Microbiome composition as a potential predictor of longevity in rabbits.

BACKGROUND: Longevity and resilience are two fundamental traits for more sustainable livestock production. These traits are closely related, as resilient animals tend to have longer lifespans. An interesting criterion for increasing longevity in rabbit could be based on the information provided by its gut microbiome. The gut microbiome is essential for regulating health and plays crucial roles in the development of the immune system. The aim of this research was to investigate if animals with different longevities have different microbial profiles. We sequenced the 16S rRNA gene from soft faeces from 95 does. First, we compared two maternal rabbit lines with different longevities; a standard longevity maternal line (A) and a maternal line (LP) that was founded based on longevity criteria: females with a minimum of 25 parities with an average prolificacy per parity of 9 or more. Second, we compared the gut microbiota of two groups of animals from line LP with different longevities: females that died/were culled with two parities or less (LLP) and females with more than 15 parities (HLP). RESULTS: Differences in alpha and beta diversity were observed between lines A and LP, and a partial least square discriminant analysis (PLS-DA) showed a high prediction accuracy (> 91%) of classification of animals to line A versus LP (146 amplicon sequence variants (ASV)). The PLS-DA also showed a high prediction accuracy (> 94%) to classify animals to the LLP and HLP groups (53 ASV). Interestingly, some of the most important taxa identified in the PLS-DA were common to both comparisons (Akkermansia, Christensenellaceae R-7, Uncultured Eubacteriaceae, among others) and have been reported to be related to resilience and longevity. CONCLUSIONS: Our results indicate that the first parity gut microbiome profile differs between the two rabbit maternal lines (A and LP) and, to a lesser extent, between animals of line LP with different longevities (LLP and HLP). Several genera were able to discriminate animals from the two lines and animals with different longevities, which shows that the gut microbiome could be used as a predictive factor for longevity, or as a selection criterion for these traits.

Effect of environmental variance-based resilience selection on the gut metabolome of rabbits.

BACKGROUND: Gut metabolites are key actors in host-microbiota crosstalk with effect on health. The study of the gut metabolome is an emerging topic in livestock, which can help understand its effect on key traits such as animal resilience and welfare. Animal resilience has now become a major trait of interest because of the high demand for more sustainable production. Composition of the gut microbiome can reveal mechanisms that underlie animal resilience because of its influence on host immunity. Environmental variance (VE), specifically the residual variance, is one measure of resilience. The aim of this study was to identify gut metabolites that underlie differences in the resilience potential of animals originating from a divergent selection for VE of litter size (LS). We performed an untargeted gut metabolome analysis in two divergent rabbit populations for low (n = 13) and high (n = 13) VE of LS. Partial least square-discriminant analysis was undertaken, and Bayesian statistics were computed to determine dissimilarities in the gut metabolites between these two rabbit populations. RESULTS: We identified 15 metabolites that discriminate rabbits from the divergent populations with a prediction performance of 99.2% and 90.4% for the resilient and non-resilient populations, respectively. These metabolites were suggested to be biomarkers of animal resilience as they were the most reliable. Among these, five that derived from the microbiota metabolism (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol, N6-acetyllysine, and serine), were suggested to be indicators of dissimilarities in the microbiome composition between the rabbit populations. The abundances of acylcarnitines and metabolites derived from the phenylalanine, tyrosine, and tryptophan metabolism were low in the resilient population and these pathways can, therefore impact the inflammatory response and health status of animals. CONCLUSIONS: This is the first study to identify gut metabolites that could act as potential resilience biomarkers. The results support differences in resilience between the two studied rabbit populations that were generated by selection for VE of LS. Furthermore, selection for VE of LS modified the gut metabolome, which could be another factor that modulates animal resilience. Further studies are needed to determine the causal role of these metabolites in health and disease.

Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience.

BACKGROUND: Environmental variance (VE) is partially under genetic control, which means that the VE of individuals that share the same environment can differ because they have different genotypes. Previously, a divergent selection experiment for VE of litter size (LS) during 13 generations in rabbit yielded a successful response and revealed differences in resilience between the divergent lines. The aim of the current study was to identify signatures of selection in these divergent lines to better understand the molecular mechanisms and pathways that control VE of LS and animal resilience. Three methods (FST, ROH and varLD) were used to identify signatures of selection in a set of 473 genotypes from these rabbit lines (377) and a base population (96). A whole-genome sequencing (WGS) analysis was performed on 54 animals to detect genes with functional mutations. RESULTS: By combining signatures of selection and WGS data, we detected 373 genes with functional mutations in their transcription units, among which 111 had functions related to the immune system, stress response, reproduction and embryo development, and/or carbohydrate and lipid metabolism. The genes TTC23L, FBXL20, GHDC, ENSOCUG00000031631, SLC18A1, CD300LG, MC2R, and ENSOCUG00000006264 were particularly relevant, since each one carried a functional mutation that was fixed in one of the rabbit lines and absent in the other line. In the 3'UTR region of the MC2R and ENSOCUG00000006264 genes, we detected a novel insertion/deletion (INDEL) variant. CONCLUSIONS: Our findings provide further evidence in favour of VE as a measure of animal resilience. Signatures of selection were identified for VE of LS in genes that have a functional mutation in their transcription units and are mostly implicated in the immune response and stress response pathways. However, the real implications of these genes for VE and animal resilience will need to be assessed through functional analyses.

Genomic regions influencing intramuscular fat in divergently selected rabbit lines.

Intramuscular fat (IMF) is one of the main meat quality traits for breeding programmes in livestock species. The main objective of this study was to identify genomic regions associated with IMF content comparing two rabbit populations divergently selected for this trait, and to generate a list of putative candidate genes. Animals were genotyped using the Affymetrix Axiom OrcunSNP Array (200k). After quality control, the data involved 477 animals and 93 540 SNPs. Two methods were used in this research: single marker regressions with the data adjusted by genomic relatedness, and a Bayesian multiple marker regression. Associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU8 and OCU13. The highest value for the percentage of the genomic variance explained by a genomic region was found in two consecutive genomic windows on OCU8 (7.34%). Genes in the associated regions of OCU1 and OCU8 presented biological functions related to the control of adipose cell function, lipid binding, transportation and localisation (APOLD1, PLBD1, PDE6H, GPRC5D and GPRC5A) and lipid metabolic processes (MTMR2). The EWSR1 gene, underlying the OCU13 region, is linked to the development of brown adipocytes. The findings suggest that there is a large component of polygenic effect behind the differences in IMF content in these two lines, as the variance explained by most of the windows was low. The genomic regions of OCU1, OCU8 and OCU13 revealed novel candidate genes. Further studies would be needed to validate the associations and explore their possible application in selection programmes.

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP.

Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.

A genomewide association study in divergently selected lines in rabbits reveals novel genomic regions associated with litter size traits.

Uterine capacity (UC), defined as the total number of kits from unilaterally ovariectomized does at birth, has a high genetic correlation with litter size. The aim of our research was to identify genomic regions associated with litter size traits through a genomewide association study using rabbits from a divergent selection experiment for UC. A high-density SNP array (200K) was used to genotype 181 does from a control population, high and low UC lines. Traits included total number born (TNB), number born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE) and embryo, foetal and prenatal survivals at second parity. We implemented the Bayes B method and the associations were tested by Bayes factors and the percentage of genomic variance (GV) explained by windows. Different genomic regions associated with TNB, NBA, IE and OR were found. These regions explained 7.36%, 1.27%, 15.87% and 3.95% of GV, respectively. Two consecutive windows on chromosome 17 were associated with TNB, NBA and IE. This genomic region accounted for 6.32% of GV of TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 candidate genes which presented functional annotations linked to some reproductive processes. Our findings suggest that a genomic region on chromosome 17 has an important effect on litter size traits. However, further analyses are needed to validate this region in other maternal rabbit lines.

Selection for environmental variance of litter size in rabbits.

BACKGROUND: In recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare. RESULTS: We developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment. CONCLUSIONS: We conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. This has implications for animal welfare, since animals that cope better with their environment have better welfare than more sensitive animals. We also conclude that selection for reduced environmental variance of litter size does not depress litter size.

Modeling production functions and economic weights in intensive meat production of guinea pigs.

A profit function for a typical commercial farm of intensive guinea pig production was designed. The simulated farm contained 86 cages with a ratio of 7:1 females/males, with continuous mating. Kits were weaned at 15 days of age and slaughtered for meat production at 90 days of age. The absolute (EW) and relative economic weights of the main traits were calculated. The highest EW were kits produced per kindling (US$25), kits weaned per kindling (US$22), kits born alive per kindling (US$20), and the number of kindlings per female and year (US$12). Profit, returns, and costs per female and year were US$15, 68, and 53, respectively. Returns came from the production of young guinea pigs and discarded reproductive adults for meat production, 90 and 10% of the total returns. The highest costs were feeding and labor, 44 and 23% of the total cost. The EW and profit did not substantially change when simulating variations of ±20% in the prices of kilograms of fattening feed and kilograms of live weight of guinea pig, showing their robustness to future variations in market prices or to variations in prices between countries. The results obtained highlight the importance of the feeding costs in the guinea pig meat production.

Correlated Responses to Selection for Intramuscular Fat on the Gut Microbiome in Rabbits.

Intramuscular fat (IMF) content is important for meat production and human health, where the host genetics and its microbiome greatly contribute to its variation. The aim of this study is to describe the consequences of the genetic modification of IMF by selecting the taxonomic composition of the microbiome, using rabbits from the 10th generation of a divergent selection experiment for IMF (high (H) and low (L) lines differ by 3.8 standard deviations). The selection altered the composition of the gut microbiota. Correlated responses were better distinguished at the genus level (51 genera) than at the phylum level (10 phyla). The H-line was enriched in Hungateiclostridium, Limosilactobacillus, Legionella, Lysinibacillus, Phorphyromonas, Methanosphaera, Desulfovibrio, and Akkermansia, while the L-line was enriched in Escherichia, Methanobrevibacter, Fonticella, Candidatus Amulumruptor, Methanobrevibacter, Exiguobacterium, Flintibacter, and Coprococcus, among other genera with smaller line differences. A microbial biomarker generated from the abundance of four of these genera classified the lines with 78% accuracy in a logit regression. Our results demonstrate different gut microbiome compositions in hosts with divergent IMF genotypes. Furthermore, we provide a microbial biomarker to be used as an indicator of hosts genetically predisposed to accumulate muscle lipids, which opens up the opportunity for research to develop probiotics or microbiome-based breeding strategies targeting IMF.

Plasma metabolomic profiling in two rabbit lines divergently selected for intramuscular fat content.

This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity supported its relevant role in the lipid deposition. Future research will focus on the analysis of the metabolomic profile of the cecum content, and on the integration of the several -omics datasets available for these lines, to help disentangle the host and microbiome biological mechanisms involved in the IMF deposition.

Selection for environmental variance shifted the gut microbiome composition driving animal resilience.

BACKGROUND: Understanding how the host's microbiome shapes phenotypes and participates in the host response to selection is fundamental for evolutionists and animal and plant breeders. Currently, selection for resilience is considered a critical step in improving the sustainability of livestock systems. Environmental variance (V E), the within-individual variance of a trait, has been successfully used as a proxy for animal resilience. Selection for reduced V E could effectively shift gut microbiome composition; reshape the inflammatory response, triglyceride, and cholesterol levels; and drive animal resilience. This study aimed to determine the gut microbiome composition underlying the V E of litter size (LS), for which we performed a metagenomic analysis in two rabbit populations divergently selected for low (n = 36) and high (n = 34) V E of LS. Partial least square-discriminant analysis and alpha- and beta-diversity were computed to determine the differences in gut microbiome composition among the rabbit populations. RESULTS: We identified 116 KEGG IDs, 164 COG IDs, and 32 species with differences in abundance between the two rabbit populations studied. These variables achieved a classification performance of the V E rabbit populations of over than 80%. Compared to the high V E population, the low V E (resilient) population was characterized by an underrepresentation of Megasphaera sp., Acetatifactor muris, Bacteroidetes rodentium, Ruminococcus bromii, Bacteroidetes togonis, and Eggerthella sp. and greater abundances of Alistipes shahii, Alistipes putredinis, Odoribacter splanchnicus, Limosilactobacillus fermentum, and Sutterella, among others. Differences in abundance were also found in pathways related to biofilm formation, quorum sensing, glutamate, and amino acid aromatic metabolism. All these results suggest differences in gut immunity modulation, closely related to resilience. CONCLUSIONS: This is the first study to show that selection for V E of LS can shift the gut microbiome composition. The results revealed differences in microbiome composition related to gut immunity modulation, which could contribute to the differences in resilience among rabbit populations. The selection-driven shifts in gut microbiome composition should make a substantial contribution to the remarkable genetic response observed in the V E rabbit populations. Video Abstract.

Genome-Wide Association Study of Maternal Genetic Effects on Intramuscular Fat and Fatty Acid Composition in Rabbits.

Maternal genetic effects (MGE) could affect meat quality traits such as intramuscular fat (IMF) and its fatty acid composition. However, it has been scarcely studied, especially in rabbits. The objectives of the present study were, first, to assess the importance of MGE on intramuscular fat and fatty acid composition by applying a Bayesian maternal animal model in two rabbit lines divergently selected for IMF. The second objective was to identify genomic regions and candidate genes of MGE that are associated with the traits of these offspring, using Bayesian methods in a Genome Wide Association Study (GWAS). Quantitative analyses were performed using data from 1982 rabbits, and 349 animals from the 9th generation and 76 dams of the 8th generation with 88,512 SNPs were used for the GWAS. The studied traits were IMF, saturated fatty acids (total SFA, C14:0; myristic acid, C16:0; palmitic acid and C18:0; stearic acid), monounsaturated fatty acids (total MUFA, C16:1n-7; palmitoleic acid and C18:1n-9; oleic acid), polyunsaturated fatty acids (total PUFA, C18:2n-6; linoleic acid, C18:3n-3; α-linolenic acid and C20:4n-6; arachidonic acid), MUFA/SFA and PUFA/SFA. The proportion of phenotypic variance explained by the maternal genetic effect ranged from 8 to 22% for IMF, depending on the model. For fatty acid composition, the proportion of phenotypic variance explained by maternal genetic effects varied from 10% (C18:0) to 46% (MUFA) in a model including both direct and additive maternal genetic effects, together with the common litter effect as a random variable. In particular, there were significant direct maternal genetic correlations for C16:0, C18:1n9, C18:2n6, SFA, MUFA, and PUFA with values ranging from -0.53 to -0.89. Relevant associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU5 and OCU19 containing some relevant candidates (TANC2, ACE, MAP3K3, TEX2, PRKCA, SH3GL2, CNTLN, RPGRIP1L and FTO) related to lipid metabolism, binding, and obesity. These regions explained about 1.2 to 13.9% of the total genomic variance of the traits studied. Our results showed an important maternal genetic effect on IMF and its fatty acid composition in rabbits and identified promising candidate genes associated with these traits.

Intramuscular Fat Selection in Rabbits Modifies the Fatty Acid Composition of Muscle and Liver Tissues.

This study was conducted on two rabbit lines divergently selected for intramuscular fat (IMF) content in the Longissimus thoracis et lumborum (LTL) muscle. The aim was to estimate the direct response to selection for IMF after 10 generations, and the correlated responses in carcass quality traits, meat fatty acid content, liver fat and its fatty acid content, and in plasma metabolic markers related to liver metabolism. Selection for IMF content was successful, showing a direct response equivalent to 3.8 SD of the trait after 10 generations. The high-IMF line (H) showed a greater dissectible fat percentage than the low-IMF line (L), with a relevant difference (DH-L = 0.63%, Pr = 1). No difference was found in liver fat content (DH-L = -0.04, P0 = 0.62). The fatty acid content of both LTL muscle and liver was modified after selection. The LTL muscle had greater saturated (SFA; DH-L = 5.05, Pr = 1) and monounsaturated fatty acids (MUFA; DH-L = 5.04, Pr = 1) contents in the H line than in the L line. No relevant difference was found in polyunsaturated fatty acids content (PUFA; Pr = 0.05); however, greater amounts of C18:2n6 (DH-L = 3.03, Pr = 1) and C18:3n3 (DH-L = 0.56, Pr = 1) were found in the H than in the L line. The liver presented greater MUFA (DH-L = 1.46) and lower PUFA (DH-L = -1.46) contents in the H than in the L line, but the difference was only relevant for MUFA (Pr = 0.86). The odd-chain saturated fatty acids C15:0 and C17:0 were more abundant in the liver of the L line than in the liver of the H line (DH-L = -0.04, Pr = 0.98 for C15:0; DH-L = -0.09, Pr = 0.92 for C17:0). Greater concentrations of plasma triglycerides (DH-L = -34) and cholesterol (DH-L = -3.85) were found in the L than in the H line, together with greater plasma concentration of bile acids (DH-L = -2.13). Nonetheless, the difference was only relevant for triglycerides (Pr = 0.98).

Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation.

Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc.). These data are generally regarded as compositional since the total number of counts identified within a sample is irrelevant. The central concept in compositional data analysis is the logratio transformation, the simplest being the additive logratios with respect to a fixed reference component. A full set of additive logratios is not isometric, that is they do not reproduce the geometry of all pairwise logratios exactly, but their lack of isometry can be measured by the Procrustes correlation. The reference component can be chosen to maximize the Procrustes correlation between the additive logratio geometry and the exact logratio geometry, and for high-dimensional data there are many potential references. As a secondary criterion, minimizing the variance of the reference component's log-transformed relative abundance values makes the subsequent interpretation of the logratios even easier. On each of three high-dimensional omics datasets the additive logratio transformation was performed, using references that were identified according to the abovementioned criteria. For each dataset the compositional data structure was successfully reproduced, that is the additive logratios were very close to being isometric. The Procrustes correlations achieved for these datasets were 0.9991, 0.9974, and 0.9902, respectively. We thus demonstrate, for high-dimensional compositional data, that additive logratios can provide a valid choice as transformed variables, which (a) are subcompositionally coherent, (b) explain 100% of the total logratio variance and (c) come measurably very close to being isometric. The interpretation of additive logratios is much simpler than the complex isometric alternatives and, when the variance of the log-transformed reference is very low, it is even simpler since each additive logratio can be identified with a corresponding compositional component.

Correlated Response on Growth Traits and Their Variabilities to Selection for Ovulation Rate in Rabbits Using Genetic Trends and a Cryopreserved Control Population.

The aim of this work was to estimate correlated responses in growth traits and their variabilities in an experiment of selection for ovulation rate during 10 generations in rabbits. Individual weight at 28 days old (IW28, kg) and at 63 days old (IW63, kg) was analyzed, as well as individual growth rate (IGR = IW63 - IW28, kg). The variability of each growth trait was calculated as the absolute value of the difference between the individual value and the mean value of their litter. Data were analyzed using Bayesian methodology. The estimated heritabilities of IW28, IW63 and IGR were low, whereas negligible heritabilities were obtained for growth variability traits. The common litter effect was high for all growth traits, around 30% of the phenotypic variance, whereas low maternal effect for all growth traits was obtained. Low genetic correlations between ovulation rate and growth traits were found, and also between ovulation rate and the variability of growth traits. Therefore, genetic trends methods did not show correlated responses in growth traits. A similar result was also obtained using a cryopreserved control population.

Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits.

Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1-S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson's correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.

Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment.

Our study provides an exhaustive comparison of the microbiome core functionalities (captured by 3,936 microbial gene abundances) between hosts with divergent genotypes for intramuscular lipid deposition. After 10 generations of divergent selection for intramuscular fat in rabbits and 4.14 phenotypic standard deviations (SD) of selection response, we applied a combination of compositional and multivariate statistical techniques to identify 122 cecum microbial genes with differential abundances between the lines (ranging from -0.75 to +0.73 SD). This work elucidates that microbial biosynthesis lipopolysaccharides, peptidoglycans, lipoproteins, mucin components, and NADH reductases, amongst others, are influenced by the host genetic determination for lipid accretion in muscle. We also differentiated between host-genetically influenced microbial mechanisms regulating lipid deposition in body or intramuscular reservoirs, with only 28 out of 122 MGs commonly contributing to both. Importantly, the results of this study are of relevant interest for the efficient development of strategies fighting obesity.

Novel Genomic Regions Associated with Intramuscular Fatty Acid Composition in Rabbits.

Intramuscular fat (IMF) content and its composition affect the quality of meat. Selection for IMF generated a correlated response on its fatty acid composition. The increase of IMF content is associated with an increase of its saturated (SFA) and monounsaturated (MUFA) fatty acids, and consequently a decrease of polyunsaturated fatty acids (PUFA). We carried out a genome wide association study (GWAS) for IMF composition on two rabbit lines divergently selected for IMF content, using a Bayes B procedure. Association analyses were performed using 475 individuals and 90,235 Single Nucleotide Polymorphisms (SNPs). The main objectives were to identify genomic regions associated with the IMF composition and to generate a list of candidate genes. Genomic regions associated with the intramuscular fatty acid composition were spread across different rabbit chromosomes (OCU). An important region at 34.0-37.9 Mb on OCU1 was associated with C14:0, C16:0, SFA, and C18:2n6, explaining 3.5%, 11.2%, 11.3%, and 3.2% of the genomic variance, respectively. Another relevant genomic region was found to be associated at 46.0-48.9 Mb on OCU18, explaining up to 8% of the genomic variance of MUFA/SFA. The associated regions harbor several genes related to lipid metabolism, such as SCD, PLIN2, and ERLIN1. The main genomic regions associated with the fatty acids were not previously associated with IMF content in rabbits. Nonetheless, MTMR2 is the only gene that was associated with both the IMF content and composition in rabbits. Our study highlighted the polygenic nature of the fatty acids in rabbits and elucidated its genetic background.

Correlated Response to Selection for Litter Size Residual Variability in Rabbits' Body Condition.

A divergent selection experiment for residual variance of litter size at birth was carried out in rabbits during twelve generations. Residual variance of litter size was estimated as the within-doe variance of litter size after pre-correction for year and season as well as parity and lactation status effects. The aim of this work was to study the correlated response to selection for litter size residual variability in body condition from mating to weaning. Body condition is related directly to an animal's fat deposits. Perirenal fat is the main fat deposit in rabbits. Individual body weight (IBW) and perirenal fat thickness (PFT) were used to measure body condition at second mating, delivery, 10 days after delivery, and weaning. Litter size of the first three parities was analyzed. Both lines decreased body condition between mating to delivery; however, the decrease in body condition at delivery was lower in the low line, despite this line having higher litter size at birth (+0.54 kits, p = 0.93). The increment of body condition between delivery and early lactation was slightly higher in the low line. On the other hand, body condition affected success of females' receptivity and fertility at the third mating, e.g., receptive females showed a higher IBW and PFT than unreceptive ones (+129 g and +0.28 mm, respectively), and fertile females had a higher IBW and PFT than unfertile ones (+82 g and +0.28 mm, respectively). In conclusion, the does selected for reducing litter size variability showed a better deal with situations of high-energy demand, such as delivery and lactation, than those selected for increasing litter size variability, which would agree with the better health and welfare condition in the low line.