259 results for ""independent+of+the+choice+of+kernel"+regularization+parameter+volumetric+rendering+paper"

in 12.77 milliseconds.

• Shallow whole genome sequencing DETECT samples

  Shallow whole genome sequencing of samples from the paper Beddowes et al. A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival. Mol. One 2025. This includes 1048 longitudinal plasma samples from 149 patients and 35 normals. Samples were sequenced with shallow whole genome sequencing for copy number estimation. All the files are single-end fastq files.

  Dataset EGAD50000001333

• Transcriptomic and chromatin accessibility profiling in T cells for the MTOR genetics paper.

  This submission contains raw ATAC and RNA sequencing data from human primary CD4+ and CD8+ T cells, either stimulated or unstimulated with anti-CD3/CD28 Dynabeads, from three healthy donors. It includes 24 paired-end FASTQ files consisting of 12 poly(A)-enriched RNA-seq samples and 12 ATAC-seq samples sequenced on the Illumina NextSeq 500 platform.

  Dataset EGAD50000001307

• He et al. WES data

  This dataset are the bam files of WES data from the paper by He et al.

  Dataset EGAD00001007134

• BCG Molecular Subtyping

  Dataset for the paper: Non-muscle Invasive Bladder Cancer Molecular Subtypes Predict Differential Response to Intravesical Bacillus Calmette-Guérin

  Dataset EGAD00001010065

• Megabase scale methylation phasing using Nanopore long reads and NanoMethPhase

  This paper describes the work by Akbari V,. et al. on detection of allele specific methylation using oxford nanopore sequencing data. They have developed set of tools, SNVoter and NanoMethPhase, and workflow which enable the detection of allele specific methylation even in samples with sparse coverage of nanopore sequencing data.

  Dataset EGAD00001007920

• RNA-sequencing of six Pilocytic astrocytoma tumors

  Total stranded TruSeq RNA sequencing by Illumina of six tumor samples from six cases of pediatric Pilocytic astrocytoma. The data is published in the following paper: Tomic TT, Olausson J, Wilzen A, Sabel M, Truve K, Sjogren H, Dosa S, Tisell M, Lannering B, Enlund F, Martinsson T, Aman P, Abel F. A new GTF2I-BRAF fusion mediating MAPK pathway activation in pilocytic astrocytoma. PLoS One. 2017 Apr 27;12(4):e0175638.

  Dataset EGAD00001003143

• Clonal cultures of T memory cells

  Sequence data (bam files) of two RRBS samples for paper "A comprehensive analysis of 195 DNA methylomes reveals shared and cell specific features of partially methylated domains". Short Description: CD4+ T memory cells (CD3+ CD4+ CD45RA- CD45RO+ CD25-) from donors were sorted by flow-cytometry either as a bulk culture ('ex vivo' sample) or in a single-cell format into 96 well-plates ('clone' sample) in the presence of a TCR stimulus.

  Dataset EGAD00001004303

• Data for Paper: Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  mRNA, total RNA, small noncoding RNA, NOMe-Seq and DNase-Seq data from following samples (not every Sequencing Type for every sample): 01_HepG2_LiHG_Ct1 41_Hf01_LiHe_Ct 41_Hf02_LiHe_Ct 41_Hf03_LiHe_Ct 51_Hf03_BlCM_Ct 51_Hf04_BlCM_Ct 51_Hf03_BlEM_Ct 51_Hf04_BlEM_Ct 51_Hf03_BlTN_Ct 51_Hf04_BlTN_Ct Metadata available at deep.dkfz.de

  Dataset EGAD00001002735

• Facial Skin Biophysical Multi-Parameter and Microbiome-Based Korean Skin Cutoype (KSC) Determination

  In this study, we conducted an integrated analysis of skin measurements, clinical BSTI surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical association. By utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups based on their age distribution. We established strong connections between 15 core genera and the four KSC types within the three aging groups, revealing three prominent clusters of the facial skin microbiome. Together with skin microbiome variations, skin tone/elasticity distinguishes aging groups while oiliness/hydration distinguishes individual differences within aging groups. Our study provides prospective reality data for customized skin care based on the microbiome environment of each skin type.

  Study EGAS00001007334

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• Human brain development single cell sequencing additional samples

  69 tissue samples from various parts of the developing human embryo brain were dissociated and single cells were collected and processed without bias for mRNA-seq using 10X chromium 3' protocol. Libraries were sequenced on Illumina NovaSeq and reads aligned against the human GRCh38 genome. This is an addition to EGAD00001006049 Human development single cell sequencing, consisting of samples added after the original submission to EGA, but included in final paper.

  Dataset EGAD50000001295

• WGS data for Pancreatic Cancer samples (resection cohort)

  WGS data from pancreatic cancer samples. These are part of a resected cohort from a tandem duplicator phenotype paper (Farooq et al, NPJ Precision Oncology, 2025). The dataset includes tumour and normal BAM files sequenced on Illumina HiSeq/NovaSeq and aligned against GRCh38 (n=189 tumour/normal pairs).

  Dataset EGAD50000001834

• ctDNA quantification in Ewing sarcoma patients

  Background. Treatment stratification and response assessment in pediatric sarcomas has relied on imaging studies and surgical/histopathological evidence of vital tumor cells. Such studies and evidence collection processes often involve radiation and/or general anesthesia in children. Cell-free circulating tumor DNA (ctDNA) detection in blood plasma is one available method of so-called liquid biopsies that has been shown to correlate qualitatively and quantitatively to some extent with the existence of vital tumor cells in the body. Our clinical observational study focused on the utility and feasibility of ctDNA detection in pediatric Ewing sarcoma (EWS) as a marker of minimal residual disease (MRD). Patients and Methods. We performed whole genome sequencing to identify the exact breakpoints in tumors known to carry the EWS-FLI1 fusion gene. Patient-specific fusion breakpoints were tracked in peripheral blood plasma using digital droplet PCR (ddPCR) before, during, and after therapy in six consecutive children and young adults with EWS. Presence and levels of fusion breakpoints were correlated with clinical disease courses. The genetic changes in the tumors were defined, and inter- and intra-individual changes in the tumor DNA copy loads were observed over time. Results. We show that the detection of ctDNA in the peripheral blood of EWS patients i) is feasible in the clinical routine and ii) allows for the longitudinal real-time monitoring of MRD activity in children and young adults. Although changing ctDNA levels correlated well with clinical outcome within patients, between patients, a high variability was observed (inter-individually) for the six patients described here. Conclusions. ctDNA detection by ddPCR is a highly sensitive, specific, feasible, and highly accurate method that can be applied in EWS for follow-up assessments as an additional surrogate parameter for clinical MRD monitoring and, potentially, also for treatment stratification in the near future.

  Study EGAS00001006433

• De Novo Characterization of Cell-Free DNA Fragmentation Hotspots in Plasma Whole-Genome Sequencing

  The raw sequencing data deposited here are low-coverage (~1X) whole-genome sequencing (WGS) of plasma cell-free DNA (cfDNA). The purpose is to validate the performance of early-stage cancer diagnosis in the Zhou et al. 2020 BioRxiv paper, mainly for two types of cancer: early-stage liver cancer and breast cancer. The cases and controls are matched with age, gender, smoking history, and alcohol usage. We developed a computational method to identify the cfDNA fragmentation hotspots from pooled low-coverage cfDNA WGS data. We found the cfDNA fragmentation hotspots are highly enriched in regulatory regions, such as open chromatin regions. The signals from these regions can help the diagnosis of early-stage cancers and their tissues-of-origin.

  Study phs003062

• Hyperdiploid Acute Lymphoblastic Leukemia RNA-Seq

  Few genetic drivers of children with hyperdiploid precursor B-cell acute lymphoblastic leukemia (ALL) have been identified to date. In an effort to detect novel genomic rearrangements that could be promoting leukemogenesis in this subset of patients, we sequenced ribosomal RNA-depleted transcriptomes isolated from 5 hyperdiploid acute lymphoblastic leukemia samples. In addition to looking for novel genomic rearrangements, we also sought to identify transcripts that could be created as a result of a novel posttranscriptional process. Surprisingly, we identified transcripts created as the result of exon circularization. The results of our studies are present in the paper "Circular RNAs Are the Predominant Transcript Isoform from Hundreds of Human Genes in Diverse Cell Types" published in PLoS ONE (PMID: 22319583).

  Study phs000522

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• Single-cell transcriptomics of PBMC’s from healthy, acute decompensated (AD) and acute chronic liver failure (ACLF) patients.

  This dataset contains the raw sequencing data (fastq-files) of 10 sample libraries. These are 10x genomics single-cell transcriptome libraries of human peripheral blood mononuclear cells (PBMC’s). The samples are taken from healthy, acute decompensated (AD) and acute chronic liver failure ACLF patients. The sequencing was performed on an Illumina NovaSeq6000. This data are related to the paper: "Distinct immunometabolic signatures in circulating immune cells define disease outcome in acute-on-chronic liver failure"

  Dataset EGAD50000000574

• subset of DEEP IHEC release 2016 (EGAS00001001937), as used in EGAS00001001656 (bidirectional promotors paper, Fatemeh et al. 2018)

  part of the DEEP project results resulted in the publication of 'Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters', Epigenetics & Chromatin (2018), Fatemeh et al., DOI: 10.1186/s13072-018-0236-7, PMID: 30414612, PMCID: PMC6230222. This dataset contains the subset of DEEP data related to that study.

  Dataset EGAD00001005953

• Small RNA-sequencing and RNA-sequencing data of tuberous sclerosis complex subependymal giant cell astrocytomas

  This data set contains small RNA-sequencing and RNA-sequencing data from subependymal giant cell astrocytomas (SEGA) resected from tuberous sclerosis complex patients. Small RNA-sequencing and RNA-sequencing were performed on the same set of SEGAs (n=19) and periventricular controls (n=8). For full details on library preparation and patients please refer to the paper "The coding and non-coding transcriptional landscape of subependymal giant cell astrocytomas." (PMID: 31834371 DOI: 10.1093/brain/awz370).

  Dataset EGAD00001005932

• Synthetic data - Genome in a Bottle

  In May, the National Institute of Standards and Technology (NIST) released its first genome in a bottle, a reference sample of DNA for validating human genome sequences. This so-called truth sequence comes from a decades-old sample donated by a Utah woman for (other) research purposes (NA12878 cell line), which, over the years, has been one of the most studied, and hence best-characterized, human samples. Seeing genomic medicine moving toward mainstream healthcare, researchers at NIST recognized the need for a reference human genome and assembled a private-public consortium in 2012 to create one. As detailed in a 2014 Nature Biotechnology paper (Nat. Biotechnol.32, 246–251, 2014), the group integrated and arbitrated among sequences from 14 data sets, five sequencing technologies, seven read mappers and three variant callers.

  Study EGAS00001005591

• Ulcerative Colitis Human Microbiome Project (UCHMP)

  The aims of the multi-center Ulcerative Colitis Human Microbiome Project (UCHMP) are to examine the role of the enteric microbiome in causing human ulcerative colitis, specifically the development of pouchitis. Pouchitis is an inflammatory condition of the surgically-created ileoanal pouch that serves as a pseudo-rectum in patients with ulcerative colitis who have undergone a total colectomy. It is a condition unique to ulcerative colitis (UC), as it rarely occurs in non-UC patients who have the same procedure. Within one year, about 50% of patients will develop pouchitis. The condition is almost certainly due to aspects of the pouch microbiota on a background of genetic susceptibility, as most patients respond to treatment with antibiotics. While there have been reports on the microbiota in pouchitis patients, all have been performed after the inflammatory process is initiated, rendering interpretation of the results difficult, as the inflammatory process itself will change the microbiota. Our project is therefore unique and possibly the only opportunity in the role of the enteric microbiome in IBD in a prospective manner, thereby establishing potentially important causal relationships between microbiota structure or function and development of UC.The studies also offer two additional advantages. First, the development of the pouch microbiota can be observed prospectively. Second, the cause of antibiotic treatment failure in some patients with pouchitis may be revealed. The two aims are (1) to identify causal factors in the structure and/or function of the enteric microbiota in the development of pouchitis, and (2) to determine the basis of treatment failure in UC pouchitis patients. Our analyses will include serial measurements of enteric microbial structure (16S rRNA gene-based), function (metagenomics and functional candidate genes), and cultivation, the latter to enhance interpretation of gene sequences derived from metagenomes and targeted gene surveys (either functional or 16S genes). The insights gained from these studies will help us understand the fundamental and causative roles of the enteric microbiome in human inflammatory bowel diseases (IBD). This information will be the basis for developing strategies to restore host-microbial relationships to prevent and treat IBD.

  Study phs000262

• Multi-omic and functional analysis for classification and treatment of sarcomas with FUS-TFCP2 or EWSR1-TFCP2 fusions(H021/INF)

  Many rare cancers are not well understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. Leveraging two (inter)national precision oncology trials that enroll large numbers of rare cancers, we investigated the clinical, histopathologic, molecular, and functional characteristics of rhabdomyosarcoma (RMS) with fusions of FUS or EWSR1 to the TFCP2 transcription factor, a recently discovered ultra-rare entity whose classification, pathogenesis, and optimal treatment are unclear. Unusually for fusion-driven sarcomas, most cases had highly rearranged genomes, including chromothripsis, and signs of defective homologous recombination DNA repair. All tumors showed extreme expression of a truncated TERT variant and the ALK receptor tyrosine kinase, which was additionally affected by intragenic deletions and aberrant splicing, resulting in the expression of shortened variants in 58% of cases. Three ALK variants were oncogenic in immortalized cells, and patient-derived tumor cells expressing two variants responded to certain ALK inhibitors. Other recurrent alterations included CDKN2A/MTAP co-deletions and mutations in PAPPA2, encoding an IGFBP5-specific proteinase, in 67% and 25% of cases, respectively. DNA methylation analysis, along with 19 other soft-tissue sarcoma classes, revealed a close relationship with undifferentiated sarcoma but not other RMS subtypes, suggesting that TFCP2-rearranged RMS is a separate entity, possibly arising from a distinct cell of origin. Expression of TFCP2 fusions in immortalized human cells blocked late myogenic differentiation and significantly induced genes that were also highly expressed in patient tumors, including ALK, TERT, and two known regulators of skeletal muscle cells, IGFBP5 and PTH1R. Genome-wide chromatin profiling demonstrated direct binding of FUS-TFCP2 to the ALK and TERT loci outside their regular promoters, which correlated with the expression of alternative transcript variants. Finally, FUS-TFCP2 inhibited the repair of DNA double-strand breaks in immortalized cells, rendering them sensitive to treatment with cisplatin. This study provides insight into the pathogenesis and therapeutic vulnerabilities of a new RMS subtype and illustrates the value of linking comprehensive molecular diagnostics and functional annotation within multicenter consortia to improve the understanding and clinical management of rare cancers.

  Study EGAS00001006939

• WGS data for ATLAS paper (123 patients)

  WGS data of multi-region samples from PLANET 123 Patient cohort

  Dataset EGAD00001009858

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Cardiology Biobanking for Biomarker Discovery

  This study is a part of NHGRI's Center for Common Disease Genomics, which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. Atrial fibrillation will affect between 6-12 million individuals in the US by 2050. AF also is associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with >20,000 individuals with AF and >100,000 without AF. In the latest analyses, 14 loci have been identified for AF1. Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules.Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdfThe BioHEART study is a multi-center, prospective, observational cohort study to identify new biomarkers in stable coronary artery disease (CAD) and acute myocardial infarction (MI) (ACTRN12618001322224). Participants are recruited at time of clinically indicated computed tomography (CT) coronary angiogram (CTCA). 1,950 of 5,000 planned participants have been recruited and serum/plasma/erythrocytes/monocytes stored, with follow-up ongoing. CTCA imaging studies provides volumetric quantification of total calcified and non-calcified plaque, which is assessed using established and novel scoring systems. Comprehensive molecular phenotyping is performed on blood samples using state-of-the-art genomic, transcriptomic, proteomics, metabolomic, lipidomic, and immunophenotyping. Outcome data will be used to build improved risk models for CAD.Reference: http://www.anzctr.org.au/ - ACTRN12618001322224

  Study phs002726

• Sex Chromosome Aneuploidy Effects on Human Gene Expression

  Sex chromosome aneuploidy (SCA) syndromes are defined by the presence of a sex chromosome complement other than the typical XX for females and XY for males. SCAs are collectively common and increase risks for diverse immune, metabolic, and neuropsychiatric illnesses. Understanding genome-wide transcriptomic changes in SCAs is critical for clarifying the biological underpinnings of their clinical phenotypes. Assessments of sex chromosome dosage (SCD) effects on gene expression in humans have often been confined to the use of a few SCA subtypes (e.g., XO or XXY) - precluding analysis of the same SCD change in different contexts. Our study used RNA-seq data from lymphoblastoid cell lines in 197 individuals representing diverse SCA karyotypes (49 XX, 24 XXX, 39 XY, 39 XXY, 23 XXYY, 23 XYY) to assess expression changes for 25,075 genes in multiple pairwise SCD group comparisons. The paper reporting our findings has been cited below.

  Study phs003278