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.

Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.

Transcriptional profiling of hematopoietic cell subpopulations has helped to characterize the developmental stages of the hematopoietic system and the molecular bases of malignant and non-malignant blood diseases. Previously, only the genes targeted by expression microarrays could be profiled genome-wide. High-throughput RNA sequencing, however, encompasses a broader repertoire of RNA molecules, without restriction to previously annotated genes. We analyzed the BLUEPRINT consortium RNA-sequencing data for mature hematopoietic cell types. The data comprised 90 total RNA-sequencing samples, each composed of one of 27 cell types, and 32 small RNA-sequencing samples, each composed of one of 11 cell types. We estimated gene and isoform expression levels for each cell type using existing annotations from Ensembl. We then used guided transcriptome assembly to discover unannotated transcripts. We identified hundreds of novel non-coding RNA genes and showed that the majority have cell type-dependent expression. We also characterized the expression of circular RNA and found that these are also cell type-specific. These analyses refine the active transcriptional landscape of mature hematopoietic cells, highlight abundant genes and transcriptional isoforms for each blood cell type, and provide a valuable resource for researchers of hematologic development and diseases. Finally, we made the data accessible via a web-based interface: https://blueprint.haem.cam.ac.uk/bloodatlas/.

The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa.

The extent to which genomic convergence shapes locally adapted phenotypes in different species remains a fundamental question in evolutionary biology. To bring new insights to this debate we set up a framework which aimed to compare the adaptive trajectories of 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 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 more than 430 adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different adaptive mechanisms in these two closely related species. Such diversity of adaptive pathways may result from a high number of biological functions involved in adaptation to multiple eco-climatic gradients, and provides more arguments for the role of contingency and stochasticity in adaptation rather than repeatability.

Single-cell RNA-Sequencing (scRNA-Seq) has undergone major technological advances in recent years, enabling the conception of various organism-level cell atlassing projects. With increasing numbers of datasets being deposited in public archives, there is a need to address the challenges of enabling the reproducibility of such data sets. Here, we describe guidelines for a minimum set of metadata to sufficiently describe scRNA-Seq experiments, ensuring reproducibility of data analyses.

1 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria; Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria. 2 Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg University, Heidelberg, Germany. 3 Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland. 4 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. 5 Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW), Oncode Institute and University Medical Center Utrecht, Utrecht, The Netherlands. 6 Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria. 7 Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland. 8 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge. 9 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands; Department of Cancer Research, Oncode Institute, Hubrecht Institute, KNAW Utrecht, Utrecht, The Netherlands; Cancer Genomics Center, Utrecht, The Netherlands. 10 Divison of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. 11 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands. 12 Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge UK; Cavendish Laboratory, University of Cambridge, Cambridge, UK. 13 Eidgenössische Technische Hochschule (ETH) Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland. 14 Foundation Hubrecht Organoid Technology, Utrecht, The Netherlands. * Correspondence: cbock@cemm.oeaw.ac.at # Correspondence: organoids@humancellatlas.org

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Music performance anxiety (MPA) is a distressing and persistent anxious apprehension related to musical performance. The experience of MPA forces many musicians to give up performing or develop maladaptive coping mechanisms (e.g., avoidance or substance use), which can impact their career and wellbeing. High levels of MPA in students and vocalists are reported in the literature. Vocalists present a unique challenge for clinicians in that vocal and breathing mechanisms, required for performance, are negatively impacted when anxious. Acceptance and commitment therapy (ACT) has demonstrated efficacy for the treatment of a range of psychological problems including social anxiety disorder (of which MPA may be indicated as a subtype). This study sought to investigate whether group-based ACT may be a feasible and effective intervention for MPA in Australian student vocalists and aimed to design an intervention that could be adopted by music education providers. Potential participants (N = 31) completed an online survey including demographic questions and outcome measures. Six vocal students (four females; two males; aged M = 20.33 years) with elevated MPA scores participated in the ACT for MPA group program and 3-month follow-up. Group sessions were 2 h each week for six consecutive weeks. Participants were followed up 3 months post-intervention via online survey. There was a significant increase in psychological flexibility and significant decreases in MPA and psychological inflexibility. Gains were maintained at 3-month follow-up. The current study offers preliminary evidence for the feasibility and effectiveness of a group-based ACT protocol for musicians with performance anxiety which may be incorporated into tertiary performance training curricula.

Global investigation of histone marks in acute myeloid leukemia (AML) remains limited. Analyses of 38 AML samples through integrated transcriptional and chromatin mark analysis exposes 2 major subtypes. One subtype is dominated by patients with NPM1 mutations or MLL-fusion genes, shows activation of the regulatory pathways involving HOX-family genes as targets, and displays high self-renewal capacity and stemness. The second subtype is enriched for RUNX1 or spliceosome mutations, suggesting potential interplay between the 2 aberrations, and mainly depends on IRF family regulators. Cellular consequences in prognosis predict a relatively worse outcome for the first subtype. Our integrated profiling establishes a rich resource to probe AML subtypes on the basis of expression and chromatin data.

Transcriptional profiling of hematopoietic cell subpopulations has helped characterize the developmental stages of the hematopoietic system and the molecular basis of malignant and non-malignant blood diseases for the past three decades. The introduction of high-throughput RNA sequencing has increased knowledge of the full repertoire of RNA molecules in hematopoietic cells of different types, without relying on prior gene annotation. Here, we introduce the analysis of the BLUEPRINT consortium gene expression data for mature hematopoietic cells, comprising 90 total RNA and 32 small RNA sequencing experiments, from 27 different cell types. We used these data to describe the transcriptional profile of each we used guided transcriptome assembly to extend the annotation of the transcribed genome, which led to the identification of hundreds of novel non-coding RNA genes, which display a high degree of cell type specificity. We also characterized the expression of circular RNAs and found that these are also highly cell type specific. This resource refines the active transcriptional landscape of mature hematopoietic cells, highlights abundant genes and transcriptional isoforms for each cell type, and provides valuable data and visualisation tools for the scientific community working on hematological development and diseases.

Neutrophils play fundamental roles in innate inflammatory response, shape adaptive immunity1, and have been identified as a potentially causal cell type underpinning genetic associations with immune system traits and diseases2,3 The majority of these variants are non-coding and the underlying mechanisms are not fully understood. Here, we profiled the binding of one of the principal myeloid transcriptional regulators, PU.1, in primary neutrophils across nearly a hundred volunteers, and elucidate the coordinated genetic effects of PU.1 binding variation, local chromatin state, promoter-enhancer interactions and gene expression. We show that PU.1 binding and the associated chain of molecular changes underlie genetically-driven differences in cell count and autoimmune disease susceptibility. Our results advance interpretation for genetic loci associated with neutrophil biology and immune disease.

We present a set of biallelic SNVs and INDELs, from 2,548 samples spanning 26 populations from the 1000 Genomes Project, called de novo on GRCh38. We believe this will be a useful reference resource for those using GRCh38. It represents an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date by encompassing all of the GRCh38 primary assembly autosomes and pseudo-autosomal regions, including novel, medically relevant loci. Here, we describe how the data set was created and benchmark our call set against that produced by the final phase of the 1000 Genomes Project on GRCh37 and the lift-over of that data to GRCh38.

Whole genome sequences (WGS) greatly increase our ability to precisely infer population genetic parameters, demographic processes, and selection signatures. However, WGS may still be not affordable for a representative number of individuals/populations. In this context, our goal was to assess the efficiency of several SNP genotyping strategies by testing their ability to accurately estimate parameters describing neutral diversity and to detect signatures of selection. We analysed 110 WGS at 12× coverage for four different species, i.e., sheep, goats and their wild counterparts. From these data we generated 946 data sets corresponding to random panels of 1K to 5M variants, commercial SNP chips and exome capture, for sample sizes of five to 48 individuals. We also extracted low-coverage genome resequencing of 1×, 2× and 5× by randomly subsampling reads from the 12× resequencing data. Globally, 5K to 10K random variants were enough for an accurate estimation of genome diversity. Conversely, commercial panels and exome capture displayed strong ascertainment biases. Besides the characterization of neutral diversity, the detection of the signature of selection and the accurate estimation of linkage disequilibrium (LD) required high-density panels of at least 1M variants. Finally, genotype likelihoods increased the quality of variant calling from low coverage resequencing but proportions of incorrect genotypes remained substantial, especially for heterozygote sites. Whole genome resequencing coverage of at least 5× appeared to be necessary for accurate assessment of genomic variations. These results have implications for studies seeking to deploy low-density SNP collections or genome scans across genetically diverse populations/species showing similar genetic characteristics and patterns of LD decay for a wide variety of purposes.

We present a set of biallelic SNVs and INDELs, from 2,548 samples spanning 26 populations from the 1000 Genomes Project, called de novo on GRCh38. We believe this will be a useful reference resource for those using GRCh38. It represents an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date by encompassing all of the GRCh38 primary assembly autosomes and pseudo-autosomal regions, including novel, medically relevant loci. Here, we describe how the data set was created and benchmark our call set against that produced by the final phase of the 1000 Genomes Project on GRCh37 and the lift-over of that data to GRCh38.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.

The Functional Annotation of ANimal Genomes (FAANG) project aims, through a coordinated international effort, to provide high quality functional annotation of animal genomes with an initial focus on farmed and companion animals. A key goal of the initiative is to ensure high quality and rich supporting metadata to describe the project's animals, specimens, cell cultures and experimental assays. By defining rich sample and experimental metadata standards and promoting best practices in data descriptions, deposition and openness, FAANG champions higher quality and reusability of published datasets. FAANG has established a Data Coordination Centre, which sits at the heart of the Metadata and Data Sharing Committee. It continues to evolve the metadata standards, support submissions and, crucially, create powerful and accessible tools to support deposition and validation of metadata. FAANG conforms to the findable, accessible, interoperable, and reusable (FAIR) data principles, with high quality, open access and functionally interlinked data. In addition to data generated by FAANG members and specific FAANG projects, existing datasets that meet the main-or more permissive legacy-standards are incorporated into a central, focused, functional data resource portal for the entire farmed and companion animal community. Through clear and effective metadata standards, validation and conversion software, combined with promotion of best practices in metadata implementation, FAANG aims to maximise effectiveness and inter-comparability of assay data. This supports the community to create a rich genome-to-phenotype resource and promotes continuing improvements in animal data standards as a whole.

Chronic lymphocytic leukemia (CLL) is a frequent hematological neoplasm in which underlying epigenetic alterations are only partially understood. Here, we analyze the reference epigenome of seven primary CLLs and the regulatory chromatin landscape of 107 primary cases in the context of normal B cell differentiation. We identify that the CLL chromatin landscape is largely influenced by distinct dynamics during normal B cell maturation. Beyond this, we define extensive catalogues of regulatory elements de novo reprogrammed in CLL as a whole and in its major clinico-biological subtypes classified by IGHV somatic hypermutation levels. We uncover that IGHV-unmutated CLLs harbor more active and open chromatin than IGHV-mutated cases. Furthermore, we show that de novo active regions in CLL are enriched for NFAT, FOX and TCF/LEF transcription factor family binding sites. Although most genetic alterations are not associated with consistent epigenetic profiles, CLLs with MYD88 mutations and trisomy 12 show distinct chromatin configurations. Furthermore, we observe that non-coding mutations in IGHV-mutated CLLs are enriched in H3K27ac-associated regulatory elements outside accessible chromatin. Overall, this study provides an integrative portrait of the CLL epigenome, identifies extensive networks of altered regulatory elements and sheds light on the relationship between the genetic and epigenetic architecture of the disease.