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• Sequencing data for oesophageal and related samples - Ng, Contino et al (RNA)

  Data supporting: "Interplay of processes shapes structural variations undergoing selection in oesophageal adenocarcinoma" Ng, Contino et al. RNAseq (BAM files) 214 oesophageal adenocarcinoma samples

  Dataset EGAD00001007809

• Sequencing data for oesophageal and related samples - Alex Frankell et al (RNA)

  Data supporting: "The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic." Frankell et al. RNAseq (BAM files) 116 tumours

  Dataset EGAD00001004423

• Exome trios in patients with gastroschisis (2019-04-08)

  Gastroschisis (MIM 230750) is a herniation of the intestines through a defect of the abdominal wall lateral to the umbilicus (usually on the right side), and it is not covered by a membrane [Ledbetter, 2012]. Gastroschisis is a congenital anomaly with increasing incidence, easy prenatal diagnosis and extremely variable postnatal outcomes. On the basis of clinical manifestations, epidemiologic charateristics, and the presence and type of additional malformations, gastroschisis could be considered a heterogeneous condition with no gene/s discovered yet. This congenital anomaly affects approximately 1-3 infancts per 10,000 live births [Calzolari et al.1995;Parker et al.,2010] Current knowledge about causative mutations/variants. To date, no single gene has been linked to gastroschisis. Some publications have tried to link this malformation to variants in genes (such as AEBP1 (adipocyte enhancer binding protein) gene [Feldkamp et al,. 2012] or the VEGF-NOS3 pathway [Lammer et al., 2008]. Previously, a Scribble mutant mouse model (circletail) was reported to exhibit gastroschisis, however recent studies demonstrated that the Scribble knockout fetus exhibits exomphalos phenotype of gastroschisis [Carnagham et al., 2013]. 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 2019-04-08.

  Dataset EGAD00001004942

• DNMT3A MOPD patient ChIP-seq data

  ChIP-seq data from controls and patients with de novo DNMT3A mutations resulting in microcephalic primordial dwarfism.

  Dataset EGAD00001004473

• RNA-seq sequence data from DNMT3A microcephalic dwarfism patients.

  RNA-seq data generated from cells from control individuals and individuals with de novo DNMT3A variants causing microcephalic dwarfism.

  Dataset EGAD00001004471

• Growth Hormone (GH) -secreting Pituitary Adenoma

  Data from 12 fresh-frozen somatotropinomas and their corresponding blood samples. Details are given in Valimaki et al. Whole-genome sequencing of Growth Hormone (GH) -secreting Pituitary Adenoma. Provisionally accepted, 2015.

  Study EGAS00001001293

• Chronic lymphocytic leukemia patient-derived xenografts recapitulate clonal evolution to Richter transformation

  Whole-genome sequencing (WGS) and RNA-seq data from patient-derived xenografts (PDXs) obtained from CLL and RT primary samples as described in Playa-Albinyana et al.

  Study EGAS00001006965

• Dataset-linking-WGS-via-README-for-EGAS00001004884

  This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007669

• NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)

  ECLIPSE was a longitudinal observational study of 2164 COPD subjects and a smaller number of smoking controls (337) and nonsmoking controls (245) followed regularly for three years, with three chest CT scans (at baseline, one year, and three years) (Vestbo et al., 2008, PMID: 18216052). Subjects were enrolled at clinical centers in the US, Canada, Europe, and New Zealand. Inclusion criteria included subjects ages 40-75, at least 10 pack-years of smoking, and spirometry in GOLD grades 2-4 (COPD cases) or normal spirometry with post-bronchodilator FEV1 >85% (predicted) and FEV1/FVC>0.7 (controls). Study visits were performed at enrollment, three months, and every six months thereafter with spirometry, questionnaires, and other clinical evaluations. The ECLIPSE CT scans have been analyzed with the VIDA software for emphysema and airway phenotypes. ECLIPSE has provided key insights into the clinical epidemiology of COPD, including COPD exacerbations (Hurst, et. al., 2010, PMID: 20843247) and lung function decline in COPD (Vestbo, et. al., 2011, PMID: 21991892). ECLIPSE has been used in a number of genetic studies of COPD susceptibility and protein biomarkers (Faner, et. al., 2014, PMID: 24310110). Genome-wide gene expression microarray data are available in 147 induced sputum samples from COPD subjects and 248 peripheral blood samples from COPD and control subjects. Phenotype data for ECLIPSE subjects is available through dbGaP phs001252.

  Study phs001472

• The Cardiogenics study

  Cardiogenics is an European collaborative project, it started in January 2007 and was funded by the European Commission through its Sixth Framework Program (reference LSHM -CT-2006-037593) until 2011. It involved several laboratories from France (INSERM UMRS937), Germany (Lübeck University; Regensburg University), United Kingdom (the Welcome Trust Sanger Institute; University of Leicester) and Sweden (Uppsala University Hospital) and was coordinated by a management group composed of scientists from these different institutions. Under the coordination of the management group the laboratories are still developing projects using Cardiogenics data sets. As part of the Cardiogenics project, RNA from monocytes and macrophages of 363 patients with coronary artery disease and 395 healthy individuals was prepared and genome-wide expression was assessed in both cell types using the Illumina HumanRef 8 v3 Beadchip . The DNA of all these individuals was genotyped using the Human 610 Quad custom arrays. Both data sets were used to conduct eQTL analyses. A subsample of subjects included in an allelic-imbalance substudy were also genotyped using the Illumina Human Custom 1.2M array. Detailed description of these datasets can be found in Shah S et al. Circ Cardiovs Genet 2011; Rotival M et al. Plos Genet 2011, Almlöf et al. Plos One 2012 and Garnier S et al. Plos Genet 2013

  Study EGAS00001000411