Phylogenomics and the rise of the angiosperms.

Angiosperms are the cornerstone of most terrestrial ecosystems and human livelihoods1,2. A robust understanding of angiosperm evolution is required to explain their rise to ecological dominance. So far, the angiosperm tree of life has been determined primarily by means of analyses of the plastid genome3,4. Many studies have drawn on this foundational work, such as classification and first insights into angiosperm diversification since their Mesozoic origins5-7. However, the limited and biased sampling of both taxa and genomes undermines confidence in the tree and its implications. Here, we build the tree of life for almost 8,000 (about 60%) angiosperm genera using a standardized set of 353 nuclear genes8. This 15-fold increase in genus-level sampling relative to comparable nuclear studies9 provides a critical test of earlier results and brings notable change to key groups, especially in rosids, while substantiating many previously predicted relationships. Scaling this tree to time using 200 fossils, we discovered that early angiosperm evolution was characterized by high gene tree conflict and explosive diversification, giving rise to more than 80% of extant angiosperm orders. Steady diversification ensued through the remaining Mesozoic Era until rates resurged in the Cenozoic Era, concurrent with decreasing global temperatures and tightly linked with gene tree conflict. Taken together, our extensive sampling combined with advanced phylogenomic methods shows the deep history and full complexity in the evolution of a megadiverse clade.

Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

A Comprehensive Phylogenomic Platform for Exploring the Angiosperm Tree of Life.

The tree of life is the fundamental biological roadmap for navigating the evolution and properties of life on Earth, and yet remains largely unknown. Even angiosperms (flowering plants) are fraught with data gaps, despite their critical role in sustaining terrestrial life. Today, high-throughput sequencing promises to significantly deepen our understanding of evolutionary relationships. Here, we describe a comprehensive phylogenomic platform for exploring the angiosperm tree of life, comprising a set of open tools and data based on the 353 nuclear genes targeted by the universal Angiosperms353 sequence capture probes. The primary goals of this article are to (i) document our methods, (ii) describe our first data release, and (iii) present a novel open data portal, the Kew Tree of Life Explorer (https://treeoflife.kew.org). We aim to generate novel target sequence capture data for all genera of flowering plants, exploiting natural history collections such as herbarium specimens, and augment it with mined public data. Our first data release, described here, is the most extensive nuclear phylogenomic data set for angiosperms to date, comprising 3099 samples validated by DNA barcode and phylogenetic tests, representing all 64 orders, 404 families (96$\%$) and 2333 genera (17$\%$). A "first pass" angiosperm tree of life was inferred from the data, which totaled 824,878 sequences, 489,086,049 base pairs, and 532,260 alignment columns, for interactive presentation in the Kew Tree of Life Explorer. This species tree was generated using methods that were rigorous, yet tractable at our scale of operation. Despite limitations pertaining to taxon and gene sampling, gene recovery, models of sequence evolution and paralogy, the tree strongly supports existing taxonomy, while challenging numerous hypothesized relationships among orders and placing many genera for the first time. The validated data set, species tree and all intermediates are openly accessible via the Kew Tree of Life Explorer and will be updated as further data become available. This major milestone toward a complete tree of life for all flowering plant species opens doors to a highly integrated future for angiosperm phylogenomics through the systematic sequencing of standardized nuclear markers. Our approach has the potential to serve as a much-needed bridge between the growing movement to sequence the genomes of all life on Earth and the vast phylogenomic potential of the world's natural history collections. [Angiosperms; Angiosperms353; genomics; herbariomics; museomics; nuclear phylogenomics; open access; target sequence capture; tree of life.].

Target sequence data shed new light on the infrafamilial classification of Araceae.

PREMISE: Recent phylogenetic studies of the Araceae have confirmed the position of the duckweeds nested within the aroids, and the monophyly of a clade containing all the unisexual flowered aroids plus the bisexual-flowered Calla palustris. The main objective of the present study was to better resolve the deep phylogenetic relationships among the main lineages within the family, particularly the relationships between the eight currently recognized subfamilies. We also aimed to confirm the phylogenetic position of the enigmatic genus Calla in relation to the long-debated evolutionary transition between bisexual and unisexual flowers in the family. METHODS: Nuclear DNA sequence data were generated for 128 species across 111 genera (78%) of Araceae using target sequence capture and the Angiosperms 353 universal probe set. RESULTS: The phylogenomic data confirmed the monophyly of the eight Araceae subfamilies, but the phylogenetic position of subfamily Lasioideae remains uncertain. The genus Calla is included in subfamily Aroideae, which has also been expanded to include Zamioculcadoideae. The tribe Aglaonemateae is newly defined to include the genera Aglaonema and Boycea. CONCLUSIONS: Our results strongly suggest that new research on African genera (Callopsis, Nephthytis, and Anubias) and Calla will be important for understanding the early evolution of the Aroideae. Also of particular interest are the phylogenetic positions of the isolated genera Montrichardia, Zantedeschia, and Anchomanes, which remain only moderately supported here.

A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity.

Before environmental DNA (eDNA) can establish itself as a robust tool for biodiversity monitoring, comparison with existing approaches is necessary, yet is lacking for terrestrial mammals. Moreover, much is unknown regarding the nature, spread and persistence of DNA shed by animals into terrestrial environments, or the optimal experimental design for understanding these potential biases. To address some of these challenges, we compared the detection of terrestrial mammals using eDNA analysis of soil samples against confirmed species observations from a long-term (approx. 9-year) camera-trapping study. At the same time, we considered multiple experimental parameters, including two sampling designs, two DNA extraction kits and two metabarcodes of different sizes. All mammals regularly recorded with cameras were detected in eDNA. In addition, eDNA reported many unrecorded small mammals whose presence in the study area is otherwise documented. A long metabarcode (≈220 bp) offering a high taxonomic resolution, achieved a similar efficiency as a shorter one (≈70 bp) and a phosphate buffer-based extraction gave similar results as a total DNA extraction method, for a fraction of the price. Our results support that eDNA-based monitoring should become a valuable part of ecosystem surveys, yet mitochondrial reference databases need to be enriched first.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Meeting review. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics.

A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

Rapid identification and interpretation of gene-environment associations using the new R.SamBada landscape genomics pipeline.

samßada is a genome-environment association software, designed to search for signatures of local adaptation. However, pre- and postprocessing of data can be labour-intensive, preventing wider uptake of the method. We have now developed R.SamBada, an r-package providing a pipeline for landscape genomic analysis based on samßada, spanning from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, R.SamBada standardizes the landscape genomics pipeline and eases the search for candidate genes of local adaptation, enhancing reproducibility of landscape genomic studies. The efficiency and power of the pipeline is illustrated using two examples: sheep populations from Morocco with no evident population structure and Lidia cattle from Spain displaying population substructuring. In both cases, R.SamBada enabled rapid identification and interpretation of candidate genes, which are further discussed in the light of local adaptation. The package is available in the r CRAN package repository and on GitHub (github.com/SolangeD/R.SamBada).

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata.

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.

Characterizing neutral genomic diversity and selection signatures in indigenous populations of Moroccan goats (Capra hircus) using WGS data.

Since the time of their domestication, goats (Capra hircus) have evolved in a large variety of locally adapted populations in response to different human and environmental pressures. In the present era, many indigenous populations are threatened with extinction due to their substitution by cosmopolitan breeds, while they might represent highly valuable genomic resources. It is thus crucial to characterize the neutral and adaptive genetic diversity of indigenous populations. A fine characterization of whole genome variation in farm animals is now possible by using new sequencing technologies. We sequenced the complete genome at 12× coverage of 44 goats geographically representative of the three phenotypically distinct indigenous populations in Morocco. The study of mitochondrial genomes showed a high diversity exclusively restricted to the haplogroup A. The 44 nuclear genomes showed a very high diversity (24 million variants) associated with low linkage disequilibrium. The overall genetic diversity was weakly structured according to geography and phenotypes. When looking for signals of positive selection in each population we identified many candidate genes, several of which gave insights into the metabolic pathways or biological processes involved in the adaptation to local conditions (e.g., panting in warm/desert conditions). This study highlights the interest of WGS data to characterize livestock genomic diversity. It illustrates the valuable genetic richness present in indigenous populations that have to be sustainably managed and may represent valuable genetic resources for the long-term preservation of the species.

Prospects and challenges for the conservation of farm animal genomic resources, 2015-2025.

Livestock conservation practice is changing rapidly in light of policy developments, climate change and diversifying market demands. The last decade has seen a step change in technology and analytical approaches available to define, manage and conserve Farm Animal Genomic Resources (FAnGR). However, these rapid changes pose challenges for FAnGR conservation in terms of technological continuity, analytical capacity and integrative methodologies needed to fully exploit new, multidimensional data. The final conference of the ESF Genomic Resources program aimed to address these interdisciplinary problems in an attempt to contribute to the agenda for research and policy development directions during the coming decade. By 2020, according to the Convention on Biodiversity's Aichi Target 13, signatories should ensure that "…the genetic diversity of …farmed and domesticated animals and of wild relatives …is maintained, and strategies have been developed and implemented for minimizing genetic erosion and safeguarding their genetic diversity." However, the real extent of genetic erosion is very difficult to measure using current data. Therefore, this challenging target demands better coverage, understanding and utilization of genomic and environmental data, the development of optimized ways to integrate these data with social and other sciences and policy analysis to enable more flexible, evidence-based models to underpin FAnGR conservation. At the conference, we attempted to identify the most important problems for effective livestock genomic resource conservation during the next decade. Twenty priority questions were identified that could be broadly categorized into challenges related to methodology, analytical approaches, data management and conservation. It should be acknowledged here that while the focus of our meeting was predominantly around genetics, genomics and animal science, many of the practical challenges facing conservation of genomic resources are societal in origin and are predicated on the value (e.g., socio-economic and cultural) of these resources to farmers, rural communities and society as a whole. The overall conclusion is that despite the fact that the livestock sector has been relatively well-organized in the application of genetic methodologies to date, there is still a large gap between the current state-of-the-art in the use of tools to characterize genomic resources and its application to many non-commercial and local breeds, hampering the consistent utilization of genetic and genomic data as indicators of genetic erosion and diversity. The livestock genomic sector therefore needs to make a concerted effort in the coming decade to enable to the democratization of the powerful tools that are now at its disposal, and to ensure that they are applied in the context of breed conservation as well as development.