Search Results - EGA European Genome-Phenome Archive

Genetic screening of GPI-anchor protein synthesis

This study will analyse the guide sequence which were used for making mutations in the Cas9-expressing cells. We used GeCKO v2 library which were released by Feng Zhang, 2014. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Dataset EGAD00001001928

RNA expression profiling of melanoma patient-derived xenograft

Patient-derived xenografts (n=96) were derived from metastatic melanoma patients. RNA expression profiling will be preformed to study 1. HLA-typing and 2. the effect of the tumour microenvironment on tumour growth This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Dataset EGAD00001002230

Whole Genome Sequencing of INTERVAL (2019-06-12)

15x Whole Genome Sequencing of 15,000 individuals from the INTERVAL study cohort, phase III. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-06-12.

Dataset EGAD00001005086

Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

Dataset EGAD00001005724

Genomic analysis of pancreatic neuroendocrine tumour with MEN1, ATRX, or DAXX mutations

This dataset includes 52 samples from 19 individuals with pancreatic neuroendocrine tumours. These samples were analyzed using RNA-sequencing, whole-exome sequencing, shallow (~0.3x) whole-genome sequencing, and a 21-gene panel targeted capture sequencing. Everything was sequenced using the Illumina HiSeq and is provided in BAM or FASTQ format.

Dataset EGAD00001006001

Whole Exome Sequencing of High grade T1 non-muscle invasive bladder cancer

The dataset consists of a multisample VCF (version 4.1) and the corresponding annotated MAF file, containing the somatic point mutations found from exome sequencing across the high-grade T1 bladder cancer cohort (HGT1, n=61 samples). The VCF file is in accordance with the HTS format specifications (https://samtools.github.io/hts-specs/). The dataset comprises also a CSV file with clinical data.

Dataset EGAD00001006346

PIAMA nasal RNAseq dataset

This study included nasal gene expression data collected from nasal brushes of adolescents in PIAMA birth cohort, which is used in the project Nasal DNA methylation at three CpG sites predicts childhood allergic disease. 186 samples were included in this analysis, and phenotypes (age and sex) were provided together with a gene expression count table. Gene expression was measured by the Illumina HiSeq2500 sequencer.

Dataset EGAD00001008767

Chromatin accessibility (ATAC-seq) of human acute leukemias and healthy donors

ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). ATAC-seq of CD34+ HSPCs from 3 healthy donors is also included.

Dataset EGAD00001011050

47 urothelial cancer patients WES and 38 RNAseq

We performed a global mutational landscape analysis using tumor samples from the 47 urothelial cancer patients included in MATCH-R or MOSCATO studies, with advanced metastatic disease and WES available (RNAseq is included for 38 patient samples).

Dataset EGAD00001011063

Targeted DNA sequencing on bulk bone marrow and peripheral blood

Targeted DNA sequencing was performed on 195 bone marrow samples to identify cases of clonal haematopoiesis, and on 99 paired peripheral blood samples. The SeqCap EZ HyperCap protocol was followed, and targeted capture performed against a panel of 97 genes recurrently mutated in myeloid malignancies and clonal hematopoiesis. One BAM file (mapped to the hg38 reference genome) is provided per sample.

Dataset EGAD00001011083

DNA and RNAseq of serial biopsies from 75 DLBCL patients

This data set includes serial biopsies from 75 patients with DLBCL. Tumour tissue was preserved either in FFPE or frozen. Each biopsy was sequenced with either whole genome or exome. A custom targeted sequencing data set is available to match most whole genome samples. RNAseq data were available for a subset of biopsies.

Dataset EGAD00001011816

Spatially resolved cellular and molecular drivers of cardiac remodelling in healthy and failing human hearts: Adult RNA (2025-07-31)

Cell Atlas of the diseased human heart. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2025-07-31.

Dataset EGAD00001015668

Submitting array based metadata

Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

Documentation submission/metadata/submission/array

Angelman, Rett, Prader-Willi Syndromes Consortium (ARP) Angelman Syndrome Natural History Protocol

This study is a longitudinal multidisciplinary investigation on the natural history, morbidity and mortality of Angelman Syndrome (AS). We will collect detailed longitudinal data on a cohort of AS individuals to gain a better understanding of the disease progression, and to follow the natural history of the clinical features of this cohort including assessment of quality of life and longevity. The participants to be recruited for the study will include 1) patients who have a documented molecular diagnosis of AS and 2) patients with a clear clinical diagnosis of Angelman Syndrome as determined by the Principal Investigator (PI) and the Co-investigators in this study but who do not have a known molecular defect. One of the goals of the natural history study will be to characterize the phenotypic differences between patients with Class I deletions and those with Class II deletions, particularly with respect to the issue of autism. A blood sample may be collected on the participants in order to create a DNA repository, and in some cases, to establish cell lines if further material is required for molecular studies. Alternatively, DNA may be obtained by buccal mucosa swabs/brushing. In those AS patients with known deletions involving the 15q11-q13 regions, a blood sample will be collected to perform comparative genome hybridization (CGH) microarray studies to characterize the extent of the deletion. No drugs or treatments will be administered through this protocol. In rare instances, a skin biopsy to establish a fibroblast cell line may be requested (using separate consent).

Study phs000576

Family Investigation of Nephropathy and Diabetes (FIND) Study

The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. Study subjects were recruited from eleven centers and in many ethnic groups throughout the United States. A genome-wide association study (GWAS) was conducted with the Affymetrix 6.0 chip. Subjects (index cases) with diabetes and kidney disease were initially recruited, and their parents and siblings were invited to participate. Genetic material from these participants was used to genotype markers throughout the genome. For association-based testing, a case-control design was implemented with study subjects selected primarily from the index cases of the families. Unrelated controls were selected from families where a case was not already selected. Several study sites also contributed non-FIND subjects, both cases and controls (consent forms for the release of FIND and non-FIND subjects/samples are included in this dbGaP release). Cases were selected if they met study criteria for diabetic nephropathy or met inclusion criteria based on elevated serum creatinine levels and abnormal urine protein excretion. Similarly, controls were long-term diabetics with otherwise normal kidney function. See inclusion/exclusion criteria section for a detailed description for the FIND study as a whole and this GWAS. The goal of the FIND study is to identify genes that influence susceptibility to diabetic kidney disease, leading to a better understanding of how kidney disease develops. In the long run, this may lead to improved treatment and prevention of diabetic kidney disease.

Study phs000333

Strabismus, CCDD and other anomalies

Strabismus is one of the most common ophthalmological diseases affecting our population. It has many detrimental effects for affected individuals, including functional visual loss from amblyopia, secondary psychosocial difficulties, and limited employment opportunities. Strabismus also imposes a significant economical burden on society from the cost of screening programs, surgical correction, and lost productivity. Congenital incomitant strabismus is a broad term that encompasses congenital ocular motility disorders with restricted movement in one or more directions of gaze, and includes various forms of Duane's retraction syndrome, horizontal gaze palsy, congenital fibrosis of extraocular muscles, and Moebius syndrome. Incomitant strabismus together with a group of related disorders such as congenital ptosis and congenital facial palsy have been collectively redefined as congenital cranial dysinnervation disorders (CCDDs). This redefinition stemmed from previous studies in the Engle lab showing that these disorders can be caused by mutations in genes encoding transcription factors critical to ocular cranial motor neuron development and by genes that encode proteins essential to the proper growth and guidance of developing axons. Thus, while these disorders account for only a small percent of strabismus, understanding their genetic etiology has led to our ability to provide genetic testing and counseling, and has provided the first insights into the molecular etiologies of strabismus. While the Engle lab has identified all seven CCDD genes published to date, our cohort still includes >100 familial CCDD pedigrees that are mutation-negative. We continue to utilize co-segregation analysis and whole exome or whole genome sequencing technologies to identify new disease genes in this mutation-negative cohort.

Study phs000478

NHLBI GO-ESP: Family Studies (Thoracic aortic aneurysms leading to acute aortic dissections)

The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. Acute aortic dissections are a common cause of premature death in the United States, ranking as high as the 15th leading cause of death. The goal of the Familial Thoracic Aortic Aneurysm and Dissection (TAAD) study at the University of Texas Health Science Center at Houston is to identify the genes causing thoracic aortic disease, and prevent premature deaths due to aortic dissections by identifying individuals who are genetically predisposed to the disease and initiating medical and surgical therapies to prevent dissections. In this study ("NHLBI GO-ESP Family Studies: Aortic Disease"), we have selected a series of families from the TAAD study, and are performing exome sequencing of 2 to 3 individuals per family.

Study phs000347

University of Washington Cerebrospinal Fluid Biomarker Study for Parkinson disease

There is a clear need to develop biomarkers for Parkinson disease (PD) diagnosis and monitoring disease progression. In this study we evaluated cerebrospinal fluid (CSF) proteins, which are known to be critically involved in PD or identified in our preliminary profiling studies, aptamers, and RNAs as potential PD biomarkers. Access to subjects for this study was via the Pacific Northwest Udall Center (PANUC) and the Alzheimer's Disease Research Center (ADRC) at the University of Washington and Oregon Health and Sciences University (OHSU). Using CSF samples from 30 well-characterized patients with PD and 30 age-, sex-matched healthy controls, we prepared RNA seq libraries and performed deep sequencing of all RNA species, including small and long RNA, mRNAs, noncoding RNAs and differentially spliced transcripts. We then tried several methods for RNAseq data analysis to optimize our analysis pipeline. We identified a total of 3381 transcripts corresponding to 182 long intergenic RNAs (LincRNAs), 11 microRNAs (miRNAs), 2861 protein-coding transcripts, 200 pseudogenes and 127 antisense RNAs; some of them were differentially expressed between PD and control groups. Selected differentially expressed RNAs have been validated in the same set of CSF samples using real-time PCR (RT-PCR). Further validations in independent, larger cohorts of samples are still ongoing. Our results obtained so far suggested that CSF proteins and RNAs could be used as good indexes for PD diagnosis and disease severity/progression. This study is a part of the NIDDS-funded Parkinson's Disease Biomarkers Program (PDBP).

Study phs000901

DNA Methylomic Profiling of Preeclampsia Across Pregnancy

Preeclampsia (PE) is a hypertensive, multi-system disorder of pregnancy that significantly impacts maternal and infant morbidity/mortality across the globe, as it increases risk of cardiovascular disease and remains a leading killer of women and babies. Despite PE's significant impact on morbidity/mortality, there are no clinically reliable biomarkers that predict PE. DNA methylation, a dynamic regulator of gene expression, represents a mechanism that is known to be impacted by the environment. Because PE stems from a dysfunctional placenta that releases debris into the maternal circulation, we hypothesized that the in-vivo environment created by the dysfunctional placenta will impact DNA methylation in the maternal circulation, and that these blood-based methylation profiles would serve as a systemic biomarker of the maternal response to placental dysfunction. Our overall objective of this pilot study was to longitudinally characterize DNA methylation profiles across the three trimesters of pregnancy in the maternal blood at time points before and after clinically overt PE using a targeted (endoglin and endoglin-related genes) and a discovery-based approach. For this pilot study, 28 normotensive control participants and 28 PE case participants enrolled in the NICHD funded pregnancy study entitled Prenatal Exposures and Preeclampsia Prevention: Mechanisms of Preeclampsia and the Impact of Obesity (PEPP3; P01HD30367) were 1:1 frequency matched on self-reported race, pre-pregnancy BMI, smoking history, and gestational age at sample collection. Methylation data were collected with the Infinium® MethylationEPIC Beadchip. Methylation assay data collection were carried out at Johns Hopkins University Genetic Resources Core Facility, The SNP Center, Baltimore, MD, USA.

Study phs001937

Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

It is well established that autism spectrum disorders (ASD) have a strong genetic component; however, for at least 70% of cases, the underlying genetic cause is unknown. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes-so-called sporadic or simplex families-we sequenced all coding regions of the genome (the exome) for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 that were previously reported. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19), for a total of 617 individual exomes from 209 families deposited in dbGaP. Here we show that de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD. Moreover, 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected beta-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes: CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3 and SCN1A. Combined with copy number variant (CNV) data, these results indicate extreme locus heterogeneity but also provide a target for future discovery, diagnostics and therapeutics.

Study phs000482

Decoding Human Heart Morphogenesis through Single-cell Multi-modal Analyses

The heart, which is the first organ to develop, is highly dependent on its form to function. However, how diverse cardiac cell types spatially coordinate to create complex morphological structures critical for heart function remains to be elucidated. Here, we show that integration of single cell RNA-sequencing with high-resolution multiplexed error-robust fluorescent in situ hybridization (MERFISH) not only resolves the identity of cardiac cell types developing the human heart but also provides a spatial mapping of individual cells that enables illumination of their organization into cellular communities forming distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, supporting their specialization according to cellular ecosystem and anatomic region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities revealed signaling pathways orchestrating the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. In vivo conditional genetic mouse models and in vitro human pluripotent stem cell studies confirmed an intricate multicellular PLXN-SEMA crosstalk among specific ventricular cardiomyocyte, fibroblast and endothelial cell subpopulations that directs the compaction of the ventricular wall layers. Thus, these detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodeling the human heart offer new insights into structural heart diseases as well as engineering complex multi-cellular tissues for human heart repair. Reprinted from Farah et al. (In press), with permission from Nature.

Study phs002031

Blood DNA Methylation in Post-Acute Sequelae of COVID-19 (PASC): a Prospective Cohort Study

Background: DNA methylation integrates environmental signals with transcriptional programs. COVID-19 infection induces changes in the host methylome. While post-acute sequelae of COVID-19 (PASC) is a long-term complication of acute illness, its association with DNA methylation is unknown. No universal blood marker of PASC, superseding single organ dysfunctions, has yet been identified. Methods: In this single center prospective cohort study, PASC, post-COVID without PASC, and healthy participants were enrolled to investigate their symptoms association with peripheral blood DNA methylation data generated with state-of-the-art whole genome sequencing (WGS). PASC-induced quality-of-life deterioration was scored with a validated instrument, SF-36. Analyses were conducted to identify potential functional roles of differentially methylated loci, and machine learning algorithms were used to resolve PASC severity. Findings: 103 patients with PASC (22.3% male, 77.7% female), 15 patients with previous COVID-19 infection but no PASC (40.0% male, 60.0% female), and 27 healthy volunteers (48.1% male, 51.9% female) were enrolled. Whole genome methylation sequencing revealed 39 differentially methylated regions (DMRs) specific to PASC, each harboring an average of 15 consecutive positions, that differentiate patients with PASC from the two control groups. Motif analyses of PASC-regulated DMRs identify binding domains for transcription factors regulating circadian rhythm and others. Some DMRs annotated to protein coding genes were associated with changes of RNA expression. Machine learning support vector algorithm and random forest hierarchical clustering reveal 28 unique differentially methylated positions (DMPs) in the genome discriminating patients with better and worse quality of life.

Study phs003658

Cancer Moonshot Biobank

The National Cancer Institute initiated the Cancer Moonshot Biobank to accelerate research through the collection and distribution of biospecimens for key research efforts. The Biobank will engage with over one thousand cancer patients, being treated with standard-of-care therapies, for longitudinal biospecimen and data collection. Cancer patients will be enrolled into the study from a diverse participant population engaged at medical institutions throughout the U.S. The biospecimens will be distributed to qualified scientists to accelerate research progress. The Biobank program, working closely with Leidos Biomedical, Inc. (LBR), will partner with community hospitals and other medical institutions, designated as Biospecimen Source Sites (BSS), to engage and consent eligible participants and collect biospecimens and associated clinical data; work with a central Biospecimen Core Resource (BCR) to support biospecimen collection, processing and storage activities and perform pathology quality control; work with LBR's Molecular Characterization (MoCha) lab to perform clinical tumor molecular characterization assays and return results to patients and their physicians; and make the biospecimens and associated data available to researchers to accelerate cancer research progress. Return of clinical results, a dedicated Patient and Provider Engagement (PPE) website, electronic informed consent, local participant engagement projects, and an embedded ethical, legal and social issues sub-study will together support patient and provider engagement in the program. Cancer Moonshot Biobank imaging data is being made available in The Cancer Imaging Archive (TCIA) on a release schedule that is coordinated with the program releases of data to dbGaP. The imaging data can be found at the following link: Cancer Moonshot Biobank Imaging

Study phs002192

Genetic Modifiers of Duchenne Muscular Dystrophy

The primary determinant of disease severity in patients with severe Duchenne muscular dystrophy (DMD) or milder Becker muscular dystrophy (BMD) is whether their dystrophin gene (DMD) mutation disrupts the mRNA reading frame or permits the expression of a partially functional protein. However, even in the complete absence of dystrophin, variability in disease severity is observed, with candidate gene studies implicating several genes as potential DMD modifiers. This study undertakes a comprehensive genome-wide search for modifier loci influencing disease severity in DMD patients. The availability of subjects for such studies remains limited, resulting in modest sample sizes that challenge the GWAS design. To address this, we have implemented measures to minimize heterogeneity within the dataset at the dystrophin (DMD) gene itself, adopting a conservative approach to DMD mutation classification to limit the possibility of residual dystrophin expression. Additionally, the study employed statistical methods that are well-suited to smaller sample sizes, including the use of a novel linear regression-like residual for time to ambulatory loss and the application of evidential statistics, specifically the Posterior Probability of Linkage Disequilibrium (PPLD), to assess trait-SNP associations in the GWAS framework. With a sample size of 419 patients, this study has identified multiple candidate genetic modifier loci. The molecular data available in dbGaP includes 2,562,265 directly genotyped variants in 419 people using the Illumina Infinium Omni2.5Exome-8 Beadchip assay, achieving a genotyping rate of 0.998. The phenotypic data comprises age at ambulatory loss, steroid status, DMD gene mutation, and the PPLD value from the time-to-event (TE) phenotype.

Study phs003680

Epigenetic Control of Topoisomerase 1 by MacroH2A1.1

DNA transactions introduce torsional constraints that pose an inherent risk to genome integrity. While topoisomerase 1 (TOP1) activity is essential for removing DNA supercoiling, aberrant stabilization of TOP1:DNA cleavage complexes (TOP1ccs) can result in cytotoxic single-stranded DNA lesions (SSLs). What protects the genome from aberrant TOP1 activity remains unknown. Using a combination of CUT&RUN and TOP1 Covalent Adduct Detection (CAD) Seq in MDA-MB-231 breast cancer cells (American Type Culture Collection, ATCC), we identified chromatin context as an essential means to ensure TOP1cc resolution at TOP1 hot spots. Through its ability to bind poly(ADP-ribose) (PAR), a protein modification required for TOP1cc repair, the histone variant macroH2A1.1 establishes a TOP1-permissive chromatin environment, while the alternatively spliced macroH2A1.2 isoform is unable to bind PAR or protect from TOP1ccs. MacroH2A1 isoform-specific analyses were based on reconstitution of macroH2A1.1 knockout cells with wild-type or PAR binding-deficient FLAG-tagged macroH2A1.1 in human breast cancer cells. Mechanistically, we find that macroH2A1.1 facilitates the recruitment of the TOP1cc repair factor XRCC1 in response to both endogenous and drug-induced topological stress. Impaired macroH2A1.1 splicing, a frequent cancer feature, was predictive of increased sensitivity to TOP1 poisons in a pharmaco-genomic screen in breast cancer cells, and macroH2A1.1 inactivation mirrored this effect in breast and ovarian cancer cells. Consistent with this, low macroH2A1.1 expression correlated with improved survival in cancer patients treated with TOP1 inhibitors (TOP1i). We propose that macroH2A1 alternative splicing serves as an epigenetic modulator of TOP1-associated genome maintenance and a potential cancer vulnerability.

Study phs003729

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