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• scEC&T-seq manuscript data

  This dataset includes all data produced in the study describing "scEC&T-seq", a method for parallel sequencing of extrachromosomal circular DNA and transcriptome in single cells. This dataset includes: - Illumina scEC&T-seq Circle-seq data (scCircle-seq) for a total of 626 single cells / nuclei - bam files - Illumina scEC&T-seq RNA-seq data (scRNA-seq-Illumina) for the same single cells / nuclei - bam files - Nanopore scCircle-seq data for 18 single cells - bam files - Nanopore bulk WGS for 2 cell lines and 2 primary tumor samples - bam files - Illumina bulk WGS for 2 cell lines - bam files - Illumina bulk Circle-seq data from 1 cell line - bam file - Illumina ChIP-seq H3K27me3 data from 1 cell line - fasta files + peaks bed file + coverage bw file

  Dataset EGAD00001010071

• Digital tEchnology For Lung Cancer Treatment

  The aim of the DEFLeCT project (Digital tEchnology For Lung Cancer Treatment) is to generate a technological platform for precision medicine in order to set predictive personalised algorithms for patient diagnosis and therapy. All resectable patients having histologically confirmed stage IB-IIIA non-small cell lung cancer(NSCLC) will be enrolled in the PROMOLE study, a related and ongoing observational prospective clinical study, for tissue sampling. A large biobank of lung cancer samples and the corresponding healthy tissues and biological components (ie, blood, stools, etc.) with complete clinical, pathological and molecular information will be collected. The project will lay the basis of a knowledge hub and qualified technology aimed not only at answering the medical and scientific community's questions, but also meant to be useful to individual patients by predicting the response to adjuvant and second-line drugs in case of relapse of the disease.

  Study EGAS00001007219

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844

• 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

• Alpha1-Antitrypsin Deficiency Registry (AADR-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: To characterize the clinical and laboratory course of patients with severe alpha 1-antitrypsin deficiency whether or not the patient is undergoing long-term augmentation therapy.Background: A hereditary disorder, patients with low serum levels of alpha-1-antitrypsin are at an increased risk for the early onset of emphysema. The only approved treatment for alpha-1-antitrypsin deficiency is augmentation therapy using a purified preparation of human alpha-1-antitryspin. Sample sizes for a randomized controlled clinical trial of augmentation therapy were determined to be infeasible; therefore, a multi-center registry was initiated in 1988 to explore the natural history of the disease and the relative efficacy of augmentation therapy in patients with a severe deficiency of alpha-1-antitrypsin.Participants: Eligible participants included individuals 18 years of age or greater for whom the Central Laboratory confirmed that the serum alpha 1-antitrypsin level is Conclusions: Participants receiving augmentation therapy had decreased mortality risk during follow-up. FEV1 decline among all participants did not differ by augmentation therapy; however, among participants with FEV1 35-49% predicted, FEV1 decline was significantly slower for participants on augmentation therapy than for those not receiving therapy. (Am J Respir Crit Care Med, 1998; 158:49-59)

  Study phs004187

• DAC for BillionToOne

  This is the Data Access Committee for studies published by BillionToOne.

  Dac EGAC50000000418

• APL nanopore sequencing

  Nanopore sequencing for an APL patient

  Study EGAS00001005618

• Transdisciplinary Studies of Genetic Variation in Colorectal Cancer(CORECT): Axiom GWAS

  This study is part 1 of 2 for Schumacher et al, Nat Commun. 2015 Jul 7;6:7138. Part 1 is the original GWAS data for 9,259 participants on the Affymetrix Axiom platform of the studies listed below in the Inclusion Criteria.

  Study phs001856

• A Genomic Approach to Improved Diagnosis and Treatment of Neuroendocrine Tumors

  These are RNA-Seq samples from patients undergoing surgery for small bowel and pancreatic neuroendocrine tumors. Normal tissue, primary tumors, lymph nodes, and liver metastases were collected at surgery and stored in RNAlater until RNA extraction was carried out for RNA-Seq.

  Study phs001772

• Comprehensive analysis of the abnormality of the genes in juvenile myelomonocytic leukemia

  To develop a simple and robust methylation test for risk stratification of patients with juvenile myelomonocytic leukemia. We used the DREAM method, a method for whole genome methylation analysis using NGS, to analyze methylation profiling of patients with juvenile myelomonocytic leukemia.

  Study JGAS000292