 2, 6, 12, and 19. Feed intake was recorded at pen level from days 0 to 42 after weaning, and individually thereafter. Plasma was collected after blood samplings at days -1, 6, 19, and 42 on half of the piglets: all piglets of a given sex in each pen were sampled, to achieve a balanced number across factors. Pigs of the low RFI (LRFI) line were heavier at weaning, had greater glucose concentration, and lower levels of diarrhea at days 1 and 2 than pigs from the high RFI (HRFI) line (P < 0.01). At day 42, there was no BW difference between lines, and G:F ratio did not differ between lines (P = 0.40). The LRFI pigs had lower feed intake and growth rate from day 0 to day 19 (P < 0.005), and greater plasma concentration of non-esterified fatty acid (P < 0.001), indicating an increased mobilization of body lipids and proteins immediately after weaning compared with HRFI pigs. They also had greater levels of diarrhea at day 6 (22% for LRFI vs. 14% for HRFI, P = 0.002), but the concentration of plasma haptoglobin did not indicate acute inflammation. The complex diet sequence improved feed intake and growth, and reduced diarrhea, mainly in the LRFI line (P < 0.001). To conclude, pigs from the LRFI line were more negatively affected by weaning stress, but managed to recover afterwards. The complex diet sequence ameliorated some of the negative effects that weaning had on the LRFI pigs, but limited effects of nursery period feeding sequence on growth performance were observed during the growing-finishing period.

Dietary fiber content and composition affect microbial composition and activity in the gut, which in turn influence energetic contribution of fermentation products to the metabolic energy supply in pigs. This may affect feed efficiency (FE) in pigs. The present study investigated the relationship between the fecal microbial composition and FE in individual growing-finishing pigs. In addition, the effects of diet composition and sex on the fecal microbiome were studied. Fecal samples were collected of 154 grower-finisher pigs (3-way crossbreeds) the day before slaughter. Pigs were either fed a diet based on corn/soybean meal (CS) or a diet based on wheat/barley/by-products (WB). Fecal microbiome was characterized by 16S ribosomal DNA sequencing, clustered by operational taxonomic unit (OTU), and results were subjected to a discriminant approach combined with principal component analysis to discriminate diets, sexes, and FE extreme groups (10 high and 10 low FE pigs for each diet by sex-combination). Pigs on different diets and males vs. females had a very distinct fecal microbiome, needing only 2 OTU for diet (P = 0.020) and 18 OTU for sex (P = 0.040) to separate the groups. The 2 most important OTU for diet, and the most important OTU for sex, were taxonomically classified as the same bacterium. In pigs fed the CS diet, there was no significant association between FE and fecal microbiota composition based on OTU (P > 0.05), but in pigs fed the WB diet differences in FE were associated with 17 OTU in males (P = 0.018) and to 7 OTU in females (P = 0.010), with 3 OTU in common for both sexes. In conclusion, our results showed a diet and sex-dependent relationship between FE and the fecal microbial composition at slaughter weight in grower-finisher pigs.

The negative impact of heat stress (HS) on the production performances in pig faming is of particular concern. Novel diagnostic methods are needed to predict the robustness of pigs to HS. Our study aimed to assess the reliability of blood metabolome to predict the sensitivity to chronic HS of 10 F1 (Large White × Creole) sire families (SF) reared in temperate (TEMP) and in tropical (TROP) regions (n = 56±5 offsprings/region/SF). Live body weight (BW) and rectal temperature (RT) were recorded at 23 weeks of age. Average daily feed intake (AFDI) and average daily gain were calculated from weeks 11 to 23 of age, together with feed conversion ratio. Plasma blood metabolome profiles were obtained by Nuclear Magnetic Resonance spectroscopy (1HNMR) from blood samples collected at week 23 in TEMP. The sensitivity to hot climatic conditions of each SF was estimated by computing a composite index of sensitivity (Isens) derived from a linear combination of t statistics applied to familial BW, ADFI and RT in TEMP and TROP climates. A model of prediction of sensitivity was established with sparse Partial Least Square Discriminant Analysis (sPLS-DA) between the two most robust SF (n = 102) and the two most sensitive ones (n = 121) using individual metabolomic profiles measured in TEMP. The sPLS-DA selected 29 buckets that enabled 78% of prediction accuracy by cross-validation. On the basis of this training, we predicted the proportion of sensitive pigs within the 6 remaining families (n = 337). This proportion was defined as the predicted membership of families to the sensitive category. The positive correlation between this proportion and Isens (r = 0.97, P < 0.01) suggests that plasma metabolome can be used to predict the sensitivity of pigs to hot climate.

The objective of this study was to evaluate the performance and thermoregulatory responses during acclimation to high ambient temperature (Ta) of pigs from two lines selected for high (RFI(+)) or low (RFI(-)) residual feed intake with the hypothesis that RFI(-) pigs producing less heat would better tolerate high Ta. Pigs (50 kg initial body weight; 17 per line among which 10 of them were catheterized) were individually housed in a climatic-controlled room where Ta was maintained at 24.2 ± 0.4 °C during 7 days and thereafter at 30.4 ± 0.7 °C during 14 days. Irrespective of Ta, RFI(-) pigs had lower feed intake (ADFI) and similar average daily gain (ADG) than RFI(+) pigs. Whatever the line, ADFI, ADG, and feed efficiency decreased with increased Ta. Overall, the Ta increase resulted in an increase in rectal temperature (RT), skin temperature (ST), and respiratory rate (RR) within the first 24-48 h and, subsequently, in a decrease followed by stabilization. The RT decrease during acclimation occurred 24 h earlier in RFI(-) pigs than in RFI(+). Thyroid hormones and cortisol decreased at high Ta and it was similar in both lines. Based on performance and RT, ST, and RR responses, it seems that selection for low RFI tends to ameliorate pigs' tolerance to high Ta. Nevertheless, this selection does not induce significant differences between lines in endocrine and metabolite responses during thermal stress.

Background: Porcine chromosome X harbors four QTL strongly affecting backfat thickness (BFT), ham weight (HW), intramuscular fat content (IMF) and loin eye area (LEA). The confidence intervals (CI) of these QTL overlap and span more than 30 cM, or approximately 80 Mb. This study therefore attempts to fine map these QTL by joint analysis of two large-scale F₂ populations (Large White × Meishan and White Duroc × Erhualian constructed by INRA and JXAU respectively) and furthermore, to determine whether these QTL are caused by mutations in three positional candidate genes (ACSL4, SERPINA7 and IRS4) involved in lipid biosynthesis., Results: A female-specific linkage map with an average distance of 2 cM between markers in the initial QTL interval (SW2456-SW1943) was created and used here. The CI of QTL for BFT, HW and LEA were narrowed down to 6-7 cM, resulting from the joint analysis. For IMF, two linked QTL were revealed in the INRA population but not in the JXAU population, causing a wider CI (13 cM) for IMF QTL. Linkage analyses using two subsets of INRA F₁ dam families demonstrate that the BFT and HW QTL were segregating in the Meishan pigs. Moreover, haplotype comparisons between these dams suggest that within the refined QTL region, the recombination coldspot (~34 Mb) flanked by markers MCSE3F14 and UMNP1218 is unlikely to contain QTL genes. Two SNPs in the ACSL4 gene were identified and showed significant association with BFT and HW, but they and the known polymorphisms in the other two genes are unlikely to be causal mutations., Conclusion: The candidate QTL regions have been greatly reduced and the QTL are most likely located downstream of the recombination coldspot. The segregation of SSCX QTL for BFT and HW within Meishan breed provides an opportunity for us to make effective use of Meishan chromosome X in crossbreeding. Further studies should attempt to identify the impact of additional DNA sequence (e.g. CNV) and expression variation in the three genes or their surrounding genes on these traits.

Background: Many QTL have been detected in pigs, but very few of them have been fine-mapped up to the causal mutation. On SSC2, the IGF2-intron3-G3072A mutation has been described as the causative polymorphism for a QTL underlying muscle mass and backfat deposition, but further studies have demonstrated that at least one additional QTL should segregate downstream of this mutation. A marker-assisted backcrossing design was set up in order to confirm the segregation of this second locus, reduce its confidence interval and better understand its mode of segregation., Results: Five recombinant full-sibs, with genotype G/G at the IGF2 mutation, were progeny-tested. Only two of them displayed significant QTL for fatness traits although four inherited the same paternal and maternal chromosomes, thus exhibiting the same haplotypic contrast in the QTL region. The hypothesis of an interaction with another region in the genome was proposed to explain these discrepancies and after a genome scan, four different regions were retained as potential interacting regions with the SSC2 QTL. A candidate interacting region on SSC13 was confirmed by the analysis of an F2 pedigree, and in the backcross pedigree one haplotype in this region was found to mask the SSC2 QTL effect., Conclusions: Assuming the hypothesis of interactions with other chromosomal regions, the QTL could be unambiguously mapped to a 30 cM region delimited by recombination points. The marker-assisted backcrossing design was successfully used to confirm the segregation of a QTL on SSC2 and, because full-sibs that inherited the same alleles from their two parents were analysed, the detection of epistatic interactions could be performed between alleles and not between breeds as usually done with the traditional Line-Cross model. Additional analyses of other recombinant sires should provide more information to further improve the fine-mapping of this locus, and confirm or deny the interaction identified between chromosomes 2 and 13.

A genome-wide scan was performed in Large White and French Landrace pig populations in order to identify QTL affecting reproduction and production traits. The experiment was based on a granddaughter design, including five Large White and three French Landrace half-sib families identified in the French porcine national database. A total of 239 animals (166 sons and 73 daughters of the eight male founders) distributed in eight families were genotyped for 144 microsatellite markers. The design included 51 262 animals recorded for production traits, and 53 205 litter size records were considered. Three production and three reproduction traits were analysed: average backfat thickness (US&#95;M) and live weight (LWGT) at the end of the on-farm test, age of candidates adjusted at 100 kg live weight, total number of piglets born per litter, and numbers of stillborn (STILLp) and born alive (LIVp) piglets per litter. Ten QTL with medium to large effects were detected at a chromosome-wide significance level of 5% affecting traits US&#95;M (on SSC2, SSC3 and SSC17), LWGT (on SSC4), STILLp (on SSC6, SSC11 and SSC14) and LIVp (on SSC7, SSC16 and SSC18). The number of heterozygous male founders varied from 1 to 3 depending on the QTL.

A multivariate QTL detection was carried out on fatness and carcass composition traits on porcine chromosome 7 (SSC7). Single-trait QTLs have already been detected in the SLA region, and multivariate approaches have been used to exploit the correlations between the traits to obtain more information on their pattern: almost 500 measurements were recorded for backfat thickness (BFT1, BFT2), backfat weight (BFW) and leaf fat weight (LFW) but only about half that number for intramuscular fat content (IMF), affecting the detection. First, groups of traits were selected using a backward selection procedure: traits were selected based on their contribution to the linear combination of traits discriminating the putative QTL haplotypes. Three groups of traits could be distinguished based on successive discriminant analyses: external fat (BFT1, BFT2), internal fat (LFW, IMF) and BFW. At least four regions were distinguished, preferentially affecting one or the other group, with the SLA region always influencing all the traits. Meishan alleles decreased all trait values except IMF, confirming an opportunity for marker-assisted selection to improve meat quality with maintenance of carcass composition based on Meishan alleles.

Human cutaneous melanoma is a complex trait inherited in about 10% of cases. Although 2 high-risk genes, CDKN2A and CDK4, and 1 low risk gene, MC1R, have been identified, susceptibility genes remain to be discovered. Here, we attempted to determine new genomic regions linked to melanoma using the pig MeLiM strain, which develops hereditary cutaneous melanomas. We applied quantitative trait loci (QTL) mapping method to a significant genome-wide scan performed on 331 backcross pigs derived from this strain. QTLs were detected at chromosome-wide level for a melanoma synthetic trait corresponding to the development of melanoma. The peak positions on Sus scrofa chromosomes (SSC) were at 49.4 and 88.0 cM (SSC1), 56.0 cM (SSC13), 86.5 cM (SSC15) and 39.8 cM (SSC17), and, on SSC2, at 16.9 cM, in families derived from F1 males only (p < 0.05, except for SSC13, p < 0.01). Analysis of 7 precise specific traits revealed highly significant QTLs on SSC10 (ulceration), on SSC12 (presence of melanoma at birth), on SSC13 (lesion type), and on SSC16 and SSC17 (number of aggressive melanomas) at the respective positions 42.0, 95.6, 81.0, 45.3 and 44.8 cM (p < 0.001 and p < 0.05 respectively at the chromosome- and genome-wide levels). We also showed that MeLiM MC1R*2 allele, which determines black coat colour in pigs, predisposes significantly to melanoma. Interactions were observed between MC1R and markers located on SSC1 (p < 0.05). Taken together, these results indicate that MeLiM swine is a model for human multigenic diseases. Comparative mapping revealed human regions of interest to search for new melanoma susceptibility candidates., ((c) 2006 Wiley-Liss, Inc.)

Feed efficiency is a trait of interest in pigs as it contributes to lowering the ecological and economical costs of pig production. A divergent genetic selection experiment from a Large White pig population was performed for 10 generations, leading to pig lines with relatively low-(LRFI) and high- (HRFI) residual feed intake (RFI). Feeding behavior and metabolic differences have been previously reported between the two lines. We hypothesized that part of these differences could be related to differential sensing and absorption of nutrients in the proximal intestine. We investigated the duodenum transcriptome and DNA methylation profiles comparing overnight fasting with ad libitum feeding in LRFI and HRFI pigs (n = 24). We identified 1,106 differentially expressed genes between the two lines, notably affecting pathways of the transmembrane transport activity and related to mitosis or chromosome separation. The LRFI line showed a greater transcriptomic response to feed intake than the HRFI line. Feed intake affected genes from both anabolic and catabolic pathways in the pig duodenum, such as rRNA production and autophagy. Several nutrient transporter and tight junction genes were differentially expressed between lines and/or by short-term feed intake. We also identified 409 differentially methylated regions in the duodenum mucosa between the two lines, while this epigenetic mark was less affected by feeding. Our findings highlighted that the genetic selection for feed efficiency in pigs changed the transcriptome profiles of the duodenum, and notably its response to feed intake, suggesting key roles for this proximal gut segment in mechanisms underlying feed efficiency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Breeding efficient pigs is a way to reduce dietary costs and environmental waste. However, optimization of feed efficiency must not be linked to a decrease of the ability of animals to cope with stress, such as the weaning. This study characterizes the response after weaning of pigs from two lines divergently selected for residual feed intake (RFI) during growth. Animals of the low (L) RFI line are more efficient than animals from the high (H) RFI line. Thirty‐six piglets from each line weaned at 28 days of age were individually housed and fed a conventional dietary sequence. Their performance, behaviour, health and oxidative status, immune and nutritional parameters were followed during three weeks. Daily feed intake and growth rate of pigs from the LRFI line were 35% and 40% lower compared with HRFI (p < 0.001). Pigs from the LRFI‐line had lower total tract apparent digestibility (−6% for OM) and suffered more from undernutrition with a 167 and 55% higher plasmatic concentration of NEFA and urea compared with HRFI (p < 0.01). In the first week after the weaning, they had more diarrhoea and had a higher inflammatory status with concentration of haptoglobin 52% higher (p < 0.001). These piglets then seemed to adapt to the weaning conditions and to recover during the second and third weeks. Both lines had similar zootechnical performance and physiological characteristics at the end of the post‐weaning period. To conclude, the physiological responses to the weaning differed between lines. Pigs from the LRFI line, selected for greater feed efficiency, were more sensitive to the weaning stress. They were also more resilient as they finally adapted to the new condition and recovered to show similar performance results as pigs of the HRFI line. [ABSTRACT FROM AUTHOR]

Background: An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. Results: Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. Conclusions: For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits. [ABSTRACT FROM AUTHOR]

The use of diets with increased dietary fibre content (HF) from alternative feedstuffs is a solution to limit the impact of increased feed costs on pig production. This study aimed at determining the impact of an alternative HF diet on pig digestibility and at estimating genetic parameters of this trait. Digestibility coefficients (DC) of energy, organic matter and nitrogen were predicted from faecal samples analysed with near infrared spectrometry for 1,242 samples, and it represented 654 Large White pigs fed a conventional (CO) diet and 588 fed a HF diet. Growth and feed efficiency traits, carcass composition and meat quality traits were recorded. Pigs fed the HF diet had significantly lower DC than pigs fed the CO diet (−4.5 to 6.0 points). The DC were moderately to highly heritable (about 0.26 ± 0.12 and 0.54 ± 0.15 in the CO and the HF diet, respectively). Genetic correlations were favourable with feed conversion ratio, daily feed intake and residual feed intake, but unfavourable with average daily gain (ADG) and carcass yield (CY). To conclude, DC could be an interesting trait to include in future breeding objectives if pigs were fed diet with HF diets, but adverse genetic trends with ADG and CY would have to be taken into account. [ABSTRACT FROM AUTHOR]

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allows identifying relevant markers and designing low-density SNP chips to evaluate selection candidates. In this research, several univariate and multivariate FS algorithms combined with various parametric and non-parametric learners were applied to the prediction of feed efficiency in growing pigs from high-dimensional genomic data. The objective was to find the best combination of feature selector, SNP subset size, and learner leading to accurate and stable (i.e., less sensitive to changes in the training data) prediction models. Genomic best linear unbiased prediction (GBLUP) without SNP pre-selection was the benchmark. Three types of FS methods were implemented: (i) filter methods: univariate (univ.dtree, spearcor) or multivariate (cforest, mrmr), with random selection as benchmark; (ii) embedded methods: elastic net and least absolute shrinkage and selection operator (LASSO) regression; (iii) combination of filter and embedded methods. Ridge regression, support vector machine (SVM), and gradient boosting (GB) were applied after pre-selection performed with the filter methods. Data represented 5,708 individual records of residual feed intake to be predicted from the animal's own genotype. Accuracy (stability of results) was measured as the median (interquartile range) of the Spearman correlation between observed and predicted data in a 10-fold cross-validation. The best prediction in terms of accuracy and stability was obtained with SVM and GB using 500 or more SNPs [0.28 (0.02) and 0.27 (0.04) for SVM and GB with 1,000 SNPs, respectively]. With larger subset sizes (1,000–1,500 SNPs), the filter method had no influence on prediction quality, which was similar to that attained with a random selection. With 50–250 SNPs, the FS method had a huge impact on prediction quality: it was very poor for tree-based methods combined with any learner, but good and similar to what was obtained with larger SNP subsets when spearcor or mrmr were implemented with or without embedded methods. Those filters also led to very stable results, suggesting their potential use for designing low-density SNP chips for genome-based evaluation of feed efficiency. [ABSTRACT FROM AUTHOR]

The present study aimed at investigating the impact of heat challenges on gut microbiota composition in growing pigs and its relationship with pigs' performance and thermoregulation responses. From a total of 10 F1 sire families, 558 and 564 backcross Large White × Créole pigs were raised and phenotyped from 11 to 23 wk of age in temperate (TEMP) and in tropical (TROP) climates, respectively. In TEMP, all pigs were subjected to an acute heat challenge (3 wk at 29 °C) from 23 to 26 wk of age. Feces samples were collected at 23 wk of age both in TEMP and TROP climate (TEMP23 and TROP23 samples, respectively) and at 26 wk of age in TEMP climate (TEMP26 samples) for 16S rRNA analyses of fecal microbiota composition. The fecal microbiota composition significantly differed between the 3 environments. Using a generalized linear model on microbiota composition, 182 operational taxonomic units (OTU) and 2 pathways were differentially abundant between TEMP23 and TEMP26, and 1,296 OTU and 20 pathways between TEMP23 and TROP23. Using fecal samples collected at 23 wk of age, pigs raised under the 2 climates were discriminated with 36 OTU using a sparse partial least square discriminant analysis that had a mean classification error-rate of 1.7%. In contrast, pigs in TEMP before the acute heat challenge could be discriminated from the pigs in TEMP after the heat challenge with 32 OTU and 9.3% error rate. The microbiota can be used as biomarker of heat stress exposition. Microbiota composition revealed that pigs were separated into 2 enterotypes. The enterotypes were represented in both climates. Whatever the climate, animals belonging to the Turicibacter – Sarcina – Clostridium sensu stricto dominated enterotype were 3.3 kg heavier (P < 0.05) at 11 wk of age than those belonging to the Lactobacillus -dominated enterotype. This latter enterotype was related to a 0.3 °C lower skin temperature (P < 0.05) at 23 wk of age. Following the acute heat challenge in TEMP, this enterotype had a less-stable rectal temperature (0.34 vs. 0.25 °C variation between weeks 23 and 24, P < 0.05) without affecting growth performance (P > 0.05). Instability of the enterotypes was observed in 34% of the pigs, switching from an enterotype to another between 23 and 26 wk of age after heat stress. Despite a lower microbial diversity, the Turicibacter – Sarcina – Clostridium sensu stricto dominated enterotype was better adapted to heat stress conditions with lower thermoregulation variations. [ABSTRACT FROM AUTHOR]

The negative impact of heat stress (HS) on the production performances in pig faming is of particular concern. Novel diagnostic methods are needed to predict the robustness of pigs to HS. Our study aimed to assess the reliability of blood metabolome to predict the sensitivity to chronic HS of 10 F1 (Large White × Creole) sire families (SF) reared in temperate (TEMP) and in tropical (TROP) regions (n = 56±5 offsprings/region/SF). Live body weight (BW) and rectal temperature (RT) were recorded at 23 weeks of age. Average daily feed intake (AFDI) and average daily gain were calculated from weeks 11 to 23 of age, together with feed conversion ratio. Plasma blood metabolome profiles were obtained by Nuclear Magnetic Resonance spectroscopy (1HNMR) from blood samples collected at week 23 in TEMP. The sensitivity to hot climatic conditions of each SF was estimated by computing a composite index of sensitivity (Isens) derived from a linear combination of t statistics applied to familial BW, ADFI and RT in TEMP and TROP climates. A model of prediction of sensitivity was established with sparse Partial Least Square Discriminant Analysis (sPLS-DA) between the two most robust SF (n = 102) and the two most sensitive ones (n = 121) using individual metabolomic profiles measured in TEMP. The sPLS-DA selected 29 buckets that enabled 78% of prediction accuracy by cross-validation. On the basis of this training, we predicted the proportion of sensitive pigs within the 6 remaining families (n = 337). This proportion was defined as the predicted membership of families to the sensitive category. The positive correlation between this proportion and Isens (r = 0.97, P < 0.01) suggests that plasma metabolome can be used to predict the sensitivity of pigs to hot climate. [ABSTRACT FROM AUTHOR]

Background: Numerous quantitative trait loci (QTL) have been detected in pigs over the past 20 years using microsatellite markers. However, due to the low density of these markers, the accuracy of QTL location has generally been poor. Since 2009, the dense genome coverage provided by the Illumina PorcineSNP60 BeadChip has made it possible to more accurately map QTL using genome-wide association studies (GWAS). Our objective was to perform high-density GWAS in order to identify genomic regions and corresponding haplotypes associated with production traits in a French Large White population of pigs. Methods: Animals (385 Large White pigs from 106 sires) were genotyped using the PorcineSNP60 BeadChip and evaluated for 19 traits related to feed intake, growth, carcass composition and meat quality. Of the 64 432 SNPs on the chip, 44 412 were used for GWAS with an animal mixed model that included a regression coefficient for the tested SNPs and a genomic kinship matrix. SNP haplotype effects in QTL regions were then tested for association with phenotypes following phase reconstruction based on the Sscrofa10.2 pig genome assembly. Results: Twenty-three QTL regions were identified on autosomes and their effects ranged from 0.25 to 0.75 phenotypic standard deviation units for feed intake and feed efficiency (four QTL), carcass (12 QTL) and meat quality traits (seven QTL). The 10 most significant QTL regions had effects on carcass (chromosomes 7, 10, 16, 17 and 18) and meat quality traits (two regions on chromosome 1 and one region on chromosomes 8, 9 and 13). Thirteen of the 23 QTL regions had not been previously described. A haplotype block of 183 kb on chromosome 1 (six SNPs) was identified and displayed three distinct haplotypes with significant (0.0001 < P < 0.03) associations with all evaluated meat quality traits. Conclusions: GWAS analyses with the PorcineSNP60 BeadChip enabled the detection of 23 QTL regions that affect feed consumption, carcass and meat quality traits in a LW population, of which 13 were novel QTL. The proportionally larger number of QTL found for meat quality traits suggests a specific opportunity for improving these traits in the pig by genomic selection. [ABSTRACT FROM AUTHOR]

The present study aimed at investigating the impact of heat challenges on gut microbiota composition in growing pigs and its relationship with pigs' performance and thermoregulation responses. From a total of 10 F1 sire families, 558 and 564 backcross Large White × Créole pigs were raised and phenotyped from 11 to 23 wk of age in temperate (TEMP) and in tropical (TROP) climates, respectively. In TEMP, all pigs were subjected to an acute heat challenge (3 wk at 29 °C) from 23 to 26 wk of age. Feces samples were collected at 23 wk of age both in TEMP and TROP climate (TEMP23 and TROP23 samples, respectively) and at 26 wk of age in TEMP climate (TEMP26 samples) for 16S rRNA analyses of fecal microbiota composition. The fecal microbiota composition significantly differed between the 3 environments. Using a generalized linear model on microbiota composition, 182 operational taxonomic units (OTU) and 2 pathways were differentially abundant between TEMP23 and TEMP26, and 1,296 OTU and 20 pathways between TEMP23 and TROP23. Using fecal samples collected at 23 wk of age, pigs raised under the 2 climates were discriminated with 36 OTU using a sparse partial least square discriminant analysis that had a mean classification error-rate of 1.7%. In contrast, pigs in TEMP before the acute heat challenge could be discriminated from the pigs in TEMP after the heat challenge with 32 OTU and 9.3% error rate. The microbiota can be used as biomarker of heat stress exposition. Microbiota composition revealed that pigs were separated into 2 enterotypes. The enterotypes were represented in both climates. Whatever the climate, animals belonging to the Turicibacter – Sarcina – Clostridium sensu stricto dominated enterotype were 3.3 kg heavier (P < 0.05) at 11 wk of age than those belonging to the Lactobacillus -dominated enterotype. This latter enterotype was related to a 0.3 °C lower skin temperature (P < 0.05) at 23 wk of age. Following the acute heat challenge in TEMP, this enterotype had a less-stable rectal temperature (0.34 vs. 0.25 °C variation between weeks 23 and 24, P < 0.05) without affecting growth performance (P > 0.05). Instability of the enterotypes was observed in 34% of the pigs, switching from an enterotype to another between 23 and 26 wk of age after heat stress. Despite a lower microbial diversity, the Turicibacter – Sarcina – Clostridium sensu stricto dominated enterotype was better adapted to heat stress conditions with lower thermoregulation variations. [ABSTRACT FROM AUTHOR]