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Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

Chronic pain is highly prevalent and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with major depressive disorder (MDD) is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the United Kingdom Biobank study. Using family-based mixed-model analyses, we examined the contribution of genetics and shared family environment to chronic pain by spouse, sibling, and household relationships. These analyses were conducted in GS:SFHS (n = 23,960), a family- and population-based study of individuals recruited from the Scottish population through their general practitioners. We then examined and partitioned the correlation between chronic pain and MDD and estimated the contribution of genetic factors and shared environment in GS:SFHS. Finally, we used data from two independent genome-wide association studies to test whether chronic pain has a polygenic architecture and examine whether genomic risk of psychiatric disorder predicted chronic pain and whether genomic risk of chronic pain predicted MDD. These analyses were conducted in GS:SFHS and repeated in UK Biobank, a study of 500,000 from the UK population, of whom 112,151 had genotyping and phenotypic data. Chronic pain is a moderately heritable trait (heritability = 38.4%, 95% CI 33.6% to 43.9%) that is significantly concordant in spouses (variance explained 18.7%, 95% CI 9.5% to 25.1%). Chronic pain is positively correlated with depression (ρ = 0.13, 95% CI 0.11 to 0.15, p = 2.72x10-68) and shows a tendency to cluster within families for genetic reasons (genetic correlation = 0.51, 95%CI 0.40 to 0.62, p = 8.24x10-19). Polygenic risk profiles for pain, generated using independent GWAS data, were associated with chronic pain in both GS:SFHS (maximum β = 6.18x10-2, 95% CI 2.84 x10-2 to 9.35 x10-2, p = 4.3x10-4) and UK Biobank (maximum β = 5.68 x 10-2, 95% CI 4.70x10-2 to 6.65x10-2, p < 3x10-4). Genomic risk of MDD is also significantly associated with chronic pain in both GS:SFHS (maximum β = 6.62x10-2, 95% CI 2.82 x10-2 to 9.76 x10-2, p = 4.3x10-4) and UK Biobank (maximum β = 2.56x10-2, 95% CI 1.62x10-2 to 3.63x10-2, p < 3x10-4). Limitations of the current study include the possibility that spouse effects may be due to assortative mating and the relatively small polygenic risk score effect sizes. Genetic factors, as well as chronic pain in a partner or spouse, contribute substantially to the risk of chronic pain for an individual. Chronic pain is genetically correlated with MDD, has a polygenic architecture, and is associated with polygenic risk of MDD.

Communities of experts collaborating on scientific or technical projects are drivers of innovation across the life sciences. The ELIXIR research infrastructure organises scientific- and technological-themed communities as one of its key mechanisms to ensure that services are user-focused, while at the same time facilitating collaboration and creating scientific impact through the life science data generated across Europe. ELIXIR has rapidly expanded its communities portfolio in response to unmet needs and has developed a comprehensive process framework to facilitate the work of these communities. The ELIXIR Communities framework is made up of a suite of tools and processes that ensure effective community evolution and management, covering how communities are established, led, supported, and can collaborate across ELIXIR and beyond. Being aware of similar approaches in other contexts and in the interests of furthering community development in other research infrastructures and similar organisations, we share insights into the ELIXIR Communities framework and outline the skill set of a community manager and what this looks like in the ELIXIR context. Finally, to show the benefits of the communities, we share concrete examples of how the ELIXIR Communities have had an impact on the scientific landscape. By showcasing these outcomes we hope to demonstrate not only to other research infrastructures, but also to funders, that supporting scientific communities provides a valuable return on investment. We hope that these examples will encourage life scientists who may be interested in joining the ELIXIR Communities, and research infrastructure professionals whose roles require structured engagement with domain experts and users.

Sensitivity to pain varies considerably between individuals and is known to be heritable. Increased sensitivity to experimental pain is a risk factor for developing chronic pain, a common and debilitating but poorly understood symptom. To understand mechanisms underlying pain sensitivity and to search for rare gene variants (MAF<5%) influencing pain sensitivity, we explored the genetic variation in individuals' responses to experimental pain. Quantitative sensory testing to heat pain was performed in 2,500 volunteers from TwinsUK (TUK): exome sequencing to a depth of 70× was carried out on DNA from singletons at the high and low ends of the heat pain sensitivity distribution in two separate subsamples. Thus in TUK1, 101 pain-sensitive and 102 pain-insensitive were examined, while in TUK2 there were 114 and 96 individuals respectively. A combination of methods was used to test the association between rare variants and pain sensitivity, and the function of the genes identified was explored using network analysis. Using causal reasoning analysis on the genes with different patterns of SNVs by pain sensitivity status, we observed a significant enrichment of variants in genes of the angiotensin pathway (Bonferroni corrected p = 3.8×10(-4)). This pathway is already implicated in animal models and human studies of pain, supporting the notion that it may provide fruitful new targets in pain management. The approach of sequencing extreme exome variation in normal individuals has provided important insights into gene networks mediating pain sensitivity in humans and will be applicable to other common complex traits.

In the version of this article initially published, Lena Dolman's second affiliation was given as Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. The correct second affiliation is Ontario Institute for Cancer Research, Toronto, Ontario, Canada. The error has been corrected in the HTML and PDF versions of the article.

Background Individuals with an extremely rare inherited condition, termed Congenital Insensitivity to Pain (CIP), do not feel pain in response to noxious stimuli. Variants in SCN9A , encoding the transmembrane voltage-gated sodium channel Na v 1.7, have previously been reported in subjects with CIP accompanied by anosmia, which are typically transmitted in a recessive pattern. Functional characterisations of some of these SCN9A mutations show that they result in complete loss-of-function of Na v 1.7. Methods In a consanguineous family we performed whole exome sequencing of three members who have a diagnosis of CIP and one unaffected family member. The functional effects of the segregating variant in SCN9A were determined using patch clamp electrophysiology in human embryonic kidney (HEK) 293 cells transfected with the variant. Results We found that each CIP subject was homozygous for a putatively nonsense variant, R1488*, in SCN9A . This variant was reported elsewhere in a subject with CIP, though the functional effect was not determined. Using electrophysiology, we confirm that this variant results in a complete loss-of-function of Na v 1.7. Conclusions We confirm through electrophysiological analysis that this R1488* variant in SCN9A results in complete loss-of-function of Na v 1.7, which is consistent with reports on other variants in this gene in subjects with CIP.

Background In moderate-throughput SNP genotyping there was a gap in the workflow, between choosing a set of SNPs and submitting their sequences to proprietary assay design software, which was not met by existing software. Retrieval and formatting of sequences flanking each SNP, prior to assay design, becomes rate-limiting for more than about ten SNPs, especially if annotated for repetitive regions and adjacent variations. We routinely process up to 50 SNPs at once. Implementation We created Seq4SNPs , a web-based, walk-away software that can process one to several hundred SNPs given rs numbers as input. It outputs a file of fully annotated sequences formatted for one of three proprietary design softwares: TaqMan's Primer-By-Design FileBuilder, Sequenom's iPLEX or SNPstream's Autoprimer , as well as unannotated fasta sequences. We found genotyping assays to be inhibited by repetitive sequences or the presence of additional variations flanking the SNP under test, and in multiplexes, repetitive sequence flanking one SNP adversely affects multiple assays. Assay design software programs avoid such regions if the input sequences are appropriately annotated, so we used Seq4SNPs to provide suitably annotated input sequences, and improved our genotyping success rate. Adjacent SNPs can also be avoided, by annotating sequences used as input for primer design. Conclusion The accuracy of annotation by Seq4SNPs is significantly better than manual annotation (P < 1e-5). Using Seq4SNPs to incorporate all annotation for additional SNPs and repetitive elements into sequences, for genotyping assay designer software, minimizes assay failure at the design stage, reducing the cost of genotyping. Seq4SNPs provides a rapid route for replacement of poor test SNP sequences. We routinely use this software for assay sequence preparation. Seq4SNPs is available as a service at http://moya.srl.cam.ac.uk/oncology/bio/s4shome.html and http://moya.srl.cam.ac.uk/cgi-bin/oncology/srl/ncbi/seq4snp1.pl , currently for human SNPs, but easily extended to include any species in dbSNP.

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

Gold-standard cancer data management is pivotal to enable precision medicine for European citizens. Achieving this goal relies on key elements: adopting standardized data formats, ensuring robust data privacy, educating professionals about the infrastructure’s benefits and leveraging cutting-edge technologies to transform cancer care.