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• Exome Sequencing to Define the Landscape of Plasma Cells in Systemic Light chain Amyloidosis

  Systemic light chain amyloidosis (AL) is characterized by the deposition of immunoglobulin light chains as amyloid fibrils in different organs, where they form toxic protein aggregates. The underlying disease is a plasma cell disorder, but limited whole exome data are available. We report the findings of an exome sequencing study in AL to define a plasma cell signature and compare this to monoclonal gammopathy of undefined significance (MGUS) and myeloma (MM). Twenty-four samples from unselected newly diagnosed untreated AL patients were analysed. CD138+ cells were isolated from bone marrow cells using MACSorting (Miltenyi Biotech, Bisley, UK). Cells were lysed in RLT+ buffer and DNA/RNA extracted using the AllPrep kit (Qiagen, Manchester, UK). Peripheral blood was isolated from patients, white blood cells purified by Ficoll-Pacque and DNA extracted using the QIAamp DNA mini kit (Qiagen). There were a median of 47 acquired nonsynonymous variants (range 4-253) per patient. Thirty percent of patients yielded a mutation in a myeloma driver gene. These mutations were either clonal or sub-clonal. There was evidence of MAPK activation with NRAS mutations, NF-κB activation and DNA repair pathway alterations. Exome data were used to determine the cytogenetic groups of AL samples and identified hyperdiploidy in 33% of cases and t(11;14) in 33% of cases. All translocations were generated via class switch recombination and breakpoints on chromosome 11 fell into the expected regions. A novel translocation, unlikely to be driving disease progression was identified involving RCC1. Finally, further analysis of the APCS gene encoding for the SAP protein did not show any biased genotype in the AL population in comparison to the general population. We conclude that exome sequencing identifies a genetic make-up of AL that is similar to other plasma cell disorders such as MM and MGUS. This not only includes copy number abnormalities and translocations but also nonsynonymous mutations.

  Study EGAS00001001418

• Overactivation of the IGF signalling pathway in osteosarcoma

  Tumour samples (low coverage WGS) from 35 patients with osteosarcoma were included. All samples were re-evaluated by an experienced bone pathologist who confirmed the diagnosis and a tumour content >50% per sample. All tumours represented conventional high-grade osteosarcomas.

  Study EGAS00001004767

• Distinct portrayal of lesions in synchronous multifocal lung adenocarcinoma revealed by genome sequencing

  Distinguishing multiple primary lung cancers in the synchronous multifocal intrapulmonary lesions has important significance on clinical staging and therapeutic decision. To investigate genomic aberration profiles, we applied whole genome and whole exome sequencing, and microarray-based comparative genomic hybridization on 15 intrapulmonary tumors derived from six patients with synchronous multifocal lung cancers having similar histological diagnosis. Any pair of intrapulmonary tumors in a single patient, which shared the identical genetic background and environment, showed an extinctive heterogeneity between each other. Phylogenetic relationship analysis indicated an independently branched evolution among all the tumors, suggesting they were multiple primary lung cancers. EGFR or KRAS mutations were found in 7 or 3 out of the 15 tumors, from 3 or 2 patients, respectively. Somatic mutational heterogeneity of these two genes in a single patient was also observed. Our analysis indicates genomic aberration profiling is valuable for identification of multiple primary lung cancer, especially when high histopathological concordance was observed between lesions. We also suggest a thoroughly molecular diagnosis against therapeutic target genes should be taken for each accessible nodule before making a plan for adjuvant therapy.

  Study EGAS00001001572

• GEI Studies - Psoriasis

  The GEI Studies Project was started as an NIH consortium involving 5 disease groups with funding provided through the Genes, Environment, and Health Initiative (GEI). The objective of the project was to optimize strategies for identifying rare variants as a follow up to genome-wide association studies (GWAS) using next-generation sequencing technology. One of the five targeted diseases, Psoriasis, is a multifactorial skin disease characterized by epidermal hyperproliferation and chronic inflammation that affects approximately 2% of Americans. Because only about one-third of all patients with psoriasis have a relative who is also affected with the disorder, psoriasis is not widely recognized as a genetic disease. However, previous research on families and identical twins has shown psoriasis has a strong genetic component, although environmental factors (such as infections, stress, and injuries) are also important. Although no definite psoriasis gene has yet been identified, research during the last ten years has revealed over forty potential gene locations that may contribute to the disease. Large amounts of data generated using next-generation sequencing technology would provide a more detailed characterization of sequence variation which would in turn increase the likelihood of detecting causative loci. With the presumption that rare variants are frequently the cause of a large portion of phenotypic variation, it was necessary to use large sample sets. In the main body of this study, we sequenced 4,966 samples (812 dermatologist-diagnosed cases of purely cutaneous psoriasis (PsC), 1,497 cases of rheumatologist-diagnosed psoriatic arthritis, 665 cases of cutaneous psoriasis with uncertain psoriatic arthritis status (PsV) and 1,992 controls) across 5.7 Mb of sequence containing 100 psoriasis candidate loci and 769 genes. For each candidate locus, we considered sequences within 250 kb of the strongest association signal. Within this window, three strategies were employed. For ten of the strongest psoriasis signals, we used a whole-region approach which included non-coding intergenic and intronic sequence. For four established loci of lesser significance, we targeted all full transcription units within the +/- 250 kb interval. For the remaining signals, we targeted the exons of all transcription units within the +/- 250 kb interval. Due to complexities of high-efficiency targeting of HLA genes we selected 5 specific candidate genes for sequencing near the HLA-C gene.

  Study phs000766

• Genetic landscape of inherited retinal dystrophies affected cases in Spain

  Inherited retinal diseases (IRDs), defined by dysfunction or progressive loss of photoreceptors, are disorders characterized by elevated heterogeneity, both at the clinical and genetic levels. Our main goal was to address the genetic landscape of IRD in a Spanish cohort of patients. A retrospective hospital-based cross-sectional study was carried out on IRD affected individuals, referred for genetic testing from all the Spanish autonomous communities. Clinical, demographic and familiar data were collected from each patient, including family pedigree, age of appearance of visual symptoms, presence of any systemic findings and geographical origin. Genetic studies were performed to all families with available DNA using different molecular techniques. Our study provides the general landscape for IRD in Spain and our results have important implications for genetic diagnosis, counselling and new therapeutic strategies to both the Spanish population and other related populations.

  Study EGAS00001005104

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• RNA-Seq in Patients with Primordial Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in the interpretation of likely causal genetic variation through identification of altered transcript abundance and/or structure. RNA-Seq is the most sensitive assay for both investigating transcript structure and abundance The primary aim of this pilot project is to investigate to what degree integrating exome-Seq and RNA-Seq data on the same individual can accelerate the identification of causal alleles for rare genetic diseases. There are two main strands to this: (i) identifying which variants discovered in exome-seq appear to be having a functional impact on transcripts, and (ii) identifying transcript outliers, especially among known causal genes, that may not necessarily have a causal variant identified from exome sequencing. The latter may identify the presence of causal variants that lie far from coding regions (e.g. the formation of cryptic splice sites deep within introns, or loss of long range regulatory elements), which can be confirmed with further targeted genetic assays. Just over 50% of all disease-causing variants recorded in the Human Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g. nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs). This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients with primordial dwarfism syndromes, for 10 of these samples we have previously generate exome data as part of our collaboration with the group of Prof Andrew Jackson. The two remaining samples are positive controls where the causal mutation is known, and is known to affect transcript structure and/or abundance. Primordial dwarfism is a prime candidate for these RNA-seq studies because all known causal mutations to date have key roles in DNA replication and thus, unsurprisingly, the products of the causal genes are typically ubiquitously expressed. Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) per sample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001000640

• Mechanisms of Chemotherapy Resistance in T-ALL

  The goals of this study are to unravel the molecular mechanisms underlying leukemic transformation and chemotherapy resistance in T-cell acute lymphoblastic leukemia (T-ALL). Towards this end, a cohort of T-ALL lymphoblast specimens was collected at the time of initial T-ALL diagnosis (prior to the start of therapy) from children enrolled on contemporary clinical trials, Children's Oncology Group study AALL0434 and Dana-Farber Cancer Institute 05-001. Samples were analyzed using targeted exon sequencing and RNA sequencing analysis. Results of these analyses form the basis of studies that have been submitted for publication, and will be linked to this entry once published.

  Study phs001513

• FEGA FAQs: a summary of the Q+A session from the FEGA workshop at ELIXIR AHM 2024

  The ELIXIR All Hands Meeting (AHM) was held in Uppsala, Sweden this June. We at EGA always look forward to this meeting as a chance to see many of our partners and colleagues face-to-face. The Federated EGA (FEGA) is one initiative that always benefits from the AHM, and this year FEGA was in the spotlight, with several questions related to the advances we've made recently. We held this year's FEGA workshop a little differently, with most time for discussions on various “hot topics”. Organised in different breakouts, over 2 rounds, participants could choose which session they wanted to attend. One breakout was a general Q+A about FEGA and the path to join. We had a diverse range of participants, including experts from Central EGA (CEGA) and FEGA inaugural nodes, people from up and coming nodes at various stages of readiness, as well as participants not directly a part of FEGA but curious to learn more. It made for a great discussion! In this blog post, we highlight some of the questions for those who didn’t have a chance to be there. What is the FEGA Collaboration Agreement? It is a legal agreement between the node and Central EGA (CEGA) where both parties agree to certain responsibilities, like what services will be provided, sharing of public metadata with CEGA, interoperable technologies and more. The agreement is for 4 years, with the expectation of long-term sustainability. If a node is made up of multiple institutes, one institute must be the signatory. This could require a legal agreement within the node. CEGA is composed of CRG and EMBL-EBI (EGA itself has its own legal agreement between these institutes) Check the FEGA Collaboration Agreement What about purely medical data? Is this accepted in FEGA? CEGA only accepts data approved for secondary research use. We do accept medical data collected in this context, as do some nodes. Nodes are able to accept any data they like, as long as it is sensitive, thus in need of controlled access. What about other data types other than genomics? Like proteomics or images? They can be accepted, although CEGA is currently not designed with those formats in mind, so they might not fit with precision in the data model. The FEGA nodes are welcome to accept any data they want. Proteomic studies generating sensitive data are increasing, and specific work will likely be developed in the near future to adress this need. Does CEGA require a certain amount or type of data to be shared by the node? No, we do not and it's up to each node to decide what specific data types they accept and in what format. Nonetheless, a FEGA node has the duty to be open to data submission from any institution of their country, and can't be limited by design to a single or limited data source. Does CEGA and FEGA require a certain amount or type of details and description on the data to be submitted to the node? Data description (technical and biological), usually called metadata in this field, is crucial for the findability of the submitted data. Thus, we highly recommend including as much information as available to maximise the discoverability of the data. Specifical minimal metadata is defined in the submission schema, and thresholds are specified by the nodes. We are currently working towards a unified FEGA metadata model. What are the geographical borders of FEGA? FEGA should make the E for Everyone, because any institute worldwide could join and we indeed do have countries and use-cases worldwide. Some of them are very close to officially joining the federation. What are the main differences between FEGA and GDI? The two networks have a lot in common and also some critical differences. You can read all about this in one of our latest blog post. If you have more questions, please consider having a look at the FEGA onboarding webpage and, of course, don't miss our workshop at the ELIXIR AHM next year!

  Blog fega-faqs-elixir-ahm-2024

• Melanoma Brain Metastasis Single-Cell RNA Sequencing Atlas

  Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma, yet our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBM (scRNA-seq n=5; snRNA-seq n=17) and 10 extracranial melanoma metastases (ECM; all snRNA-seq), and matched spatial single-cell transcriptomics (n=16) and T cell receptor (TCR)-seq (n=5). Key observations were validated in matched bulk RNA- and ATAC-seq of isogenic MBM/ECM cell culture models (n=24; including triplicates (labeled as *-A, -B and -C) of 4 MBM- and 4 ECM-derived patient cell lines with 2 matched pairs). This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

  Study phs002944

• Multiple migrations to the Philippines during the last 50,000 years

  A key link to understand human history in Island Southeast Asia is the Philippine archipelago and its poorly investigated genetic diversity. We collected and analyzed the most comprehensive set of population-genomic data for the Philippines, 1028 individuals covering 115 indigenous communities. We demonstrate that the Philippines were populated by at least five waves of human migration. The Cordillerans migrated into the Philippines prior to the arrival of rice agriculture, where some remain to be the least admixed East Asians carrying an ancestry shared by all Austronesian-speaking populations; thereby challenging an exclusive Out-of-Taiwan model of joint farming-language-people dispersal. Altogether, our findings portray Philippines as a crucial gateway, with a multilayered history, that ultimately changed the genetic landscape of the Asia-Pacific region.

  Study EGAS00001005083

• Estonian Biobank | Estonian Genome Center, University of Tartu

  The Estonian Biobank is the population-based biobank of the Estonian Genome Centre of University of Tartu. The biobank is conducted according to the Estonian Gene Research Act and all participants have signed broad informed consent. The cohort size is currently 51,535 people from 18 years of age and up. Metabolite concentrations (over 110; NMR and MS) are available for about 12,000 subjects; metabolic follow-up has been done for more than 2000 donors. GWAS has been performed on 20,000 subjects with different Illumina arrays and 2,500 have whole exomes and 2,400 full sequence. The data for the 2,400 fully sequenced samples is available through dbGaP.

  Study phs001230

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW20_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW20_F

  Dataset EGAD00001001797

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB23_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB23_C

  Dataset EGAD00001001709

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_F

  Dataset EGAD00001001776

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_M

  Dataset EGAD00001001777

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW24_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW24_F

  Dataset EGAD00001001803

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_C

  Dataset EGAD00001001820

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_M

  Dataset EGAD00001001828

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_C

  Dataset EGAD00001001829

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_M

  Dataset EGAD00001001831

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW15_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW15_M

  Dataset EGAD00001001792

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW27_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW27_M

  Dataset EGAD00001001807

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW29_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW29_M

  Dataset EGAD00001001810

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB23_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB23_F

  Dataset EGAD00001001710