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• A Microwell Platform for High-Throughput Longitudinal Phenotyping and Selective Retrieval of Organoids

  In traditional bulk culture, organoids tend to overlap, hindering the analysis of individual organoid characteristics in a high-throughput manner. Additionally, variations in the local spatial properties of the bulk matrix make it challenging to distinguish whether phenotypic differences between organoids stem from inherent cellular disparities or disparities in the microenvironment. To address these challenges, we have developed a microwell-based technique that facilitates the quantification of image-based parameters for organoids grown from single cells. Furthermore, these organoids can be easily retrieved from their respective microwells for molecular profiling. By employing a deep-learning image processing pipeline, we conducted a comprehensive assessment of various phenotypic traits in two CRISPR-engineered human gastric organoid models. This analysis encompassed growth rates, cellular movement, and apical-basal polarity. Through our investigation, we successfully identified genomic alterations associated with increased growth rate, as well as changes in accessibility and expression patterns correlated with apical-basal polarity. This novel methodology offers a valuable tool for studying organoid behavior at a single-cell level, enabling researchers to gain deeper insights into organoid biology and associated genetic modifications.

  Study phs003315

• MutWP5: CRUK Mutographs of Cancer: Lung: PD37920 (Targeted) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006929

• Exome sequencing data for the DIRECT study.

  This dataset contains all available fastq files for gemline, baseline plasma/biopsy, and response assessment patient samples for the DIRECT study. The total number of samples was 729 (188 germline, 190 baseline plasma, 159 baseline biopsy, and 192 response assessment). The dataset files were generated from targeted (exome) Illumina sequencing performed on paired biopsy tissue and plasma cell free DNA.

  Dataset EGAD50000001409

• Sequencing data for the Genomic Autopsy Study

  The .cram files of the Trio or Quad sequencing data used for generation of the genomic autopsy study. This contains a mix of genome sequencing and exome sequencing data for probands and their parents. A subset of families (n=32) did not provide consent to publicly sharing their data.

  Dataset EGAD00001009737

• Roifman DAC

  This Data Access Committee will oversee all requests for data submitted by Dr. Chaim Roifman at the Hospital for Sick Children in Toronto, Canada. Requests should be directed by email to chaim.roifman@sickkids.ca.

  Dac EGAC50000000396

• Genomic Alterations in Normal Breast Tissues Preceding Breast Cancer Diagnosis (GANPBC)

  This study was designed to examine the genomic alterations in histologically normal breast tissues of women who subsequently developed breast cancer and compare it to breast tissues from women who did not. Breast core needle biopsy tissues from 160 healthy women at the Susan G. Komen Tissue Bank were requested for this study and sequenced using whole exome sequencing. The samples included tissues from 79 participants who, during the annual follow-up with Susan G. Komen self-reported a subsequent diagnosis of invasive or non-invasive breast cancer after tissue submission; these cases were classified as pre-diagnosis normal tissues. The remaining 81 samples were from women who had no cancer diagnosis at their last follow-up and were classified as healthy control samples. Among the pre-diagnosis samples, the average time from tissue donation to breast cancer diagnosis was 4.7 years (range: 0-11). For the healthy controls, the average time from tissue donation to the last follow up was 6.1 years (range: 0-14).

  Study phs003822

• Identification and Characterization of Cancer Mutations in Japanese LungAdenocarcinoma without Sequencing of Normal Tissue Counterparts

  We analyzed whole-exome sequencing data from 97 Japanese lung adenocarcinomapatients and identified several putative cancer-related genes and pathways. Particularly, we observed that cancer-related mutation patterns were significantly different between different ethnic groups. As previously reported, mutations in the EGFR gene were characteristic to Japanese, while those in the KRAS gene were more frequent in Caucasians. Furthermore, during the course of this analysis, we found that cancer-specific somatic mutations can be detected without sequencing normal tissue counterparts. 64% of the germline variants could be excluded using a total of 217 external Japanese exome datasets. We also show that a similar approach may beused for other three ethnic groups, although the discriminative power depends on the ethnic group. We demonstrate that the ATM gene and the PAPPA2 gene could be identified as cancer prognosis related genes. By bypassing the sequencing of normaltissue counterparts, this approach provides a useful means of not only reducing the time and cost of sequencing but also analyzing archive samples, for which normal tissue counterparts are not available.

  Study JGAS000001

• Accumulation of copy number alterations and clinical progression across advanced prostate cancer

  The burden of copy number alterations positively associated with radiologically-evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher burdens. This association between copy number burden and outcome was similar in each of the four metastatic states. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1X10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC, occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov–Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumor regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037).

  Study EGAS00001006251

• WTCCC3_Anorexia_Nervosa

  As part of the Genetic Consortium for Anorexia Nervosa (GCAN) and Wellcome Trust CaseControl Consortium 3 (WTCCC3), we have amassed the largest anorexia nervosa (AN)sample in the world (~4,000). Following the WTCCC3 GWAS, we secured funding from theKlarman Family Foundation to extend the genetic analyses to variants on the exome chip(CoreExome array). This pre-lim relates to carrying out genotyping on the CoreExome arrayon up to a maximum of 580 new samples.

  Study EGAS00001000913

• Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  This study aims to evaluate the efficacy of the combination of olaparib, durvalumab, and fulvestrant for the treatment of patients with ER-positive, HER2-negative, locally advanced or metastatic breast cancer with BRCA gene alterations or alterations of genes involved in HRR or MSI status, in terms of the progression-free survival rate (PFSR) at 24 weeks.

  Study EGAS50000001166

• Genetic architecture of autism spectrum disorder in India

  The dataset consists of whole exome sequencing data (fastq format) of 100 non-syndromic autism spectrum disorder patients from India. Whole exome sequencing data is generated using Agilent SureSelect v6 capture kit and Illumina HiSeq sequencing platform. Paired end fastq files are available.

  Dataset EGAD00001008621

• Center for Common Disease Genomics (CCDG)-Cardiovascular: University of Pennsylvania Cohort

  The National Human Genome Research Institute (NHGRI) has funded the Centers for Common Disease Genomics (CCDG), a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes and to better understand the general principles of genomic architecture underlying common, complex inherited diseases. This study of early onset cardiovascular disease and metabolic risk contains data derived from the whole genome sequence data of individuals participating in Penn Medicine's BioBank.

  Study phs001502

• Allogeneic induced pluripotent stem (iPS) cell-derived cartilage transplantation for articular cartilage damage in knee joints: a phase I, first-in-human study

  Damaged articular cartilage seldom heals due to a limited capacity for self-repair, frequently leading to osteoarthritis. Regenerative interventions are expected to heal the damage. We have recently established a method to produce cartilage from induced pluripotent stem cells (iPSCs) in a clinically applicable process. The primary aim of this study was to evaluate the safety including tumorigenesis and the subsequence efficacy of allogeneic iPSC-derived cartilage transplantation into damaged areas of knee articular cartilage.

  Study EGAS50000001167

• Genetic vulnerability of knockout cancer lines (2019-04-01)

  Genome-wide CRISPR/Cas9 library screen was performed in isogenic cell lines. Three biological replicates were used. Cells were harvested at initiation of screen and at specific time points following cell culture. Using this approach we aim to identify genes specifically important in cell survival in engineered cell lines. Please perform 72, instead of 36, PCR reactions as the screen performed at 200x coverage. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004881

• Immunophenotyping in a COVID-19 Cohort (IMPACC) Transcriptomics and Genotyping Assays

  IMPACC (IMmunoPhenotyping Assessment in a COVID-19 Cohort) is a prospective longitudinal cohort study (NCT04378777) launched in May 2020 in response to the worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was funded by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health (NIH). Patient enrollment and sample analysis involved 20 hospitals associated with 15 U.S. biomedical research centers and 11 centralized Core immunoassay laboratories.The study aimed to enable the identification of potential biomarkers and inform future therapeutic interventions using a longitudinal approach encompassing the whole disease course for a comprehensive understanding of the contributions of the pathogen and host immune response in modulating the manifestations, severity, and post-acute sequelae of the infection. IMPACC enrolled close to 1200 adult symptomatic, molecularly confirmed hospitalized COVID-19 patients in the study within 48 hours of their hospital admission. Clinical data and biological samples (blood, mid-turbinate nasal swabs, and esophageal aspirant) were collected according to minimal risk guidelines at enrollment and on days 4, 7, 14, 21, and 28. Biological samples were collected at 3-month intervals up to 12 months after hospital discharge to assess functional and immunologic recovery measures. Sample collection, processing, and storage procedures were standardized across sites, and samples were transported to centralized assay-specific core laboratories (Core Labs) in batches for testing and analysis. The description of outcome severity was not limited to survival versus death but encompassed a clinical trajectory approach using 5 groups leveraging longitudinal data based on time in the hospital, disease severity by ordinal scale based on the degree of respiratory illness, and presence or absence of limitations at discharge. Disease severity was assessed based on the degree of respiratory illness using a 7-point ordinal scale adapted from the World Health Organization COVID-19 and NIAID disease ordinal severity scales. DNA for genetic analyses was collected from whole blood at a single time point. To identify any genomic determinants of severe COVID-19 disease, single-nucleotide polymorphism (SNP) genotyping and exome sequencing were conducted to assess genetic variants associated with individual susceptibility to severe disease. In addition, IMPACC included bulk transcriptomic analysis of upper airway (nasal swaps) samples, lower airway (esophageal aspirant) samples, and peripheral blood mononuclear cells (PBMCs) to evaluate dynamic changes in cellular composition and cellular response during the course of the disease. Reads were aligned to the GRCh38 human reference genome. Additional information for the IMPACC study has been submitted to The Immunology Database and Analysis Portal (ImmPort) under study accession SDY1760. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dr. Steven Kleinstein (steven.kleinstein@yale.edu).

  Study phs002686

• Imputation analysis file

  This data set includes the following summary level data file used for the imputation data: imputation.sv.assoc.txt: results from single variant association analysis in imputed samples

  Dataset EGAD00010001184

• sQTL summary statistics

  This dataset contains the sQTL summary statistics (nominal pass, significant sQTLs). sQTL mapping was performed with QTLtools. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity.

  Dataset EGAD00001005042

• Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

  This dataset contains Linked-Read Whole Exome Sequencing (lrWES) from individuals with known disease-causing variants. The dataset comprises of 30 samples from 10 donors, where multiple samples from the same donor reflect experimental differences assaying the effect of input DNA length on coverage and phasing. Raw data (i.e. BAM files) and variant analysis (i.e. VCF files) for each sample are included in this dataset.

  Dataset EGAD00001004319

• Whole Exome Sequencing in Familial Parkinson Disease

  We have an ongoing study that has recruited families having at least a sibling pair with Parkinson disease (PD). Families have been screened for mutations in known PD causative genes (LRRK2, parkin, etc). Following review of families without a causative mutation, we selected families for whole exome sequencing that had the strongest history of PD and with the most definitive diagnosis. All families have at least 3 affected family members who were evaluated as part of this study. Only affected family members were included for whole exome sequencing.

  Study phs000376

• Deep Sequencing of 3 Cancer Cell Lines on 2 Sequencing Platforms (Illumina HiSeqX and NovaSeq)

  To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278X coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. This dataset can be used for benchmarking purposes by the genomic community and as a reference call set for scientists using these 3 cell lines.

  Study phs001839

• Molecular Characterization of ETMRs

  ETMRs are poorly characterized embryonal brain tumors with a dismal outcome. We collected tumors from 184 ETMR patients and 23 matched recurrences to investigate the genomic landscape of this distinct entity. Tumors without C19MC amplification, the proposed driver, harbor other miRNA related aberrations like DICER1 mutations and amplifications of the MIR17-92 miRNA cluster. All tumors behave molecularly similar and no subgrouping was observed. Whole-genome sequencing revealed an overall low recurrence of SNVs, but highly prevalent R-loop associated genomic instability. Finally, we show that targeting R-loops with TOP1 and PARP inhibitors might be an effective treatment strategy for this deadly disease.

  Study EGAS00001003256

• Single cell exome sequencing of lung adenocarcinoma

  Single cell exome sequencing was performed on 200 single cells isolated from a lung adenocarcinoma, female, non-smoker patient. The cells were derived from two distant tumour sectors and a far normal sector. The single cells were amplified on the Fluidigm C1 machine using the GE Healthcare illustra GenomiPhi V2 DNA Amplification Kit prior to sequencing library preparation. In addition, bulk exome sequencing was also conducted for each tumour sectors. For this study, somatic variations were detected from the single cells and phylogenetic clustering was conducted to elucidate the evolutionary trajectory of lung cancer.

  Study EGAS00001002972

• Base modification analysis using single molecule real-time sequencing

  Based on the kinetic characteristics of single-molecule real-time sequencing, we implement a method to directly detect DNA modifications such as 5mC. However, there are room to further improve the accuracy of whole-genome-wide DNA modifications. Therefore, this study aims to utilize deep learning methods for the comprehensive profiling of multiple DNA modifications, including 5mC, 5hmC, and 6mA, using SMRT-seq.

  Study EGAS50000000366

• NIHR BioResource Common Disease Patients 2016

  NIHR BioResource Common Disease Patients 2016. The dataset includes 13489 samples from blood donors, they were not screened for any particular disease, and therefore they are representative of the general population. Genomic data includes 845487 snps collected using the UK BioBank V1 Affymetrix array. Phenotypic data includes gender, age, ethnicity and disease. According to our internal quality check there are 81 duplicates in this dataset.

  Study EGAS00001004870

• Research Study into The Molecular Genetics of Hereditary Neuropathies

  Identification of genes implicated in inherited neuropathy through collecting and sequencing multiple generation families with inherited neuropathy. Patients in this study have been evaluated for demyelinating peripheral neuropathy as determined by decreased motor nerve conduction velocities in the sural nerve. Patients have also been screened for mutations in all genes implicated in demyelinating peripheral neuropathy and are expected to have previously unidentified mutations. Exome (WES) sequencing for two patients affected with inherited neuropathy. VCF files with called variants for each de-identified patient have been submitted.

  Study phs001351