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BackgroundWith the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not. We also investigated two alternative strategies to accommodate phenotypes of open-mated DPQs in the genetic evaluation, adding either a dummy pseudo sire in the pedigree, or a non-genetic effect to the statistical evaluation model to account for the origin of the DPQs’ mates.ResultsWhen breeding queens were inseminated with semen from drones of a single DPQ and this was known, estimates of genetic parameters and genetic trends were more precise. If they were inseminated using drones from a single or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in the estimates. For colony phenotypes of open-mated DPQs, adding a dummy pseudo sire in the pedigree for each mating location led to considerable overestimation of genetic variances, while correcting for the mating area by adding a non-genetic effect in the evaluation model gave unbiased estimates.ConclusionsKnowing only the dam of the DPQ(s) in the mating may lead to erroneous assumptions on how DPQs were used and cause severe biases in estimates of genetic parameters and genetic trends. Therefore, keeping track in the pedigree of which DPQ(s), and not only which dam of DPQ(s) are used, is recommended. Records from DPQ colonies with queens open mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Accurately measuring individual feed intake is required to include feed efficiency (FE) as an objective in commercial breeding programs. Phenotyping individual feed intake through direct measurements remains complex in fish reared in groups. One way to overcome this challenge is to find proxies for estimating FE. This study aimed to investigate the correlations between fish FE and potential predictive criteria in rainbow troutOncorhynchus mykiss. As predictive criteria, we considered the variations of body weight assessed as thermal growth coefficients (TGC) and the feeding behaviour assessed as the number of feed demands over a period and the proportion of the demands made before noon. Feed intake was assessed over three different periods in ten isogenic lines allowing a recording for each of the ten genotypes while keeping fish in groups. The protocol consisted of two successive feed deprivation and refeeding phases after initial basal growth periods. Correlations were then calculated between FE, measured either as feed conversion ratio (FCR) or residual feed intake (RFI), and the different indirect criteria. We found positive phenotypic correlations between FCR and RFI over the feed intake measurement periods (r ⩾ 0.79,P< 0.001). Assessment of the relationship between FE traits (FCR and RFI) calculated over the three measurement periods and TGC revealed no significant association. We found significant positive correlations between RFI calculated from the first feed intake measurement period and feeding behaviour, assessed as the number of feed demands (r = 0.42-0.49,P⩽ 0.022). Nevertheless, such correlations were not established for the two other measurement periods following feed deprivation. While we demonstrated that the weight variations during feed deprivation are not a good proxy for FE (FCR or RFI) in rainbow trout, we also highlighted the interest in exploring more the relationships between FE and feeding behaviour in fish.

Evolutionary processes leave footprints across the genome over time. Highly homozygous regions may correspond to positive selection of favourable alleles, while maintenance of heterozygous regions may be due to balancing selection phenomena. We analyzed 176 genomes coming from 20 sequenced US fish and 156 fish from three different French lines that were genotyped using a HD Axiom Trout Genotyping 665K SNP Array. Using methods based on either Run of Homozygosity or Extended Haplotype Homozygosity, we detected selection signals in four domesticated rainbow trout populations. Nine genomic regions composed of 253 genes, mainly located on chromosome 2 but also on chromosomes 12, 15, 16, and 20, were identified under positive selection in all four populations. In addition, four heterozygous regions containing 29 genes putatively under balancing selection were also shared by the four populations and located on chromosomes 10, 13, and 19. Whatever the homozygous or heterozygous nature of the region, we always found some genes highly conserved among vertebrates due to their critical roles in cellular and nuclear organisation, embryonic development or immunity. We identify new promising candidate genes involved in rainbow trout fitness, as well as genes already detected under positive selection in other fishes (auts2, atp1b3, zp4, znf135, igf-1α, brd2, col9a2, mrap2, pbx1, emilin-3). These findings represent a genome-wide map of signatures of selection common over rainbow trout populations, which is the foundation to understand the processes in action and to identify what kind of diversity should be preserved, or conversely avoided in breeding programs, in order to maintain or improve essential biological functions in domesticated rainbow trout populations.

Recent studies have shown that current levels of inbreeding, estimated by runs of homozygosity (ROH), are moderate to high in farmed rainbow trout lines. Based on ROH metrics, the aims of our study were to (i) quantify inbreeding effects on female size (postspawning body weight, fork length) and reproduction traits (spawning date, coelomic fluid weight, spawn weight, egg number, average egg weight) in rainbow trout, and (ii) identify both the genomic regions and inbreeding events affecting performance. We analysed the performance of 1346 females under linear animal models including random additive and dominance genetics effects, with fixed covariates accounting for inbreeding effects at different temporal and genomic scales. A significant effect of genome-wide inbreeding (

Background Efficient breeding programs are difficult to implement in honeybees due to their biological specificities (polyandry and haplo-diploidy) and complexity of the traits of interest, with performances being measured at the colony scale and resulting from the joint effects of tens of thousands of workers (called direct effects) and of the queen (called maternal effects). We implemented a Monte Carlo simulation program of a breeding plan designed specifically for Apis mellifera’s populations to assess the impact of polyandry versus monoandry on colony performance, inbreeding level and genetic gain depending on the individual selection strategy considered, i.e. complete mass selection or within-family (maternal lines) selection. We simulated several scenarios with different parameter setups by varying initial genetic variances and correlations between direct and maternal effects, the selection strategy and the polyandry level. Selection was performed on colony phenotypes. Results All scenarios showed strong increases in direct breeding values of queens after 20 years of selection. Monoandry led to significantly higher direct than maternal genetic gains, especially when a negative correlation between direct and maternal effects was simulated. However, the relative increase in these genetic gains depended also on their initial genetic variability and on the selection strategy. When polyandry was simulated, the results were very similar with either 8 or 16 drones mated to each queen. Across scenarios, polyandrous mating resulted in equivalent or higher gains in performance than monoandrous mating, but with considerably lower inbreeding rates. Mass selection conferred a ~ 20% increase in performance compared to within-family selection, but was also accompanied by a strong increase in inbreeding levels (25 to 50% higher). Conclusions Our study is the first to compare the long-term effects of polyandrous versus monoandrous mating in honeybee breeding. The latter is an emergent strategy to improve specific traits, such as resistance to varroa, which can be difficult or expensive to phenotype. However, if used during several generations in a closed population, monoandrous mating increases the inbreeding level of queens much more than polyandrous mating, which is a strong limitation of this strategy.

BackgroundSelective breeding is a promising solution to reduce fish farms vulnerability to heat peaks which intensity and frequency are predicted to increase due to climate change. However, limited information about the genetic architecture of acute hyperthermia resistance in fish is available.Two batches of sibs from a rainbow trout commercial line were produced. The first batch (N=1,382) was phenotyped for acute hyperthermia resistance at nine months, and the second batch (N=1,506) was phenotyped for main production traits (growth, body length, muscle fat content and carcass yield) at twenty months. Fish were genotyped on a 57K SNP array, and their genotypes were imputed at high-density thanks to their parents being genotyped on a 665K SNP array.ResultsThe heritability estimate of resistance to acute hyperthermia in juveniles was 0.29 ± 0.05, confirming the potential of selective breeding for this trait. Genetic correlations between acute hyperthermia resistance and main production traits at near harvest age were all close to zero. Hence, selecting for acute hyperthermia resistance should not impact the main production traits, and reversely.The genome-wide association study revealed that resistance to acute hyperthermia is highly polygenic; altogether, the six detected QTL explained less than 5% of the genetic variance. Two of these QTL, including the most significant one, might explain acute hyperthermia resistance differences across INRAE isogenic lines of rainbow trout. The phenotypic mean differences between homozygotes at peak SNP were up to 69% of the phenotypic standard deviation, showing promising potential for marker-assisted selection. We identified 89 candidate genes within the six QTL regions, among which the most convincing functional candidate genes werednajc7,hsp70b,nkiras2,cdk12,phb,fkbp10,ddx5,cygb1,enpp7,pdhxandacly.ConclusionsThis study provides valuable insight on the genetic architecture of acute hyperthermia resistance in juvenile rainbow trout. The potential for the selective breeding of this trait was shown to be substantial and should not interfere with selection for main production traits. Identified functional candidate genes give a new insight on physiological mechanisms involved in acute hyperthermia resistance, such as protein chaperoning, oxidative stress response, homeostasis maintenance and cell survival.

BackgroundViral Nervous Necrosis (VNN) is major disease affecting of European sea bass. Understanding the biological mechanisms that underlie VNN resistance is thus important for the welfare of farmed fish and the sustainability of production systems. This study aimed at identifying key genomic regions and genes that determine VNN resistance in sea bass.ResultsWe generated a dataset of around 900,000 single nucleotide polymorphisms (SNPs) identified from whole-genome sequencing (WGS) in the parental generation in two different commercial populations (pop A and pop B) comprising 2371 and 3428 European sea bass with phenotypic records for binary survival in a VNN challenge. In each commercial population, three cohorts were submitted to the redspotted grouper nervous necrosis virus (RGNNV) challenge by immersion and genotyped on a 57K SNP chip. After imputation of WGS SNPs from their parents, QTL mapping was performed using a Bayesian Sparse Linear Mixed Model (BSLMM). We found several QTL regions on different linkage groups (LG), most of which are specific to a single population, but a QTL region on LG12 was shared by both commercial populations. This QTL region is only 127 kB wide, and we identified IFI6, an interferon induced protein at only 1.9 kB of the most significant SNP. An unrelated validation population with 4 large families was used to validate the effect of the QTL, for which the survival of the susceptible genotype ranges from 39.8 to 45.4%, while that of the resistant genotype ranges from 63.8 to 70.8%.ConclusionsWe could precisely locate the genomic region implied in the main resistance QTL at less than 1.9 kb of the interferon alpha inducible protein 6 (IFI6), which has already been identified as a key player for other viral infections such as hepatitis B and C. This will lead to major improvements for sea bass breeding programs, allowing for greater genetic gain by using marker-assisted genomic selection to obtain more resistant fish. Further functional analyses are needed to evaluate the impact of the variant on the expression of this gene.

Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.

Background In response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n−3 long-chain polyunsaturated fatty acids (PUFAs)—especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids—in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection. Results Proportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection (using a reference population of 1000 individuals related to selection candidates) or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait. Conclusion This study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species.

International audience; In rainbow trout farming, Flavobacterium psychrophilum, the causative agent of bacterial cold water disease, is responsible for important economic losses. Resistance to F. psychrophilum is heritable, and several quantitative trait loci (QTL) with moderate effects have been detected, opening up promising perspectives for the genetic improvement of resistance. In most studies however, resistance to F. psychrophilum was assessed in experimental infectious challenges using injection as the infection route, which is not representative of natural infection. Indeed, injection bypasses external barriers, such as mucus and skin, that likely play a protective role against the infection. In this study, we aimed at describing the genetic architecture of the resistance to F. psychrophilum after a natural disease outbreak. In a 2000-fish cohort, reared on a French farm, 720 fish were sampled and genotyped using the medium-throughput Axiom (TM) Trout Genotyping Array. Overall mortality at the end of the outbreak was 25%. Genome-wide association studies were performed under two different models for time to death measured on 706 fish with validated genotypes for 30 060 SNPs. This study confirms the polygenic inheritance of resistance to F. psychrophilum with a few QTL with moderate effects and a large polygenic background, the heritability of the trait being estimated at 0.34. Two new chromosome-wide significant QTL and three suggestive QTL were detected, each of them explaining between 1% and 4% of genetic variance.

International audience; Viral Nervous Necrosis (VNN) is a major threat for the European sea bass (Dicentrarchus labrax) aquaculture industry. The improvement of disease resistance through selective breeding is a promising option to reduce outbreaks. With the development of high-throughput genotyping technologies, identification of genomic regions involved in the resistance could improve the efficiency of selective breeding. The aim of this study was to identify quantitative trait loci (QTL) involved in VNN resistance and to quantify their effect.Four experimental backcross families comprising 378, 454, 291 and 211 individuals and two commercial populations A and B comprising 1027 and 1042 individuals obtained from partial factorial crosses (59 male x 20 female for pop A; 39 male x 14 female for pop B) were submitted to a redspotted grouper nervous necrosis virus (RGNNV) challenge by bath. A high-density single nucleotide polymorphism (SNP) chip panel was designed to develop the ThermoFisher Axiom (TM) 57k SNP DlabChip, which was used for genotyping all individuals and building a high quality linkage map. In the backcross families, composite interval mapping was performed on 30,917, 23,592, 30,656 and 31,490 markers, respectively. In the commercial populations, 40,263 markers in pop A and 41,166 markers in pop B were used to perform genome-wide association studies (GWAS) using a GBLUP and a BayesC pi approach.One QTL was identified on chromosome LG12 in three of the four experimental backcross families, and one additional QTL on LG8 was detected in only one family. In commercial populations, QTL mapping revealed a total of seven QTLs, among which the previously mentioned QTL on LG12 was detected in both. This QTL, which was mapped to an interval of 3.45 cM, explained 9.21% of the total genetic variance in pop A, while other identified QTLs individually explained less than 1% of the total genetic variance.The identification of QTL regions involved in VNN resistance in European sea bass, with one having a strong effect, should have a great impact on the aquaculture industry. Future work could focus on the fine mapping of the causal mutation present on LG12 using whole genome sequencing.

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1–4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.

Rainbow trout has a male heterogametic (XY) sex determination system controlled by a major sex-determining gene, sdY. Unexpectedly, a few phenotypically masculinised fish are regularly observed in all-female farmed trout stocks. To better understand the genetic determinism underlying spontaneous maleness in XX-rainbow trout, we recorded the phenotypic sex of 20,210 XX-rainbow trout from a French farm population at 10 and 15 months post-hatching. The overall masculinisation rate was 1.45%. We performed two genome-wide association studies (GWAS) on a subsample of 1139 individuals classified as females, intersex or males using either medium-throughput genotyping (31,811 SNPs) or whole-genome sequencing (WGS, 8.7 million SNPs). The genomic heritability of maleness ranged between 0.48 and 0.62 depending on the method and the number of SNPs used for the estimation. At the 31K SNPs level, we detected four QTL on three chromosomes (Omy1, Omy12 and Omy20). Using WGS information, we narrowed down the positions of the two QTL detected on Omy1 to 96 kb and 347 kb respectively, with the second QTL explaining up to 14% of the total genetic variance of maleness. Within this QTL, we detected three putative candidate genes, fgfa8, cyp17a1 and an uncharacterised protein (LOC110527930), which might be involved in spontaneous maleness of XX-female rainbow trout.

Background Rainbow trout is a significant fish farming industry under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits. Results A pedigreed population of 1,343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits. Conclusions Our genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

International audience; L’agroécologie a été choisie par l’Inra comme l’un des chantiers de prospective interdisciplinaire destiné à identifier les fronts de recherche en réponse à de grands défis sociétaux. Quatre-vingt chercheurs ont dressé un bilan, et proposé des pistes de recherche pour l’agroécologie, et cet ouvrage en synthétise les principales conclusions. L’agroécologie, en tant que discipline scientifique, remettant l’écologie au centre de la conception des systèmes agricoles, est maintenant bien présente. Diversifier le vivant dans des agroécosystèmes est une visée à large spectre, pour les rendre plus robustes, plus résilients. Les recherches en génétique et en écologie du paysage sont mobilisées pour que l’agroécologie utilise des leviers de la parcelle au paysage. La modélisation des systèmes agroécologiques se développe pour mieux comprendre les interactions biotiques et abiotiques multiples, les prédire, et commencer à piloter certains systèmes. La diversification du vivant dans la production agricole (espèces, variétés, successions culturales, etc.) conduit à des produits plus variés. Les conséquences seront importantes sur les filières, ou plus exactement sur les systèmes agri-alimentaires, allant du mode de production aux produits consommés. Ces changements s’inscrivent sur le long terme. La transition agroécologique, adaptative, se coconstruisant avec les acteurs, est en soi un sujet de recherche, et pourra s’appuyer sur des dispositifs expérimentaux, des exploitations agricoles, des territoires d’innovation.

BackgroundRainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits.ResultsA pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits.ConclusionsOur genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

The objective of this study was to investigate the factors that influence calf health and survival in Charolais cattle. Data from 2,740 calves, originating from 16 French farms and observed from birth until 30 d of age, were analyzed using models that took account of direct genetic, maternal genetic, and common environmental effects. Both direct and maternal genetic parameters were estimated for birth weight (BW), calving ease (CE), neonatal vitality (NV), survival at 30 d (Surv), and umbilical infection and diarrhea at different ages (0 to 5 d: Umb1 and Diar1; 6 to 20 d: Umb2 and Diar2; and 21 to 30 d: Umb3 and Diar3). The heritability values for direct and maternal genetic effects were, 0.026 (SE = 0.027) and 0.096 (SE = 0.042) for Surv, 0.280 (SE = 0.063) and 0.063 (SE = 0.038) for BW, 0.129 (SE = 0.041) and 0 for CE, 0.073 (SE = 0.035) and 0 for NV, 0.071 (SE = 0.038) and 0.017 (SE = 0.026) for Umb1, 0 and 0.082 (SE = 0.029) for Umb2, 0 and 0.044 (SE = 0.030) for Diar1, 0.016 (SE = 0.022) and 0.012 (SE = 0.026) for Diar2, and 0.016 (SE = 0.028) and 0 for Diar3, respectively. Significant genetic variability in beef cattle was thus revealed for five calf health traits: NV, Surv, Diar1, Umb1, and Umb2. In addition, for three traits (Surv, Diar1, and Umb2), maternal genetic effects clearly contributed more to health performance than direct genetic effects. Estimates of genetic correlation between traits varied markedly (from 0 to 1 in absolute values) depending on the traits in question, the age for a given trait, and the type (direct or maternal) of the genetic effects considered. These results suggest that not all health traits in Charolais cattle can be improved simultaneously, and breeders will therefore have to prioritize certain traits of interest in their breeding objectives. Overall, our results demonstrate the potential utility of collecting and integrating data on calf diseases, NV and survival in future beef cattle breeding programs. To ensure appropriate biological and genetic evaluations of calf health performance, it is important to accurately describe the phenotypes for diarrhea and umbilical infections (in terms of age ranges) and account for maternal genetic and common environmental effects that explain calf health performance traits. Further investigation and improved data collection are now necessary to maximize the efficiency of breeding schemes designed to simultaneously improve production and health traits.

We characterized genetically the longevity and the productive life of beef cows considering three different traits: length of true life (LTL), the length of productive life (LPL) and a number of calvings (NC), in the Retinta breed. Data considered in the analysis were collected in 30 Spanish herds and consisted of 3187 cows born between 1993 and 2013: 1802 cows had complete information while 1385 cows had censored information. Pedigree information accounted for 7359 individuals and the genetic evaluation was performed under an animal model using the Weibull proportional hazard model for survival analysis. Results indicated that cow effects (age at first calving, herd, season-year of cow birth), as well as some calf effects (breed group, season-year of birth), were significant (P, Instituto de Genética Veterinaria

GWAS reveals several genomic regions governing spontaneous XX-maleness in rainbow trout. Aquaculture Europe 2019

International audience

International audience; Characterising the genetic diversity of populations allows to better understand their demographic history and their adaptation to selective pressures. In honey bees, this characterisation is facilitated by a relatively small genome size, but is hindered by the fact that often the unit of observation and sampling is the colony rather than a single individual. Moreover, performing large scale genetic analyses of honey bees is a real challenge, due to the specific reproduction mechanism including multi-male insemination, making the genotype of a bee colony a mixture of contribution from the queen and the mating drones. In this work we propose an approach to characterise the genotype of a colony based on pool sequencing of worker bees. We introduce statistical models for the analysis of pool sequence data allowing to reconstruct jointly individual queen genotypes of colonies and allele frequencies in bee populations. We demonstrate the performance of our approach using data on 1500 colonies collected throughout three years within the FranceAgriMer funded, BeeStrong project. Population admixture, in terms of queen sub-species composition, validation was accomplished using information on geographical and sociological organisation of the beekeepers. In addition to a better understanding of the population dynamics of honey bees, our approach to genotyping bee colonies promises to facilitate the genetic analysis of complex traits, and can be used for genome wide association studies on phenotypes of interests, for instance to assess Varroa resistance in honey bee populations.

Session : Génétique et Sélection; Masculinisation spontanée chez la truite arc-en-ciel : de nouvelles régions du génome identifiées. 6. Journées de la Recherche Filière Piscicole

WOS:000496576600001; International audience; In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood-based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user-defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user-set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (\textgreater200) are genotyped. We have demonstrated that APIS is an easy-to-use and reliable software for parentage assignment, even when up to 50% of sires are missing.

International audience

L’agro-écologie utilise les processus naturels et les ressources locales plutôt que des intrants chimiques pour assurer la production tout en limitant l’impact environnemental de l’agriculture. A cette fin, des innovations en génétique sont nécessaires pour disposer d’animaux productifs et adaptés à des contextes locaux variés. Pour toute filière d’élevage, réorienter les programmes de sélection actuels semble plus utile que développer des programmes spécifiques qui peineront à être efficaces en termes de coûts et de progrès génétiques attendus. Améliorer la robustesse des animaux vise à promouvoir leurs capacités d’adaptation en intégrant de nombreux critères de sélection, en particulier de reproduction, santé, et d’adaptation à des ressources alimentaires peu digestibles et au changement climatique. Cela amène à considérer les interactions génotype-environnement dans la prédiction des valeurs génétiques et à évaluer les performances des animaux dans des systèmes à bas intrants pour sélectionner ceux qui sont adaptés à des conditions limitantes. La standardisation tant des animaux que des conditions d’élevage paraît antinomique avec les principes de l’agro-écologie prônant une adaptation des animaux aux ressources et contraintes locales dans des systèmes peu artificialisés et très liés à leur environnement physique. Il n’y a donc pas UN animal type agro-écologique, mais DES animaux aux profils variés permettant de répondre aux attentes de l’agro-écologie. Face à la diversité des milieux et des systèmes d’élevage, il faut conserver une diversité génétique importante intra-race, mais aussi préserver la diversité génétique entre races. Cela nécessite une caractérisation phénotypique et génétique des races locales. Enfin, il s’agit de favoriser l'appropriation par les éleveurs et les techniciens d’élevage des outils et des ressources génétiques disponibles pour répondre à leurs besoins., Agroecology uses natural processes and local resources rather than chemical inputs to ensure production while limiting the environmental footprint of agriculture. In this context, breeding innovations are necessary to obtain animals that are both productive and adapted to a broad range of local contexts. Whatever the animal sector, reorienting current breeding programmes seems to be more useful than developing programmes dedicated to agroecological systems that will struggle to be really effective in terms of costs and expected genetic progress. Breeding for robustness aims to promote individual adaptive capacities by considering various selection criteria including reproduction, health, and adaptation to rough feed resources, warm climate or fluctuating environmental conditions. It leads considering genotype-environment interactions in the prediction of breeding values. Animal performance should be evaluated in low-input systems in order to select those animals adapted to limiting conditions. Standardization of both animals and breeding conditions appears contradictory to the agroecological paradigm that calls for an adaptation of animals to local opportunities and constraints in weakly artificialized systems tied to their physical environment. There is thus not a single agroecological animal type, but animals with various profiles that meet the expectations of agroecology. As well as supporting the value of within-breed diversity, we must preserve between-breed diversity to maintain numerous options for adaptation to a variety of production environments and contexts. This may involve to phenotypically and genetically characterize local breeds. Last but not least, improved assimilation by farmers and farm technicians of available genetic resources and breeding tools is necessary.

International audience; The objective of the study was to develop a genomic evaluation for French beef cattle breeds and assess accuracy and bias of prediction for different genomic selection strategies. Based on a reference population of 2,682 Charolais bulls and cows, genotyped or imputed to a high-density SNP panel (777K SNP), we tested the influence of different statistical methods, marker densities (50K versus 777K), and training population sizes and structures on the quality of predictions. Four different training sets containing up to 1,979 animals and a unique validation set of 703 young bulls only known on their individual performances were formed. BayesC method had the largest average accuracy compared to genomic BLUP or pedigree-based BLUP. No gain of accuracy was observed when increasing the density of markers from 50K to 777K. For a BayesC model and 777K SNP panels, the accuracy calculated as the correlation between genomic predictions and deregressed EBV (DEBV) divided by the square root of heritability was 0.42 for birth weight, 0.34 for calving ease, 0.45 for weaning weight, 0.52 for muscular development, and 0.27 for skeletal development. Half of the training set constituted animals having only their own performance recorded, whose contribution only represented 5% of the accuracy. Using DEBV as a response brought greater accuracy than using EBV (+5% on average). Considering a residual polygenic component strongly reduced bias for most of the traits. The optimal percentage of polygenic variance varied across traits. Among the methodologies tested to implement genomic selection in the French Charolais beef cattle population, the most accurate and less biased methodology was to analyze DEBV under a BayesC strategy and a residual polygenic component approach. With this approach, a 50K SNP panel performed as well as a 777K panel.

Background In beef cattle, maternal care is critical for calf survival and growth. Our objective was to evaluate the major sources of additive genetic variation in maternal behavior and suckling performance in two genetically close beef breeds. Methods Maternal performance was assessed based on maternal behavior (MB), milk yield (MY) and udder swelling score (US) of 1236 Blonde d’Aquitaine cows and 1048 Limousin cows. MB was scored just after calving to describe the intensity of the dam’s protective behavior towards her calf. Most of the cows were genotyped using the low-density chip EuroG10K BeadChip, and imputed to the high-density 770K panel within breed. Genetic parameters for each trait were estimated for each breed under a multi-trait best linear unbiased prediction animal model. Genomic analysis was performed for each breed using the high-density genotypes and a Bayesian variable selection method. Results Heritabilities were low for MB (0.11–0.13), intermediate for MY (0.33–0.45) and high for US (0.47–0.64). Genetic correlations between the traits ranged from 0.31 to 0.58 and 0.72 to 0.99 for the Blonde d’Aquitaine and Limousin breeds, respectively. Two quantitative trait loci (QTL) were detected for MB in Blonde d’Aquitaine with NPY1R and ADRA2A as candidate causative genes. Thirty to 56 QTL were detected for MY and US in both breeds and 12 candidate genes were identified as having a role in the genetic variation of suckling performance. Since very few pleiotropic QTL were detected, there was little biological explanation for the moderate (0.57) to very high (0.99) genetic correlations estimated between MY and US in the Blonde d’Aquitaine and Limousin cows, respectively. In Blonde d’Aquitaine, the correlation was largely due to the pleiotropic QTL detected in the region upstream of the CG gene, while in Limousin, this region was only identified for US, thus attesting the difference in genetic architecture between the breeds. Conclusions Our findings question the assumption that two populations that have close genetic links share many QTL. Nevertheless, we identified four candidate genes that may explain a substantial amount of the genetic variation in suckling performance of these two breeds. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0223-z) contains supplementary material, which is available to authorized users.

Background In recent years, several bovine genome sequencing projects were carried out with the aim of developing genomic tools to improve dairy and beef production efficiency and sustainability. Results In this study, we describe the first French cattle genome variation dataset obtained by sequencing 274 whole genomes representing several major dairy and beef breeds. This dataset contains over 28 million single nucleotide polymorphisms (SNPs) and small insertions and deletions. Comparisons between sequencing results and SNP array genotypes revealed a very high genotype concordance rate, which indicates the good quality of our data. Conclusions To our knowledge, this is the first large-scale catalog of small genomic variations in French dairy and beef cattle. This resource will contribute to the study of gene functions and population structure and also help to improve traits through genotype-guided selection. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0268-z) contains supplementary material, which is available to authorized users.

Le néologisme « agro-écologie » est apparu dans la littérature dans les années 30, d’abord pour désigner une discipline scientifique au carrefour de l’écologie et de l’agronomie, puis à partir des années 70 un ensemble de pratiques et un mouvement social en réaction à l’intensification de l’agriculture (Wezel et al., 2009). Francis et al. (2003) ont encore élargi cette définition en considérant l’ensemble des dimensions environnementales et socio-économiques à l’échelle des filières. Dans son acception scientifique, qui est l’angle choisi dans cette étude, l’agro-écologie se définit comme l’application des concepts et principes de l’écologie à la conception et à la gestion d’agroécosystèmes durables. La question du bouclage des cycles pour limiter les intrants et les pollutions par une meilleure réutilisation des sorties des différents ateliers est au coeur de ces problématiques. On vise également un recours accru à des régulations biologiques et écologiques que l’on cherche à piloter au mieux des attendus de l’élevage. Ceci implique de prendre en compte la diversité biologique à tous ses niveaux d'organisation, de comprendre et d’utiliser les capacités adaptatives des animaux et les interactions biotiques dans les agroécosystèmes, en considérant les systèmes de culture ou les systèmes d'élevage comme des écosystèmes particuliers (Dumont et al. 2013). Ainsi, l’agro-écologie s’inscrit-elle dans un mouvement de fond autour du « Comment produire autrement ? » dans des systèmes de production dont on cherche à concilier les performances techniques, environnementales, économiques et sociales. Lancé le 18 décembre 2012, le Projet Agro-Ecologique pour la France (PAEF) vise à conjuguer l’ensemble de ces performances tant au niveau de l'exploitation qu’au niveau collectif (territoires, filières, etc.). Il s’agit d’engager une évolution des systèmes de production, qui remette l’agronomie au coeur des pratiques, pour combiner ces différentes performances. La Loi n° 2014-1170 du 13 octobre 2014 dite Loi d'avenir pour l'agriculture, l'alimentation et la forêt en donne la définition suivante : « Les politiques publiques visent à promouvoir et à pérenniser les systèmes de production agro-écologiques, dont le mode de production biologique, qui combinent performance économique, sociale, notamment au travers d’un haut niveau de protection sociale, environnementale et sanitaire. Ces systèmes privilégient l'autonomie des exploitations agricoles et l'amélioration de leur compétitivité, en maintenant ou en augmentant la rentabilité économique, en améliorant la valeur ajoutée des productions et en réduisant la consommation d'énergie, d'eau, d'engrais, de produits phytopharmaceutiques et de médicaments vétérinaires, en particulier les antibiotiques. Ils sont fondés sur les interactions biologiques et l'utilisation des services écosystémiques et des potentiels offerts par les ressources naturelles, en particulier les ressources en eau, la biodiversité, la photosynthèse, les sols et l'air, en maintenant leur capacité de renouvellement du point de vue qualitatif et quantitatif. Ils contribuent à l'atténuation et à l'adaptation aux effets du changement climatique.

The Creole goat is a local breed used for meat production in Guadeloupe (French West Indies). As in other tropical countries, improvement of parasite resistance is needed. In this study, we compared predicted selection responses for alternative breeding programs with or without parasites resistance and resilience traits. The overall breeding goal included traits for production, reproduction and parasite resilience and resistance to ensure a balanced selection outcome. The production traits were body weight (BW) and dressing percentage (DP). The reproduction trait was fertility (FER), which was the number of doe kiddings per mating. The resistance trait was worm fecal egg count (FEC), which is a measurement of the number of gastro-intestinal parasite eggs found in the feces. The resilience trait was the packed cell volume (PCV), which is a measurement of the volume of red blood cells in the blood. DP, BW and FEC were measured at 11 mo of age, which is the mating or selling age. FER and PCV were measured on females at each kidding period. The breeding program accounting for the overall breeding goal and a selection index including all traits gave annual selection responses of 800 g for BW, 3.75% for FER, 0.08% for DP, -0.005 ln(eggs/g) for FEC, and 0.28% for PCV. The expected selection responses for BW and DP in this breeding program were reduced by 2% and 6%, respectively, compared to a breeding program not accounting for FEC and PCV. The overall breeding program, proposed for the Creole breed, offers the best breeding strategy in terms of expected selection responses, making it possible to improve all traits together. It offers a good balance between production and adaptation traits and may present some interest for the selection of other goat breeds in the tropics.

Le microbiote digestif est un symbionte dont on a démontré l’implication essentielle dans la régulation de la physiologie de l’hôte qui l’héberge pour la digestion, le métabolisme et l’immunité. Ce microbiote a en particulier un rôle majeur, et jusqu’ici sous-estimé, sur la santé de son hôte et participe à son adaptabilité à des environnements changeants. Les technologies de séquençage à haut débit devraient faciliter l’inclusion de cet écosystème complexe dans le phénotypage des animaux d’élevage. Il faut pour ce faire prendre en compte sa composition, sa diversité, sa stabilité au cours du temps et ses variations selon différents facteurs comme le génotype de l’hôte, l’alimentation et les conditions d’élevage. Ces connaissances devraient permettre de définir les propriétés d’un microbiote digestif « normal » associé à une bonne santé, d’en préciser les dysfonctionnements et de comprendre leurs répercussions sur l’état de santé de l’animal. Cela conduira à identifier des marqueurs de diagnostic et de pronostic de ces dysfonctionnements, fournissant des moyens de contrôle nouveaux de la santé en élevage, appréhendée en terme d’optimisation des rendements de production, de résistance aux maladies et de maintien du bien-être. A terme, le contrôle du microbiote digestif par l’alimentation, la génétique ou les pratiques d’élevage permettra de favoriser l’adaptation des animaux à leur environnement d’élevage en améliorant leur robustesse, de limiter l’utilisation d’antibiotiques et de préserver la sécurité sanitaire des aliments en évitant le développement de bactéries pathogènes pour l’Homme. L’ampleur des données à générer et à analyser pour les différentes espèces d’intérêt reste le défi à relever., The gastrointestinal microbiota is a symbiont deeply involved in the physiology of its host through the regulation of digestion, metabolism and immunity and consequently in the host adaptability to changing environments. In particular, the role of the gastrointestinal microbiota in maintaining host health is important and has been under-estimated until now. It is an inherent component of the animal that should be described when phenotyping animals. The rapid development of high throughput sequencing technologies facilitates the description of this complex ecosystem in livestock species. For each animal species, its description should include the measure of its composition, diversity and stability over time and the measure of its variability according to different factors such as genotype, nutrition and breeding conditions. This knowledge should allow defining the properties of a ‘normal’ gastrointestinal microbiota associated with health, identifying the microbiota dysfunctions and understanding their consequences on host physiology. It will lead to the identification of diagnostic and prognostic markers for gastrointestinal dysbiosis thus furnishing new tools for promoting health. Finally, the manipulation of the gastrointestinal microbiota will constitute a way to better adapt animals to their environment by improving their robustness, limit the use of antibiotics and improve food safety by preventing the development of bacteria threatening human health. Due to the high quantity of data to be acquired and analyzed, achieving these objectives for each species of interest remains a challenge.

Dans un contexte de demande croissante en protéines animales, l’élevage de précision et la sélection animale doivent viser à accroître la compétitivité des systèmes de production et à réduire leur impact environnemental. Les animaux doivent être plus efficaces pour transformer leur ration (réduire l’ingéré tout en maintenant une production stable), en acceptant des ressources alimentaires variées (protéines ou fibres) et en minimisant leurs rejets (totaux ou leurs composantes N et P, GES). Toutefois, l’efficacité alimentaire est un phénotype complexe qui doit être phénotypé finement pour comprendre les processus biologiques sous-jacents avant d’identifier les leviers d’action pour l’améliorer (efficacité digestive vs métabolique ; efficacité énergétique vs protéique). Ce phénotypage doit être réalisé en conditions variées tant sur le plan du génotype, du niveau alimentaire que de la composition de la ration (teneur en fibres, en acides aminés). Il s’agit aussi d’envisager une approche prédictive de l’ingéré et de l’efficacité alimentaire basée sur des paramètres peu coûteux et faciles à mesurer (croissance, bio-marqueurs…) dans le cadre d’un élevage de précision ou de programmes de sélection génétique, car le phénotypage à large échelle de l’ingéré reste coûteux et parfois difficile à mettre en oeuvre. Pour évaluer l’efficience globale des systèmes de productions animales, il s’agit de considérer les réponses des animaux pour des génotypes et des régimes alimentaires contrastés en termes d’efficacité alimentaire et de rejets en quantité et en qualité (GES, N, P), mais aussi de robustesse des animaux (comportement alimentaire, activité générale, santé)., In a context of an increasing demand for animal proteins, precision farming and animal breeding must be goal-oriented to improve production competitiveness and to reduce environmental impact. Animals have to be more efficient in transforming their diet (reducing their feed intake while maintaining production performance), accepting various feeds (protein and fibers) and reducing their wastes (total, N, P, GHG…). However, feed efficiency is a complex phenotype that needs to be deeply phenotyped. This will allow unravelling the biological processes to identify ways to improve feed efficiency (digestive versus metabolic efficiency; energetic versus protein efficiency) according to the genotype, the feed level and the diet composition (fibre content, AA content). Predictive biology of feed intake and feed efficiency is also needed using cost-effective and easy to record traits (growth, biomarkers…) for precision farming and for selective breeding, because large-scale phenotyping for feed intake remains costly. To assess the efficiency of livestock production systems, there is a need to reconsider animal responses according to contrasted genotypes and feeds not only for feed efficiency and wastes in quantity and quality (GHG, N, P), but also for robustness of animals (feeding behavior, general activity, health).

The major challenge of animal phenotyping is to acquire systemic knowledge of farm animal robustness. This will allow responding to a double objective in terms of exploiting the variability of animal aptitudes: selection for major objectives in order to improve efficiency and robustness of genotypes and precision farming that exploits individual variability of animals for an improved resilience on the herd level or to improve the management for a specific genotype. Two major challenges of our knowledge exist which are interdependent and common to all animal breeding, and which must be met in order to improve animal robustness. The first is understanding andpredicting the compromise amongst vital functions. By this we mean understanding how priority changes are made under specific limited resources. The second is understanding and exploiting the temporal aspects of robustness during an animal’s life in order to allow the animal to maintain its level of performance under different environments without compromising its reproduction, health or welfare. Attaining these objectives requires accomplishing the following: i) defining complex phenotypes through the integration of data obtained on the molecular or tissular scales of an animal or of one of its functions; ii) developing high-throughput technologiesto characterize at a lower cost and the most precisely as possible the largest number of animals; iii) developing important data bases and reliable methods and computing tools necessary for the treatment of information for modeling and predictive biology., L’enjeu majeur du phénotypage animal est d’acquérir une connaissance systémique de la robustesse des animaux d’élevage pour répondre au mieux à une double finalité en termes d’exploitation de la variabilité des aptitudes animales : une sélection éclairée vers des objectifs majeurs pour améliorer l’efficience et la robustesse des génotypes, et un élevage de précision exploitant la variabilité individuelle des animaux pour gagner en résilience à l’échelle du troupeau, ou pour améliorer la conduite à génotype donné. Deux défis majeurs de connaissance, interdépendants et communs à toutes les filières animales, sont à relever pour améliorer la robustesse des animaux : i) comprendre et prédire les compromis entre fonctions vitales, c’est-à-dire les changements de priorités dans l’allocation de ressources limitées ; ii) comprendre et exploiter les aspects temporels de la robustesse au cours de la vie de l’animal pour lui permettre de maintenir son niveau de production dans une large gamme d’environnements sans pour autant compromettre sa reproduction, sa santé et son bien-être. Atteindre ces objectifs nécessite de lever les verrous techniques suivants : i) définir des phénotypes complexes à partir de l’intégration de données obtenues à différentes échelles moléculaires, tissulaires, de l’animal ou d’une de ses fonctions ; ii) mettre en oeuvre des technologies à haut débit pour caractériser à moindre coût et le plus précisément possible le plus grand nombre d’animaux ; iii) développer d’importantes bases de données, des méthodes et outils informatiques performants, nécessaires aux traitements des informations à des fins de modélisation et de biologie prédictive.