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Additional file 1: Text S1. Sampling. Details on the sampling procedure. Text S2. SNP calling. Details on the SNP calling procedure. Text S3. Local score approach. Details on the local score approach. Text S4. Identification of causal variants under selection. Details on the approach used to detect causal variants, and on the results obtained with this approach. Text S5. Private alleles. Details on the results obtained regarding private alleles. Text S6. Biological functions under selection. Details on the results obtained regarding the biological functions under selection. Text S7. Insight from a previous gene expression study. Details on the approach used to explore gene expression in candidate regions, and on the results obtained with this approach. Text S8. Molecular mechanisms mediating selection. Discussion on the most likely molecular mechanism (regulatory or protein-coding) mediating selection in each the 10 div regions. Text S9. Candidate genes under selection. Details on several interesting candidate genes under selection. Figure S6. Distribution of p values obtained by the time-LWD and time-LWS tests. High-quality SNPs (HQSNP set) with a MAF greater than 0.1 were considered. Figure S7. Correlation of expression levels in the muscle measured in [40] for 76 probes corresponding to 48 genes found under selection in our study. Correlation between two probes was computed based on normalized expression for Large White sire (n = 10) and dam lines (n = 41) at these two probes. The ordering of probes on the graph follows from a hierarchical clustering of the correlation matrix. Only significant correlations (p value < 0.05) are shown. Figure S8. Boxplot of muscle expression levels in LWD and LWS for the 12 genes found differentially to be expressed between the two lines. All genes are differentially regulated between the two lines with a p value < 0.05. None of these genes have an adjusted p value < 0.05. Additional File 1 cites the following references: 2, 24, 25, 40, 48–61.

Additional file 2: Figure S1. MDS analysis of the genetic diversity observed in French LW pigs. High-quality SNPs (HQSNP set) with a MAF greater than 0.1 were considered. Figure S2. Summary of the genome-wide scan for recent selection in French LW pigs, chromosomes 1 to 9. Each panel of the figure shows the results of the three tests for selection for a different autosome. P values of the time-LWD (blue), time-LWS (green) and hapFLK (red) tests are plotted in log10 scale (y axis) according to genomic position (x axis). Opposite signs are used for time-LWD and time-LWS on the one hand and hapFLK on the other hand, i.e. low p values correspond to highly negative points for time-LWD and time-LWS and to highly positive points for hapFLK. Significant p values are shown in darker color for the three tests. For time-LWD and time-LWS, SNPs ranked as significant by the local score approach (see “Methods” for further details) may be hidden by non significant SNPs with higher individual p values, so significant regions for these tests were also highlighted by small triangle marks below the p value plot. Figure S3. Summary of the genome-wide scan for recent selection in French LW pigs, chromosomes 10 to 18. See Figure S2 for details. Figure S4. Top 20 significant GO terms for Biological Processes and association between these terms and groups of selection signature categories. See Fig. 5 for details. Figure S5. Top 20 significant KEGG terms and association between these terms and groups of selection signature categories. See Fig. 5 for details. Table S1. Determination of candidate genes in several selection signatures. This table reports all the genes that (i) can be considered as good candidate genes within their region (based on criteria described below) and (ii) are associated to one of the enriched biological functions listed in Table 3 (or more generally whose biological function is consistent with selection objectives in LW). For a given region of interest, genes that represent even better candidates than those selected based on condition (ii) are also included. Column ‘Nb. genes’ gives the number of genes in a region, and column ‘Gene ID’ lists the candidate genes. Column ‘Local peak’ indicates whether the most relevant test statistic for the region shows a local peak within or close to the gene (see Additional file 4 for summary plots of the four test statistics in all candidate regions); this column takes value NA when no or a single gene is observed in a region. Column ‘Diff. expr.’ indicates whether the gene was found differentially expressed between LWS and LWD in [40]; this column takes value ‘no data’ for genes that were not included in this study. Column ‘Causal variant’ indicates whether a causal variant under selection is known for this gene, or if a small number of potential candidate variants have been identified in our study (these candidates may be functional or not, see the text for more details). Based on all these observations, column ‘Overall evidence’ quantifies the evidence for each candidate gene to be the one under selection in a given region.

Additional file 1. Additional information related to the methodology (the origin of the biological samples, the experimental design for disease induction, the bioinformatic pipelines).

In times of ever decreasing amount of in-situ data for hydrology, satellite altimetry has become key to provide global and continuous datasets of water surface height. Indeed, studying lakes, reservoirs and rivers water level at global scale is of prime importance for the hydrology community to assess the Earth’s global resources of fresh water. Much progress has been made in the altimeters’ capability to acquire quality measurements over inland waters. In particular, the Open-Loop Tracking Command (OLTC) now represents an essential feature of the tracking function. This tracking mode’s efficiency has been proven on past missions and it is now stated as operational mode for current Sentinel-3 and Sentinel-6 missions. It has benefited from iterative improvements brought to onboard tables contents repeatedly since 2017. In 2022, new updates will be performed on the onboard OLTC tables of the Sentinel-3A and Sentinel-3B missions, as well as Sentinel-6A and Jason-3 following their successful Tandem Phase. The number of hydrological targets used to define the tracking command currently reaches an unprecedented number of targets of almost 100,000 for each Sentinel-3 and about 30,000 for Sentinel-6A. We expect to define a similar number of targets in the interleaved orbit of Jason-3, previously flown by Jason-2, although mostly in Closed-Loop Mode. These major improvements over the last few years have been made possible by the analysis and merging of the most up-to-date digital elevation models (SRTM, MERIT and ALOS/PalSAR) and water bodies databases (HydroLakes, GRaND v1.3, SWBD, GSW, SWORD). In addition, special effort is put into introducing the most recent reservoir databases. This methodology ensures coherency and consistent standards between all nadir altimetry missions and types of hydrological targets. Finally, additional efforts have been carried out to define a relevant tracking command outside of hydrological areas, in order to keep track of the continental surface and enabling potential other land applications, while optimizing the OLTC onboard memory. The OLTC function of nadir altimeters constitutes a great asset for building a valuable and continuous record of the water surface height of worldwide lakes, rivers, reservoirs, wetlands and even a few continental glaciers. This work is essential at institutional and scientific levels, to make the most of current altimeters coverage over land and to prepare for the upcoming calibration and validation of the Surface Water and Ocean Topography (SWOT) mission. In this context, we will show an overview of OLTC achievements and perspectives for future altimetry missions.

Additional file 3. Supplementary results on the phenotypes (Fertilisation success and survival rate of oysters) and on omics data (Table 2 = genetic, Table 3 = transcriptomic, Table 4 = epigenomic, Figs. 1 and 2 = 16S barcoding).

International audience

International audience

International audience

International audience; Gene banks, framed within the efforts for conserving animal genetic resources to ensure the adaptability of livestock production systems to population growth, income, and climate change challenges, have emerged as invaluable resources for biodiversity and scientific research. Allele frequency trajectories over the few last generations contain rich information about the selection history of populations, which cannot be obtained from classical selection scan approaches based on present time data only. Here we apply a new statistical approach taking advantage of genomic time series and a state of the art statistic (nSL) based on present time data to disentangle both old and recent signatures of selection in the Asturiana de los Valles cattle breed. This local Spanish originally multipurpose breed native to Asturias has been selected for beef production over the last few generations. With the use of SNP chip and whole-genome sequencing (WGS) data, we detect candidate regions under selection reflecting the effort of breeders to produce economically valuable beef individuals, e.g., by improving carcass and meat traits with genes such as MSTN , FLRT2 , CRABP2 , ZNF215 , RBPMS2 , OAZ2 , or ZNF609 , while maintaining the ability to thrive under a semi-intensive production system, with the selection of immune ( GIMAP7 , GIMAP4 , GIMAP8 , and TICAM1 ) or olfactory receptor ( OR2D2 , OR2D3 , OR10A4 , and 0R6A2 ) genes. This kind of information will allow us to take advantage of the invaluable resources provided by gene bank collections from local less competitive breeds, enabling the livestock industry to exploit the different mechanisms fine-tuned by natural and human-driven selection on different populations to improve productivity.

International audience; Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta-with-Spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. Based on this model, we show that the sampling period considered strongly determines the selection intensities that can be detected or properly estimated.

International audience

International audience; Past events of positive selection leave characteristic signatures in the genetic diversity of a population, which can be detected by genome-wide scans based on present time molecular data. However, determining the adaptive trait or the onset and intensity of selection at a given locus is often difficult from such data. By providing direct access to the temporal evolution of allele frequencies, the analysis of genomic data extracted from gene banks might significantly improve our understanding of selection history in livestock species. The aim of this study is to evaluate whether the analysis of genomic samples collected at different times in the recent past allows (i) detecting recent selection events and (ii) annotating selection signatures found by classical approaches based on present time data only. To answer these questions, we considered the case study of the Spanish bovine breed Asturiana de los Valles (RAV), for which genotyping data was available for 137 animals with birth dates between 1980 and 2010. Fifteen additional RAV animals born in 2008–2013 were sequenced at ~8X coverage. These data were used to detect historical selection signatures in RAV using a classical statistic (nSL) based on a single sampling time. A new statistical approach allowing detection of selection from genomic time series was applied to the combined data set including 9 distinct generations. The time series approach combined with a statistical method allowing to detect clusters of small p-values pointed out several candidate regions with a clear shift in allele frequencies over the few last generations. The time series and nSL approaches detected 13 candidate regions under selection in RAV including genes related to carcass and meat traits (such as MSTN, RBPMS2 or OAZ2), immunity (GIMAP7, GIMAP4, GIMAP8), olfactory receptors (OR2D2, OR2D3, OR10A4, 0R6A2) and milk traits (ARFIP1). Thus, the combined time series and nSL approach are complementary and should be extended to other populations where temporal data can be extracted from gene banks. These results outline that gene banks represent a great resource for the understanding of breed history and the detection of relevant functional genes and variants.

National audience

Ce volume regroupe les textes issus du programme Casdar "Innovation et Partenariat" et "Recherche finalisée et innovation" de 2013. Le colloque de restitution s’est déroulé le 6 février 2019 sous l’égide du GIS Relance Agronomique; National audience; Even though meat is increasingly consumed in elaborated form, consumers are sensitive to naturality of the products. This trend is largely taken into account by agribusiness company seeking to reduce additives (flavor enhancers or texturizing agents) or preservatives (in particular salt). This shows the importance of upstream control of quality of the raw material for elaborated products. This issue particularly affects chicken meat, mostly consumed in cut or processed but whose technological quality is highly variable. The objective of the CASDAR OPTIVIANDE project was to develop new phenotyping tools and biological or genetic markers that could be used for selection or evaluation of the impact of breeding factors. The approaches implemented concerned the use of near-infrared spectrometry (NIRS) as well as high-throughput biological and genomic analyzes. The study was based on an original animal model composed of two chicken lines selected in a divergent manner on the ultimate pH of the filet and whose technological and sensory characteristics of meat were very different. Near-infrared spectrometry is fast to implement and makes it possible to predict several technological quality criteria such as ultimate pH, water loss during storage and hardness after cooking. Transcriptomic and metabolomics analyzes made it possible to develop first prediction models based on a limited number of metabolites (blood and muscle) or muscle transcripts. At the genetic level, analyzes made possible to identify the main regions controlling the ultimate pH of the filet and revealed several genes of interest. In conclusion, the project led to significant advances in understanding the genetic and physiological mechanisms involved in the establishment of quality defects in chicken. It opens perspectives of application thanks to the development of prediction tools for selection whose genericity will have to be validated on other populations and in conditions of production

Even though meat is increasingly consumed in elaborated form, consumers are sensitive to naturality of the products. This trend is largely taken into account by agribusiness company seeking to reduce additives (flavor enhancers or texturizing agents) or preservatives (in particular salt). This shows the importance of upstream control of quality of the raw material for elaborated products. This issue particularly affects chicken meat, mostly consumed in cut or processed but whose technological quality is highly variable. The objective of the CASDAR OPTIVIANDE project was to develop new phenotyping tools and biological or genetic markers that could be used for selection or evaluation of the impact of breeding factors. The approaches implemented concerned the use of near-infrared spectrometry (NIRS) as well as high-throughput biological and genomic analyzes. The study was based on an original animal model composed of two chicken lines selected in a divergent manner on the ultimate pH of the filet and whose technological and sensory characteristics of meat were very different. Near-infrared spectrometry is fast to implement and makes it possible to predict several technological quality criteria such as ultimate pH, water loss during storage and hardness after cooking. Transcriptomic and metabolomics analyzes made it possible to develop first prediction models based on a limited number of metabolites (blood and muscle) or muscle transcripts. At the genetic level, analyzes made possible to identify the main regions controlling the ultimate pH of the filet and revealed several genes of interest. In conclusion, the project led to significant advances in understanding the genetic and physiological mechanisms involved in the establishment of quality defects in chicken. It opens perspectives of application thanks to the development of prediction tools for selection whose genericity will have to be validated on other populations and in conditions of production

International audience; The helmeted guinea fowl Numida meleagris belongs to the order Galliformes. Its natural range includes a large part of sub-Saharan Africa, from Senegal to Eritrea and from Chad to South Africa. Archaeozoological and artistic evidence suggest domestication of this species may have occurred about 2,000 years BP in Mali and Sudan primarily as a food resource, although villagers also benefit from its capacity to give loud alarm calls in case of danger, of its ability to consume parasites such as ticks and to hunt snakes, thus suggesting its domestication may have resulted from a commensal association process. Today, it is still farmed in Africa, mainly as a traditional village poultry, and is also bred more intensively in other countries, mainly France and Italy. The lack of available molecular genetic markers has limited the genetic studies conducted to date on guinea fowl. We present here a first-generation whole-genome sequence draft assembly used as a reference for a study by a Pool-seq approach of wild and domestic populations from Europe and Africa. We show that the domestic populations share a higher genetic similarity between each other than they do to wild populations living in the same geographical area. Several genomic regions showing selection signatures putatively related to domestication or importation to Europe were detected, containing candidate genes, most notably EDNRB2, possibly explaining losses in plumage coloration phenotypes in domesticated populations.