The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by F_ST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features—the use of haplotype information and of the hierarchical structure of populations—significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection.

The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the ones previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments.

Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans increases detection power, but haplotype‐based methods require individual genotypes and are not applicable on pool‐sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium in genome scans for selection, cumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal. Using computer simulations, we demonstrate that this approach detects selection with higher power than several state‐of‐the‐art single‐marker, windowing or haplotype‐based approaches. We illustrate this on two benchmark data sets including individual genotypes, for which we obtain similar results with the local score and one haplotype‐based approach. Finally, we apply the local score approach to Pool‐Seq data obtained from a divergent selection experiment on behaviour in quail and obtain precise and biologically coherent selection signals: while competing methods fail to highlight any clear selection signature, our method detects several regions involving genes known to act on social responsiveness or autistic traits. Although we focus here on the detection of positive selection from multiple population data, the local score approach is general and can be applied to other genome scans for selection or other genomewide analyses such as GWAS.

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices while avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, and data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to a well-known learning analytics problem: student dropout prediction. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding the concentration of all the data in a single place for training the models.

Behavioral traits such as sociability, emotional reactivity and aggressiveness are major factors in animal adaptation to breeding conditions. In order to investigate the genetic control of these traits as well as their relationships with production traits, a study was undertaken on a large second generation cross (F2) between two lines of Japanese Quail divergently selected on their social reinstatement behavior. All the birds were measured for several social behaviors (social reinstatement, response to social isolation, sexual motivation, aggression), behaviors measuring the emotional reactivity of the birds (reaction to an unknown object, tonic immobility reaction), and production traits (body weight and egg production). We report the results of the first genome-wide QTL detection based on a medium density SNP panel obtained from whole genome sequencing of a pool of individuals from each divergent line. A genetic map was constructed using 2145 markers among which 1479 could be positioned on 28 different linkage groups. The sex-averaged linkage map spanned a total of 3057 cM with an average marker spacing of 2.1 cM. With the exception of a few regions, the marker order was the same in Japanese Quail and the chicken, which confirmed a well conserved synteny between the two species. The linkage analyses performed using QTLMAP software revealed a total of 45 QTLs related either to behavioral (23) or production (22) traits. The most numerous QTLs (15) concerned social motivation traits. Interestingly, our results pinpointed putative pleiotropic regions which controlled emotional reactivity and body-weight of birds (on CJA5 and CJA8) or their social motivation and the onset of egg laying (on CJA19). This study identified several QTL regions for social and emotional behaviors in the Quail. Further research will be needed to refine the QTL and confirm or refute the role of candidate genes, which were suggested by bioinformatics analysis. It can be hoped that the identification of genes and polymorphisms related to behavioral traits in the quail will have further applications for other poultry species (especially the chicken) and will contribute to solving animal welfare issues in poultry production.