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Runs of homozygosity (ROH) are continuous homozygous segments that arise through the transmission of haplotypes that are identical by descent (IBD). The length and distribution of ROH segments provide insight into the genetic diversity of populations and are useful to detect selection signatures. Here, we analysed pooled whole-genome sequencing data from 265 Western honey bee colonies from the two subspecies Apis mellifera mellifera and Apis mellifera carnica. Integrating individual ROH patterns and admixture levels in a high-resolution population network visualization allowed us to ascertain major differences between the two subspecies. Within A. m. mellifera, we identified well-defined substructures according to the genetic origin of the colonies and a fair amount of admixed colonies, despite the current applied conservation efforts. In contrast, A. m. carnica colonies were more inbred and could not be differentiated according to the geographical origin. We identified 29 coding genes in overlapping ROH segments within the two subspecies. Genes embedded in A. m. carnica specific homozygosity islands suggested a strong selection for production and behavioural traits, whilst the identified cuticula protein-coding genes (CPR3 and CPR4) were associated with their breed-specific stripe pattern. Local adaption of the two subspecies could be confirmed by the identification of two genes involved in the response to ultraviolet (UV) light. We demonstrated that colony genotypes derived from pooled honey bee workers are reliable to unravel the population dynamics in A. mellifera and provide fundamental information to conserve native honey bees.

International audience; The identification of quantitative trait loci (QTL) through genome-wide association studies (GWAS) is a powerful method for unravelling the genetic background of selected traits and improving early-stage predictions. In honey bees (Apis mellifera), past genetic analyses have particularly focused on individual queens and workers. In this study, we used pooled wholegenome sequences to ascertain the genetic variation of the entire colony. In total, we sampled 216 Apis mellifera mellifera and 28 Apis mellifera carnica colonies. Different experts subjectively assessed the gentleness and calmness of the colonies using a standardised protocol. Conducting a GWAS for calmness on 211 purebred A. m. mellifera colonies, we identified three QTL, on chromosomes 8, 6, and 12. The two first QTL correspond to LOC409692 gene, coding for a disintegrin and metalloproteinase domain-containing protein 10, and to Abscam gene, coding for a Dscam family member Abscam protein, respectively. The last gene has been reported to be involved in the domestication of A. mellifera. The third QTL is located 13 kb upstream of LOC102655631, coding for a trehalose transporter. For gentleness, two QTL were identified on chromosomes 4 and 3. They are located within gene LOC413669, coding for a lap4 protein, and gene LOC413416, coding for a bicaudal C homolog 1-B protein, respectively. The identified positional candidate genes of both traits mainly affect the olfaction and nervous system of honey bees. Further research is needed to confirm the results and to better understand the genetic and phenotypic basis of calmness and gentleness.

International audience; Varroa destructor is one of the main pests of the honeybee Apis mellifera, causing severe colony losses worldwide. Chemical treatments are available to reduce infestation, but they are currently experiencing significant limits. Thus, breeding and selection of bees naturally resistant to Varroa appears as a very promising solution.Several selection criteria for Varroa resistance have been developed and validated by research laboratories. The first objective of this project was therefore to provide technical references to professional beekeepers in order to implement these measures in a relevant manner under production conditions. However, until now, there is no method to estimate the resistance potential of a colony with a simple, reliable, rapid way and without a high rate of parasitism. MOSAR therefore also had the objective of developing new tools for technicians and beekeepers: the development of a simple phenotyping system of VSH behavior ans the search for genetic markers of resistance.This work began with the evaluation under conventional beekeeping production conditions of methods for evaluating the resistance of colonies to Varroa mite developed and validated under experimental conditions. The monitoring and phenotyping of 120 colonies throughout the project identified all the advantages and disadvantages of each method for large-scale implementation. The expertise acquired also made it possible to optimize these methods for use by beekeepers while maintaining the reliability essential for use in breeding. Technical sheets presenting these different criteria, detailing the equipment and skills required, as well as the different stages and critical points were widely distributed.At the same time, the development of a method using chemical compounds to estimate the resistance of a colony to Varroa has made significant progress. Likewise, the search for genetic markers of Varroa resistance relied on the MOSAR project to progress on several methodological aspects.This project has already paved the way for the integration of resistance criteria to Varroa in breeding programs used in beekeeping and the additional work in progress should amplify this impact over time.; L'acarien Varroa destructor est l'un des principaux parasites de l'abeille domestique Apis mellifera, causant d'importantes pertes de colonies à l'échelle mondiale. Des traitements chimiques sont disponibles afin de freiner la progression de l'infestation mais ils connaissent actuellement des limites significatives. Ainsi, la sélection et l'élevage d'abeilles naturellement résistantes au Varroa apparaît comme une solution très prometteuse. Plusieurs critères de sélection pour la résistance au Varroa ont été développés par des laboratoires de recherche. Le premier objectif de ce projet était donc de fournir à la filière des références techniques afin de mettre en oeuvre ces mesures de manière pertinente dans des conditions de production. Pour autant, actuellement, il n’existe pas de méthode pour estimer le potentiel de résistance d’une colonie de manière simple, fiable, rapide et sans un taux de parasitisme élevé. MOSAR avait donc également comme objectif d’avancer dans la mise au point de nouveaux outils à destination des techniciens et des apiculteurs : le développement d’un système de phénotypage simple du comportement VSH et la recherche de marqueurs génétiques de résistance.Ce travail a démarré par l’évaluation dans des conditions de production apicole classiques des méthodes permettant d’évaluer la résistance des colonies au Varroa, mises au point et validées dans des conditions expérimentales. Le suivi et le phénotypage de 120 colonies sur l’ensemble du projet a permis d’identifier les avantages et les inconvénients de chaque méthode pour une mise en oeuvre à grande échelle. L’expertise acquise a également permis d’optimiser ces méthodes en vue d’une utilisation par les apiculteurs tout en conservant la fiabilité indispensable en vue d’une utilisation en sélection. Des fiches techniques présentant ces différents critères en détaillant le matériel et les compétences nécessaires ainsi que les différentes étapes et les points critiques ont été diffusées largement.En parallèle, la mise au point d’une méthode utilisant des composés chimiques pour estimer la résistance d’une colonie au Varroa a nettement progressé. De même, la recherche de marqueurs génétiques de la résistance à Varroa s’est appuyée sur le projet MOSAR pour progresser sur plusieurs aspects méthodologiques.Ce projet a déjà ouvert la voie à l’intégration de critères de résistance au Varroa dans les programmes de sélection utilisés en apiculture et les travaux complémentaires en cours devraient amplifier cet impact à terme.

Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

International audience

International audience

International audience

International audience

Additional file 1. Comparison of G-matrices. Comparison of different methods to calculate estimated relationships between individuals and their impact on the loss of genetic diversity.

International audience

Whole genome sequencing technologies are rapidly developing. In some ways, the speed of this development has outstripped our capacity to use this type of data in selection strategies, especially in livestock diversity conservation. In this study, relationship matrices were computed for 118 Holstein bulls, key ancestors of the current population, from three different types of data: pedigree records, 50K SNP chips and whole genome sequences, considering three different scenarios (with, without or only using rare alleles). Estimates from different data were highly correlated. Rare alleles had a significant impact on relationship estimates, mostly when whole genome sequence data were used. Hence sequence data, and information from rare alleles, are potentially of use for improving relationship computation. Estimation of relationships made with this type of data may result in different individual optimal contributions and influence selection strategies and conservation decisions of livestock species

Genomic selection is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, genomic selection allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for genomic selection our findings are of major economic interest. This study aims to answer the following questions: How would decisions on the reference population affect the breeding population? How to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation and optimal contribution strategies. Optimal contribution maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over ten generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

International audience; Chicken is by far the most widely used bird in animal breeding and is also a model organism that has long been used for scientific observations, typically in embryology. This position at the crossroads of the breeding industry and of basic research is part of the reasons for which chicken was amongst the first vertebrates sequenced at the dawn of the era of large genomes analyses, with a draft genome published only three years after the human one. The other reasons were that it was the only representative of the bird lineage at the time having available genomics resources such as genetic maps or large-insert DNA libraries. Just like a lot of the understanding of bird biology stemmed from using chicken as model, new insights into the specificities of bird genomes, such as the karyotype organization into macrochromosomes and microchromosomes, could be investigated in great detail.Since then, largely due to the advent of the second-generation parallel sequencing and third-generation long-read methods, more bird species were sequenced and at the time of writing, 163 assemblies are available with various levels of quality in the NCBI genome database. These include other birds bred as agricultural species, such as turkey, duck, quail or guinea fowl.In animal breeding, the genomic information is mainly used for QTL detection, marker-assisted or genomic selection and for a deeper understanding of biological mechanisms. Also, much had been done in poultry, to take advantage of the large collection of phenotypic variants present either in commercial or in fancy breeds. For instance, a number of genes causing coloration and ornamental variation were identified first in chicken, thanks for all the genomics tools available, and later on in other species, usually and in the absence of a reference genome, by directly testing the candidate genes found in chicken.With the advent of high quality and low-cost sequencing, the number of available genomes will expand rapidly and their quality will increase. However, does this mean model organisms will not be needed anymore?

Background Whole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare (minor allele frequency