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• Comparison of single/pooled CTCs and bulk exome sequencing

  Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

  Dataset EGAD00001009052

• Transcriptional consequences of SETBP1 variants in developmental disorders and malignancies

  The SETBP1 gene encodes a DNA-binding nuclear protein that regulates gene expression across multiple tissues. Precise control of SETBP1 dosage is essential for normal cellular function, as both loss- and gain-of-function variants of SETBP1 can lead to severe phenotypic consequences. De novo germline gain-of-function point mutations that prolong SETBP1 half-life result in Schinzel-Giedion syndrome (SGS), an ultra-rare and severe congenital disorder associated with extensive developmental abnormalities and health complications. These SGS-associated variants cluster within a hotspot in the SKI-homology domain of SETBP1, overlapping with somatic mutations recurrently observed in myeloid leukaemia with poor prognosis. Thus far, the precise mechanisms by which these SETBP1 mutations drive disease remain largely unclear. Here, through single-cell and single-nucleus mRNA sequencing of a rare collection of primary patient samples with SGS and myeloid leukaemia, we examined the transcriptional consequences of SETBP1 gain-of-function variants in haematopoietic cells, comparing their impacts when acquired in the germline and somatically in malignancies.

  Dataset EGAD00001015482

• Jackson Heart Study (JHS) Cohort

  The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: (1) random, 17%, (2) volunteer, 30%, (3) currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31%, and (4) secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents, aged 35-84, during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (see study history) include medical history, physical examination, blood/urine analytes, and interview questions on areas such as physical activity, stress, coping and spirituality, racism and discrimination, socioeconomic position, and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurements, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to update information, confirm vital statistics, document interim medical events, hospitalizations and functional status, and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset. Note: Regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP, the coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis. The JHS Cohort is utilized in the following dbGaP substudies. To view genotypes, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000286 JHS Cohort. phs000402 ESP Heart GO JHS phs000498 JHS Allelic Spectrum Seq phs000499 JHS CARephs001069 MI Gen JHS phs001098 T2D GENES Exome Seq phs001356 Exome Chip phs002256 Cardiometabolic Renal Proteomics phs002369 Metabolomics Insulin Resistance

  Study phs000286

• Kids First: Whole Genome Sequencing in Structural Defects of the Neural Tube

  Myelomeningocele (aka meningomyelocele, MM) is the most severe form of spina bifida, a neural tube defect (NTD) in humans and the most common CNS birth defect. MM is considered a genetically complex disease, and occurs in 3.72/10,000 live US birth, and is partly preventable with prenatal folate, but the genetic basis and the mechanisms by which folate work to reduce disease incidence remain obscure. MM is associated nearly uniformly with prenatal hydrocephalus and the Arnold-Chiari malformation, as well as paraplegia and lifelong neuromotor disability. The genes for several rare syndromic forms of NTDs are known, but the causes for the majority with sporadic MM remain unknown. Despite the importance of MM, most previous research has been limited to targeted sequencing and association studies of folate metabolism genes, or very small-scale exome sequencing. We hypothesize that de novo mutations (DNMs) produce likely gene disrupting (LGD) events that contribution to MM risk. Using conservative estimates of between 50-100 recurrently mutated discoverable genes contributing to risk, and our preliminary data demonstrating an excess of LGD DNMs in MM compared with control individuals, we estimate that with a cohort size of 1000 trios, we should uncover between 5-20 new recurrently mutated genes underlying MM, with minimal false-discovery. With this in mind, we formed the Spina Bifida Sequencing Consortium, and established a platform for data and sample sharing. Preliminary analysis of our first batch of 100 trios analyzed by WGS from GMKF suggests a wealth of important gene mutations. We have embarked on a new recruitment effort of an additional cohort of 400 new simplex MM trios, in collaboration with the US Spina Bifida Association, consented trios to allow for data sharing, and have performed detailed sample quality control. We also have preliminary data that use of dried bloodspot DNA from trios performs comparably to whole blood DNA, so will be happy to swap saliva for bloodspot recruitment at NIH's preference. This cohort is now half-way assembled, with the remaining cohort to be ascertained in the next 6 months. We have established a workflow for de novo SNP/INDEL/SV detection from WGS and have ample computer storage and nodes to see the project to completion. We also plan to continue recruitment into the future with the goal of 2000 trios in the next 5 years. We propose a detailed bioinformatics workflow to identify gene mutations within a statistical framework, considering detailed scRNA expression profiling from developing mammalian neural tube, and have developed a robust functional validation workflow using Xenopus and mouse gene targeting. Our project has the potential to uncover a host of causes for this most common of the CNS birth defects, paving the way for future breakthroughs in detection, treatment, and prevention.

  Study phs002591

• Population Genetic Testing and SERPINA1 Sequencing Identifies Unidentified Alpha-1 Antitrypsin Deficiency Alleles and Gene-Environment Interaction with Hepatitis C Infection

  Design and aims: Data were available through an institutional effort to combine de-identified electronic health records (EHR) data with genomic information from a DNA biobank. The design of this work was a retrospective cohort study in which we investigated the rates of liver transplantation and hepatitis C infection as it relates to genotype risk for alpha-1 antitrypsin deficiency (AATD). We used regression analysis to look for relationships between these risks and outcomes. This led to further investigation of environmental exposures and phenotypes as it related to the underlying genotypic risk. Population information: The cohort consisted of 72,027 individuals of European ancestry for whom genotype and EHR data were available. Molecular technologies employed: Genome-wide genotyping was previously performed using the Illumina Infinium Expanded Multi-Ethnic Genotyping Array plus custom content (referred to as the Vanderbilt University Medical Center BioVU MEGAEX). Individuals' AATD-related alleles were inferred from the genotyping data. Principal findings of the study: Liver transplantation was associated with presence of the Z AATD allele and with hepatitis C infection. There was a significant interaction between AATD genotype groups and hepatitis C infection as it related to liver transplantation. Data available through dbGaP: Inferred AATD genotype status, sex, age, length of EHR in years, presence/absence of AATD clinical diagnosis, liver transplant status, and hepatitis C infection status.

  Study phs003297

• eMERGE Geisinger eGenomic Medicine (GeM) - MyCode Project Controls

  A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patient agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. For the initial eMERGE Geisinger eGenomic Medicine (GeM) genotyping project, a subset of 1,232 unique samples were genotyped using Illumina HumanOmniExpress-12 v1.0 arrays, and used as population controls for other Geisinger Clinic case cohorts, including abdominal aortic aneurysm and gastric bypass surgery cases. These samples were selected from a larger subset of approximately 6,000 MyCode DNA samples using a partial matching algorithm that included age, sex, and body mass index as variables.

  Study phs000381

• Heterogeneity in Lysosomal Storage Disorders

  Gaucher disease (GD) is an inherited lysosomal storage disorder caused by biallelic pathogenic variants in GBA1, which encodes glucocerebrosidase. Additionally, GBA1 variants are a well-established genetic risk factor for Parkinson's disease (PD), leading some PD research centers to incorporate GBA1 screening to identify patients who may benefit from targeted therapeutics. However, detecting GBA1 variants remains challenging due to the gene's high sequence homology with its pseudogene, which can give rise to complex recombinant alleles. This study evaluates the performance of Gauchian, a recently developed software tool for identifying GBA1 variants from whole genome sequencing (WGS) data. The assessment was conducted in a cohort of 90 individuals with GD and five GBA1 heterozygotes, all previously characterized through Sanger sequencing. While Gauchian successfully identified the most common genotypes, it demonstrated limitations in detecting rare or de novo variants due to its restricted internal database and heavy reliance on intergenic structural variants. These limitations led to misclassified homozygosity, incomplete genotyping, and undetected recombination events, ultimately reducing the tool's utility for comprehensive variant screening and precluding its application in diagnostic settings. The study dataset includes WGS from the 90 individuals used in the Gauchian pipeline. This data will be available through dbGaP to facilitate further research on GBA1 variant detection, genotype-phenotype correlations in GD and PD, and the development of improved bioinformatics tools for complex variant identification.

  Study phs003459

• Whole genome sequencing of matched primary and metastatic acral melanomas

  Next generation sequencing has enabled systematic discovery of mutational spectra in cancer samples. Here we used whole genome sequencing to characterise somatic mutations and structural variation in a primary acral melanoma and its lymph node metastasis. Our data show that the somatic mutational rates in this acral melanoma sample pair was more comparable to the rate reported in cancer genomes not associated with mutagenic exposure than in the genome of a melanoma cell line or the transcriptome of melanoma short-term cultures. Despite the perception that acral skin is sun-protected, the dominant mutational signature in these samples is compatible with damage due to ultraviolet light exposure. A nonsense mutation in ERCC5 discovered in both the primary and metastatic tumours, could also have contributed to the mutational signature through accumulation of unrepaired dipyrimidine lesions. However, evidence of transcription-coupled repair was suggested by the lower mutational rate in the transcribed regions and expressed genes. The primary and the metastasis are highly similar at the level of global gene copy number alterations, loss of heterozygosity and single nucleotide variation (SNV). Furthermore, the majority of the SNVs in the primary tumour were propagated in the metastasis and one non-synonymous coding SNV and one splice site mutation appeared to arise de novo in the metastatic lesion.

  Study EGAS00001000169

• Organoid BulkRNAseq

  Here, we aim to understand early determinants of brain organoid quality and generated a set of brain organoids from 12 different hPSC lines using an adaptation of the unguided Lancaster protocol (Lancaster & Knoblich, 2014). All cell lines were obtained from healthy donors. 12 donor hPSC lines were differentiated into brain organoids and a set of 72 individual organoids (6 per line) were randomly selected morphologically diverse organoids and subjected to bulk RNA sequencing for gene expression analysis. RNA was extracted using the RNeasy kit (Qiagen, Germany) according to the manufacturer’s instructions. A total of 1000 ng per sample was sent for RNA sequencing to Azenta Life Sciences (Genewiz Leipzig, Germany) for sequencing. Library preparation was performed using the NEBNext Ultra II RNA Library Prep Kit for Illumina following manufacturer’s instructions (NEB, Ipswich, MA, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End (PE) configuration v1.5 at a depth of >20 million reads in each sample. Raw sequence data (.bcl files) generated from Illumina NovaSeq6000 was converted into fastq files and de-multiplexed using Illumina bcl2fastq program version 2.20. The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014).

  Study EGAS50000000659

• JMML targeted sequencing (2013)

  Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic myeloproliferative neoplasm of early childhood initiated by germline or somatic RAS-activating mutations (Chang, Dvorak et al. 2014). Clinical evolution is heterogeneous, with acute myeloid leukemia (AML) transformation in 1/3 of cases and frequent relapses after hematopoietic-stem-cell transplantation (HSCT), although some patients experience spontaneous remission (Locatelli, Nollke et al. 2005; Matsuda, Shimada et al. 2007; Takagi, Piao et al. 2013; Locatelli and Niemeyer 2015). The initiating lesion incompletely predicts outcome, suggesting the involvement of additional mutations. Genetic profiling of a large JMML cohort (n=118), including whole-exome sequencing (WES) in 30 cases, uncovered additional genetic abnormalities in 56 cases (47%). Somatic events were rare (0.38/case/Mb) and restricted to sporadic (47/78; 60%) or NF1-associated (8/8; 100%) JMML. Multiple concomitant genetic hits were identified targeting the RAS pathway in 13/78 (17%), RAC2D63V, which activates the PI3K/PDK1/AKT and the mTORC2 pathways, and a novel pathway for JMML, the polycomb repressive complex 2 (PRC2), in 26/78 (33%) of the sporadic JMML cases. Interestingly, PRC2 imbalance was recently shown to synergize with RAS activation (De Raedt, Beert et al. 2014). JMML outcome was associated to the mutational profile, suggesting a dose-dependent effect for RAS-pathway activation, distinguishing very aggressive JMML rapidly progressing to AML.

  Study EGAS00001001324

• Machine Learning Signal Enrichment for Ultrasensitive Plasma Tumor Burden Monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007451

• Double mutant DNMT3A AML: a unique subtype

  Mutation of DNMT3A, encoding a de novo methyltransferase essential for cytosine methylation, is a common early event in clonal hematopoiesis (CH) and adult acute myeloid leukemia (AML). Spontaneous deamination of methylated cytosines damages DNA, which is repaired by the base excision repair (BER) enzymes MBD4 and TDG. Congenital MBD4-deficiency has been linked to early-onset CH and AML, and is marked by exceedingly high levels of DNA damage and mutation of DNMT3A. Strikingly, wildtype (WT) DNMT3A binds TDG, thereby potentiating its repair activity. Since TDG is the only remaining BER enzyme in MBD4-deficient AML patients capable of repairing methylation damage, we investigated whether mutant DNMT3A negatively affects the repair function of TDG. We found that, whereas WT DNMT3A stimulates TDG function, mutant DNMT3A impairs TDG-mediated repair of DNA damage in vitro. In light of this finding and to extrapolate our observations to the broader AML patient population, we investigate here the genetic profiles and survival outcomes of AML patients with single (SM) versus double mutant (DM) DNMT3A. DM DNMT3A AML patients show a characteristic driver mutation landscape and reduced overall survival when compared to SM DNMT3A AML patients. Importantly, whole-genome sequencing showed a trend for increased DNA damage in primary DM DNMT3A AML samples, especially when DNMT3A mutations are located at the DNMT3A-TDG interaction interface.

  Study EGAS00001007966

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007545

• NHGRI GREGoR Consortium: Genomics Research to Elucidate the Genetics of Rare Disease

  The NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare Disease) Consortium was established in June 2021 with the goal of developing novel tools and approaches to advance the discovery of the genetic basis of rare conditions. Numerous types of molecular data are generated in GREGoR and available on the AnVIL cloud platform via dbGaP application, including short- and long-read genome and exome sequencing, transcriptomics, metabolomics, methylomics, and proteomics. De-identified clinical and demographic data is obtained, with a focus on standardized ontologies.Visit the GREGoR Consortium data webpage for summary information about the GREGoR Dataset, including numbers of participants and data types, methods documentation, and Release Notes. The Consortium comprises five Research Centers (RCs - see below), a Data Coordinating Center (DCC), and various partner members and external collaborators.Baylor College of Medicine Research Center (BCM-GREGoR) The Baylor College of Medicine GREGoR program, which is part of the GREGoR consortium, enrolls individuals, families, and cohorts with suspected rare disease across a range of syndromic and non-syndromic phenotypes. Subjects are enrolled from national and international collaborating physician referrals. Subjects provide written informed consent for future re-contact. Data generated and shared include family structure, detailed phenotypes, exome or short-read genome data, and in some cases long-read genome or RNA-sequencing, and these are shared upon completion of standard quality control checks and annotation. Broad Institute (Broad) The Broad Center for Mendelian Genomics, part of the GREGoR consortium uses next-generation sequencing (exome, genome, transcriptome, and long read sequencing), computational approaches, and functional studies to discover the variants and genes that underlie Mendelian conditions with a particularly focus on neuromuscular, neurodevelopmental, and syndromic phenotypes. Samples come from collaborators and direct enrollment through the Rare Genomes Project and we are committed to rapid data sharing without an embargo period. University of California, Irvine (UCI-GREGoR) To accelerate the pace of Mendelian disease gene discovery and clinical implementation, we propose a Mendelian Genomics Research Center, part of the GREGoR Consortium, leveraging the broad pediatric and adult clinical and research expertise at Children's National Hospital and University of California, Irvine. Our goal is to develop best practices to increase the diagnostic yield of rare diseases, engage the community to reduce health disparities for complex diagnoses, while creating a dataset accessible to all. Our Center will unite world class experts combining basic and translational research with innovative approaches to phenotyping, variant identification and functional investigation of both coding and non-coding sequence changes with the goals of discovering novel Mendelian gene variations and identifying variants not detected on current sequencing pipelines, disambiguating uncertain variants into disease-causing versus benign categorizations, and sharing information by working collaboratively with the GREGoR community.GREGoR Stanford Site (GSS) The goal of the GREGoR Stanford Site (GSS) is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease. Participants included individuals with undiagnosed suspected Mendelian disease who had non-diagnostic exome sequencing and their immediate family members. Participants and their family members provided written, informed consent and biological samples from which DNA, RNA, plasma, fibroblasts, PBMCs and other cell types were generated and stored. Samples from research participants and their immediate family members may have undergone short and long-read genome sequencing, transcriptome sequencing, metabolomics and/or lipidomics profiling, methyl-capture-sequencing and ATAC-sequencing. De-identified clinical data extracted from participant medical records are linked to the samples. University of Washington Center for Rare Disease Research (UW-CRDR) The goals of the University of Washington Center for Rare Disease Research are to: (1) maximize novel gene discovery for Mendelian conditions by recruitment, short- and long-read whole genome sequencing, transcriptome sequencing and analysis of families with rare conditions for which the gene is either unknown or the gene is known but no pathogenic variant can be identified via clinical testing; (2) develop new strategies for gene discovery for Mendelian conditions caused by variants that are difficult to detect using conventional testing strategies, variants of unknown function effect (e.g., regulatory, structural variants) or have unusual modes of inheritance; and (3) implement high-throughput screening and targeted follow-up functional studies to prioritize and validate candidate non-coding variants. De-identified data and phenotypic information are shared via MyGene2, ClinVar, and AnVIL.

  Study phs003047

• TSO500 STJAN33, BRAF mutated CUP

  The data set contains FASTQ files (filetype) of a NEXTSEQ550DX run (instrument). De FASTQ files are from DNA and RNA sample. The Library prep is a NGS TSO500 library prep (Illumina) (technology). A Study to Examine the Clinical Value of Comprehensive Genomic Profiling Performed by Belgian NGS Laboratories: a Belgian Precision Study of the BSMO in Collaboration With the Cancer Centre (BALLETT) This 2-year study involves the consortium of 9 cooperating Belgian NGS laboratories and will enroll 936 metastatic or locally advanced cancer patients coming from 13 different Belgian hospitals and cancer centers. Upon inclusion, all cancer patients will be offered 'comprehensive genomic profiling' (CGP) using Illumina's TSO500 NGS panel. This targeted NGS panel of 523 genes allows for the detection of single nucleotide variants, small indels, copy number variations and fusions, as well as for the determination of the 'tumor mutational burden' (TMB) and the 'microsatellite-instability' status (MSI). Both the wet lab execution of the CGP as well as the biological and clinical classification of the variants will be performed in a fully standardized way among the 9 participating Belgian local NGS laboratories.

  Dataset EGAD50000000689

• Myeloid cell programming in patients with non-medullary thyroid carcinoma

  This dataset contains single-cell RNA sequencing data from patients with thyroid cancer (n=7), multinodal Goiter (n=3) and healthy individuals (n=5). Mononuclear cells were taken from both the peripheral blood and the bone marrow compartments. We used a pooled single-cell design where multiple individuals were pooled in a single sample for sequencing (NextSeq 500-V2) and later demultiplexed using their genotype data. Associated metadata contains information on the phenotypes per individual, the pooling design and the linkage between the supplied files and sequenced pools. Due to limitations from EGA in uploading single-cell data, the raw fastq files were processed as follows: (i) I1/I2/R1/R2 fast files were concatenated over the different lanes. (ii) Concatenated I1 and I2 files were interleaved, as were the concatenated R1 and R2 files to generate two fastq files per pool containing all the information. To interleave the fastq files, the BBmap tool bbmap/reformat.sh was used, which can also be used to de-interleave the files.

  Dataset EGAD00001008108

• Phenotype Risk Scores Identify Patients with Unrecognized Mendelian Disease Patterns

  This study was designed to assess the phenotypic effects of rare variants. Rare variants are difficult to study in a high-throughput manner because most cohorts are underpowered to detect associations. In order to gain power to test rare variants, we use Phenotype Risk Scores (PheRS) based on features of Mendelian diseases. PheRS is calculated using claims data from an EHR that is mapped to clinical features from OMIM's clinical descriptions of Mendelian disease. We calculated PheRS for 1,204 Mendelians diseases in a cohort of 21,701 individuals genotyped on the HumanExome BeadChip. We then tested for association between these diseases and rare variants in causal genes. A phenotype risk score is calculated as the weighted sum of features for a given disease. The features are defined as a set of phecodes (consolidated ICD codes) associated with a particular Mendelian disease as described in OMIM. The weights for each phecode are calculated as the log inverse prevalence of the phecode in our cohort. For an individual, their score equals the sum of the weights for each phecode that is present in their medical record. Our study was based on a cohort of individuals with genotype data linked to de-identified electronic health records (EHR) from Vanderbilt's BioVU resource. We hope that this method will help generate phenotype-genotype correlation data on rare variants which in turn may inform rare variant interpretations use for diagnostics.

  Study phs001516

• Enhanced Adjuvanticity of a Personal Neoantigen Vaccine Generates Potent Neoantigen-Specific Immunity

  Personalized neoantigen-targeting vaccines can induce durable and diverse T cell responses and have been associated with improved outcomes. To augment immunogenicity, we tested a synthetic long peptide vaccine formulated with the adjuvants Montanide and poly-ICLC in 10 patients with melanoma. The vaccines, co-administered locally with ipilimumab and systemically with nivolumab, generated de novo ex vivo T cell responses against the majority of immunizing neoepitopes in all 9 fully vaccinated patients, and ex vivo CD8+ T cell responses in 6 of 9. Hundreds of circulating and intratumoral T cell receptor (TCR) clonotypes emerged following vaccination that were distinct from those arising after PD-1 inhibition. By linking vaccine-specificity and cellular phenotype of TCRs in post-treatment tumors at single cell resolution, we demonstrate remodeling of the intratumoral T cell repertoire following vaccination. These observations demonstrate the impact of multi-pronged immune adjuvanticity on the T cell repertoire in the context of neoantigen-targeting vaccines. We are submitting WES data from whole blood samples and tumor FFPE samples, and RNA-seq data from tumor FFPE samples. We are also submitting RNA-seq data from purified tumor samples. scRNA-seq data will also be submitted for skin samples (pre and post neoantigen vaccine dose #3), and tumor samples before starting treatment, before starting neoantigen vaccine Dose #1, and before neoantigen vaccine dose #4. Bulk TCR-seq data will be submitted for blood samples.

  Study phs003919

• Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis

  Targeted therapy interventions using tyrosine kinase inhibitors (TKIs) provide encouraging treatment responses in ALK-rearranged lung adenocarcinomas, yet resistances occur almost inevitably. Apart from tumor cell-intrinsic resistance mechanisms, accumulating evidence supports a role of cancer-associated fibroblasts (CAFs) in affecting the therapeutic vulnerability of lung cancer cells. Here, we aimed to investigate underlying molecular networks shaping the therapeutic susceptibility of ALK-driven lung adenocarcinoma cells via tumor microenvironmental cues using three-dimensional (3D) spheroid co-culture settings. We show that CAFs promote therapy resistance of lung tumor cells against ALK inhibition by reducing apoptotic cell death and increasing cell proliferation. Using single-cell RNA-sequencing analysis, we show that genes involved in lipogenesis constitute the major transcriptional difference between TKI-treated homo- and heterotypic lung tumor spheroids. CAF-conditioned medium and CAF-secreted factors HGF and NRG1 were both able to promote resistance of 3D-cultured ALK-rearranged lung tumor cells via AKT signaling, which was accompanied by enhanced de novo lipogenesis and supression of lipid peroxidation. Notably, simultaneous targeting of ALK and SREBP-1 was able to overcome the established CAF-driven lipid metabolic-supportive niche of TKI-resistant lung tumor spheroids. Our findings highlight a crucial role of CAFs in mediating ALK-TKI resistance via lipid metabolic reprogramming and suggest new ways to overcome resistance towards molecular directed drugs by targeting vulnerabilities downstream of oncogenic signaling.

  Study EGAS50000000135

• Whole genome sequencing with linked reads of pediatric glioblastoma samples

  Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. To understand mechanisms of tumor evolution in this cancer, we performed whole genome sequencing with linked reads on longitudinally resected pGBM samples. Our analyses showed that all diagnostic and recurrent samples were collections of genetically diverse subclones. Clonal composition rapidly evolved at recurrence, with less than 8% of non-synonymous single nucleotide variants being shared in diagnostic-recurrent pairs. In order to track the origins of the mutational events we observed in pGBM, we generated whole genome datasets for two patients and their parents. These trios showed that genetic variants could be (i) somatic, (ii) inherited from a healthy parent, or (iii) arose de novo in the germlines of pGBM patients. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. Interestingly, radiation and anti-mitotic chemotherapeutics did not increase overall tumor burden upon recurrence. These findings support an important role for slow-cycling stem cell populations in contributing to recurrences, since slow-cycling cell populations are expected to be less prone to genotoxic stress induced by these treatments and to accumulate few mutations. Our results highlight the need for new targeted treatments that account for the complex functional hierarchies and genomic heterogeneity of pGBM.

  Study EGAS00001003432

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Small variants in mtDNA Canary Islands - WGS Illumina (ITER)

  The mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short- read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

  Study EGAS00001005679

• Small variants in mtDNA Canary Islands - WES Illumina (ITER)

  The mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short- read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

  Study EGAS00001005678

• Small variants in mtDNA Canary Islands - WGS Oxford Nanopore Technologies (ITER)

  The mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short- read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

  Study EGAS00001005677

• eMERGE Network Imputed GWAS for 41 Phenotypes

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites (Cincinnati Children's Hospital Medical Center/Boston Children's Hospital, Children's Hospital of Philadelphia, Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University, Geisinger Clinic, Group Health Cooperative/University of Washington, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Vanderbilt University Medical Center) funded by the NHGRI to investigate the use of electronic medical record (EMR) systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 55,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of over forty phenotypes, including Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection, as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. Sites and participants include: Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic medical records (EMRs). EMRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): Cincinnati Children's Hospital Medical Center (CCHMC) and Boston Children's Hospital (BCH) are pediatric institutions dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. The CCHMC/BCH site has been built on a five-year history of collaboration, particularly in patient electronic record (ERM)-related informatics, the basis of much of eMERGE II. CCHMC and BCH together bring an extraordinary faculty to eMERGE II who are committed to diseases that afflict children, specifically phenotypes that focus upon diseases of children in ways that will leverage the available eMERGE adult GWAS and EMRs to discover meaningful use results. CCHMC/BCH plans to demonstrate real-time execution of phenotypic selection across their two distinct pediatric institutions as a model for ensuring phenotypic standardization and for national scalability. They will also look carefully at parents' responses to results and use of their children's research results and better understand the factors that influence their decisions about learning incidental findings. In addition to patient and parent perceptions CCHMC/BCH will also explore clinician perceptions of pharmacogenetic research results after EMR integration. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Group Health(GH)/University of Washington (UW): GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300.) Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF), or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if participants had any laboratory event of creatine kinase (CK) >1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Mayo Clinic: The Mayo biobank is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval; this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG , was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

  Study phs000888