Non-stationary patterns of isolation-by-distance: inferring measures of local genetic differentiation with Bayesian kriging

Patterns of isolation-by-distance arise when population differentiation increases with increasing geographic distances. Patterns of isolation-by-distance are usually caused by local spatial dispersal, which explains why differences of allele frequencies between populations accumulate with distance. However, spatial variations of demographic parameters such as migration rate or population density can generate non-stationary patterns of isolation-by-distance where the rate at which genetic differentiation accumulates varies across space. To characterize non-stationary patterns of isolation-by-distance, we infer local genetic differentiation based on Bayesian kriging. Local genetic differentiation for a sampled population is defined as the average genetic differentiation between the sampled population and fictive neighboring populations. To avoid defining populations in advance, the method can also be applied at the scale of individuals making it relevant for landscape genetics. Inference of local genetic differentiation relies on a matrix of pairwise similarity or dissimilarity between populations or individuals such as matrices of FST between pairs of populations. Simulation studies show that maps of local genetic differentiation can reveal barriers to gene flow but also other patterns such as continuous variations of gene flow across habitat. The potential of the method is illustrated with 2 data sets: genome-wide SNP data for human Swedish populations and AFLP markers for alpine plant species. The software LocalDiff implementing the method is available at http://membres-timc.imag.fr/Michael.Blum/LocalDiff.htmlComment: In press, Evolution 201

Haplotype based genotyping-by-sequencing in oat genome research

authorsversionPeer reviewe

BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

Aim: Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. Innovation: For this purpose we introduce BHPMF, a ierarchical Bayesian extension of probabilistic matrix actorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation frompoint measurements to larger spatial scales.We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. Main conclusions: Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait-based research in macroecology and functional biogeography

Targeted re-sequencing confirms the importance of chemosensory genes in aphid host race differentiation.

Host-associated races of phytophagous insects provide a model for understanding how adaptation to a new environment can lead to reproductive isolation and speciation, ultimately enabling us to connect barriers to gene flow to adaptive causes of divergence. The pea aphid (Acyrthosiphon pisum) comprises host-races specialising on legume species, and provides a unique system for examining the early stages of diversification along a gradient of genetic and associated adaptive divergence. As host-choice produces assortative mating, understanding the underlying mechanisms of choice will contribute directly to understanding of speciation. As host-choice in the pea aphid is likely mediated by smell and taste, we use capture sequencing and SNP genotyping to test for the role of chemosensory genes in the divergence between eight host-plant species across the continuum of differentiation and sampled at multiple locations across western Europe. We show high differentiation of chemosensory loci relative to control loci in a broad set of pea aphid races and localities, using a model-free approach based on Principal Component analysis. Olfactory and gustatory receptors form the majority of highly differentiated genes, and include loci that were already identified as outliers in a previous study focusing on the three most closely related host races. Consistent indications that chemosensory genes may be good candidates for local adaptation and barriers to gene flow in the pea aphid open the way to further investigations aiming to understand their impact on gene flow, and to determine their precise functions in response to host plant metabolites. This article is protected by copyright. All rights reserved

Common garden experiments in the genomic era : new perspectives and opportunities

PdV was supported by a doctoral studentship from the French Ministère de la Recherche et de l’Enseignement Supérieur. OEG was supported by the Marine Alliance for Science and Technology for Scotland (MASTS)The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (e.g. genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (e.g. genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.PostprintPeer reviewe

Whole genome SNP-associated signatures of local adaptation in honeybees of the Iberian Peninsula

The availability of powerful high-throughput genomic tools, combined with genome scans, has helped identifying genes and genetic changes responsible for environmental adaptation in many organisms, including the honeybee. Here, we resequenced 87 whole genomes of the honeybee native to Iberia and used conceptually different selection methods (Samβada, LFMM, PCAdapt, iHs) together with in sillico protein modelling to search for selection footprints along environmental gradients. We found 670 outlier SNPs, most of which associated with precipitation, longitude and latitude. Over 88.7% SNPs laid outside exons and there was a significant enrichment in regions adjacent to exons and UTRs. Enrichment was also detected in exonic regions. Furthermore, in silico protein modelling suggests that several non-synonymous SNPs are likely direct targets of selection, as they lead to amino acid replacements in functionally important sites of proteins. We identified genomic signatures of local adaptation in 140 genes, many of which are putatively implicated in fitness-related functions such as reproduction, immunity, olfaction, lipid biosynthesis and circadian clock. Our genome scan suggests that local adaptation in the Iberian honeybee involves variations in regions that might alter patterns of gene expression and in protein-coding genes, which are promising candidates to underpin adaptive change in the honeybee.John C. Patton, Phillip San Miguel, Paul Parker, Rick Westerman, University of Purdue, resequenced the 87 whole genomes of IHBs. Jose Rufino provided computational resources at IPB. Analyses were performed using the computational resources at the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), Uppsala University. DH was supported by a PhD scholarship (SFRH/BD/84195/2012) from the Portuguese Science Foundation (FCT). MAP is a member of and receives support from the COST Action FA1307 (SUPER-B). This work was supported by FCT through the programs COMPETE/QREN/EU (PTDC/BIA-BEC/099640/2008) and the 2013-2014 BiodivERsA/FACCE-JPI (joint call for research proposals, with the national funders FCT, Portugal, CNRS, France, and MEC, Spain) to MAP

Pengembangan Materi Multibahasa untuk Siswa Pesantren

ABSTRAK Tujuan penelitian ini adalah: (1) untuk menginvestigasi implementasi pembelajaran multibahasa di pesantren; (2) untuk mengeksplor kebutuhan siswa, guru, dan stakeholder pesantren akan materi pembelajaran multibahasa; (3) untuk mengembangkan materi pembelajaran multibahasa yang dapat meningkatkan kemampuan berbicara multibahasa; (4) untuk mengukur efektifitas materi pembelajaran multibahasa yang telah dikembangkan. Metode penelitian ini adalah Research and Development (R&D) dengan menggunakan model Borg & Gall. Penelitian ini telah dilaksanakan Pesantren IMMIM, Pesantren Pondok Madinah, dan Pesantren Darul Arqam Muhammadiyah Gombara di kota Makassar yang dipilih secara purposive dan random untuk terlibat dalam penelitian ini sesuai dengan tahapan-tahapan R&D. Instrumen yang digunakan untuk mengumpulkan data adalah observasi, interview, angket untuk need analysis, tes berbicara, dan dokumentasi dari aktifitas belajar-mengajar di pesantren. Data kualitatif dianalisa secara diskriptif melalui tiga tahapan model yaitu, penyajian, reduksi, dan perifikasi. Data yang diperoleh dari angket analisa kebutuhan dianalisa secara diskriptif menggunakan analisa SWOT. Data kuantitatif dari spoken tes dan angket acceptability dan aplikability dari materi yang telah dikembangkan dianalisa menggunakan SPPP versi 17.0. Hasil dari penelitian ini ditemukan: (1) implementsi pembelajaran multibahasa menngunakan eklektik (penggabungan) beberapa pendekatan termasuk immersion, transitional, dual language, dan pullout. Selain itu juga menggunakan empat strategi utama yaitu; komunikasi guru-siswa,hubungan siswa dan siswa lain, rutinitas sehari-hari, dan aktifitas grup bahasa. Sedangkan model pengajarannya menggunakan MTB bersamaan-bertahap; (2) siswa, guru, dan stakeholder butuh mempelajari multibahasa untuk berkomunikasi dengan native speaker, berorientasi masa depan menggunakan bahasa non formal. Komponen prioritas dari pembelajaran multibahasa mencakup kosa kata dan dialog sehari-hari, mereka juga membutuhkan materi multibahasa yang tepat seperti silabus, RPP, dan buku ajar; (3) menyesuaikan materi multibahasa dengan silabus, RPP, dan buku ajar yang telah ada; (4) hasil analisa dari semua pretes dan posttest pada uji coba skala kecil, sedang, dan besar menunjukkan bahwa nilai probability adalah (0.00) lebih kecil dibanding tingkat signifikansi pada t-tabel (0.05). Jadi, peneliti menyimpulkan bahwa pengembangan materi ajar mutibahasa sangat memberikan kontribusi dan efektifitas terhadap pembelajaran multibahasa di pesantren

Bayesian statistics in population genetics : factor model and gaussian processes to study neutral and adaptive genetic variation

Nous présentons dans cette thèse plusieurs travaux de statistiques bayésiennes appliquées à la génétique des populations. La génétique des populations a pour but d'expliquer les variations génétiques au sein d'une espèce, et d'inférer les processus ayant conduits à ces variations. Pour cela, des données génétiques massives sont utilisées et il y a un besoin grandissant de méthodes statistiques pour traiter ces données. Le travail de cette thèse s'inscrit dans cet effort de modélisation statistique pour répondre aux enjeux de la génétique des populations, et de la biologie de l'évolution. Nous nous intéressons tout particulièrement à la détection de traces d'adaptation locale dans les génomes, et à l'inférence des variations spatiales non stationnaires.Un modèle d'analyse factorielle bayésien est proposé pour détecter les traces d'adaptation locale. Nous comparons notre approche aux méthodes existantes, et démontrons qu'elle permet d'obtenir un plus faible taux de fausses découvertes. Nous présentons également un modèle bayésien basé sur des processus gaussiens pour caractériser les variations génétiques spatiales dans l'aire de répartition d'une espèce. Les performances de ces méthodes sont démontrées sur différents exemples issus de simulations ou de données. Plusieurs logiciels open source qui implémentent ces méthodes ont été développés pendant la thèse.In this thesis we present several works related to Bayesian statistics in population genetics. Population genetics aims at explaining genetic variation within natural species, and infer the different processes that lead to current genetic variation. Large scale genomic datasets are produced, and there is an increasing need of statistical methods to extract information from these datasets. My thesis work is part of this statistical modeling effort to answer to evolutionary biology and population genetic questions. We are interested in detecting footprints of local adaptation without, and infering non-stationary patterns of spatial variation. A Bayesian factor model is used to detect genes involved in local adaptation. We compare our factor model to existing methods, and show that it can reduce the false discovery rate. We also present a Bayesian model based on Gaussian processes to caracterize spatial genetic variations within species. The performances of these methods are tested on simulations and real datasets. Several open source software are available online

Statistiques bayésiennes en génétique des populations : modèle à facteurs et processus gaussiens pour étudier la variation génétique neutre et adaptative

In this thesis we present several works related to Bayesian statistics in population genetics. Population genetics aims at explaining genetic variation within natural species, and infer the different processes that lead to current genetic variation. Large scale genomic datasets are produced, and there is an increasing need of statistical methods to extract information from these datasets. My thesis work is part of this statistical modeling effort to answer to evolutionary biology and population genetic questions. We are interested in detecting footprints of local adaptation without, and infering non-stationary patterns of spatial variation. A Bayesian factor model is used to detect genes involved in local adaptation. We compare our factor model to existing methods, and show that it can reduce the false discovery rate. We also present a Bayesian model based on Gaussian processes to caracterize spatial genetic variations within species. The performances of these methods are tested on simulations and real datasets. Several open source software are available online.Nous présentons dans cette thèse plusieurs travaux de statistiques bayésiennes appliquées à la génétique des populations. La génétique des populations a pour but d'expliquer les variations génétiques au sein d'une espèce, et d'inférer les processus ayant conduits à ces variations. Pour cela, des données génétiques massives sont utilisées et il y a un besoin grandissant de méthodes statistiques pour traiter ces données. Le travail de cette thèse s'inscrit dans cet effort de modélisation statistique pour répondre aux enjeux de la génétique des populations, et de la biologie de l'évolution. Nous nous intéressons tout particulièrement à la détection de traces d'adaptation locale dans les génomes, et à l'inférence des variations spatiales non stationnaires.Un modèle d'analyse factorielle bayésien est proposé pour détecter les traces d'adaptation locale. Nous comparons notre approche aux méthodes existantes, et démontrons qu'elle permet d'obtenir un plus faible taux de fausses découvertes. Nous présentons également un modèle bayésien basé sur des processus gaussiens pour caractériser les variations génétiques spatiales dans l'aire de répartition d'une espèce. Les performances de ces méthodes sont démontrées sur différents exemples issus de simulations ou de données. Plusieurs logiciels open source qui implémentent ces méthodes ont été développés pendant la thèse