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• Comprehensive de novo variant discovery with HiFi long-read sequencing

  We performed whole-genome sequencing of 8 trios with intellectual disability using Pacbio HiFi long-reads. We then called variants with PBSV and Deepvariant. We filtered for high-quality de novo variants and validated them.

  Study EGAS00001006479

• Australia and New Guinea haplotype phasing (2017-06-27)

  Whole-genome sequencing and phasing of admixed Aboriginal Australian genomes and Papua New Guinean genomes using 10x Genomics Chromium technology. 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 please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-06-27.

  Dataset EGAD00001003407

• Isotype-resolved sequencing of B-cell receptor in health and disease (2017-09-13)

  Sequencing of B-cell receptor repertoires in healthy individuals and patients with chronic lymphocytic leukemia. 1) 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 please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-09-13.

  Dataset EGAD00001003748

• X Ten analysis of spiked placental tissue samples

  Metagenomics data of 80 placental tissue samples analyzed by X Ten for their possible microbial content. These 80 samples from pre-labor C-section deliveries, representing Cohort 1, were spiked with 1100 CFU Salmonella bongori. These same samples were also analyzed by 16S amplicon sequencing (search for ERP109246 in ENA).

  Dataset EGAD00001004198

• BLUEPRINT release August 2015, DNase-Hypersensitivity for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  DNase-Hypersensitivity data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001524

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute Myeloid Leukemia - CTR, on genome GRCh38

  Bisulfite-Seq data for 1 Acute Myeloid Leukemia - CTR sample(s). 18 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001530

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute promyelocytic leukemia, on genome GRCh38

  Bisulfite-Seq data for 3 Acute promyelocytic leukemia sample(s). 24 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001537

• BLUEPRINT release January 2015, RNA-Seq for common lymphoid progenitor

  RNA-Seq data for 5 common lymphoid progenitor sample(s). 20 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001184

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001186

• BLUEPRINT release January 2015, Bisulfite-Seq for mature neutrophil

  Bisulfite-Seq data for 6 mature neutrophil sample(s). 79 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001201

• BLUEPRINT release January 2015, Bisulfite-Seq for germinal center B cell

  Bisulfite-Seq data for 1 germinal center B cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001203

• BLUEPRINT release January 2015, Bisulfite-Seq for memory B cell

  Bisulfite-Seq data for 1 memory B cell sample(s). 20 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001131

• BLUEPRINT release January 2015, RNA-Seq for granulocyte monocyte progenitor cell

  RNA-Seq data for 3 granulocyte monocyte progenitor cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001146

• BLUEPRINT release January 2015, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001140

• BLUEPRINT release January 2015, Bisulfite-Seq for hematopoietic multipotent progenitor cell

  Bisulfite-Seq data for 1 hematopoietic multipotent progenitor cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001141

• BLUEPRINT release January 2015, Bisulfite-Seq for alternatively activated macrophage

  Bisulfite-Seq data for 4 alternatively activated macrophage sample(s). 64 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001143

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 6 hematopoietic stem cell sample(s). 13 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001156

• BLUEPRINT release August 2015, Bisulfite-Seq for erythroblast, on genome GRCh38

  Bisulfite-Seq data for 2 erythroblast sample(s). 35 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001484

• BLUEPRINT release August 2015, DNase-Hypersensitivity for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  DNase-Hypersensitivity data for 4 CD14-positive, CD16-negative classical monocyte sample(s). 4 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001476

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute Myeloid Leukemia, on genome GRCh38

  Bisulfite-Seq data for 6 Acute Myeloid Leukemia sample(s). 66 run(s), 6 experiment(s), 6 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001482

• BLUEPRINT release August 2014, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000915

• BLUEPRINT release August 2014, RNA-Seq for common lymphoid progenitor

  RNA-Seq data for 3 common lymphoid progenitor sample(s). 15 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000918

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000919

• BLUEPRINT release August 2014, Bisulfite-Seq for germinal center B cell

  Bisulfite-Seq data for 1 germinal center B cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000943

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 3 hematopoietic stem cell sample(s). 8 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000939

• BLUEPRINT release August 2014, Bisulfite-Seq for mature neutrophil

  Bisulfite-Seq data for 6 mature neutrophil sample(s). 79 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000935

• BLUEPRINT release August 2014, RNA-Seq for granulocyte monocyte progenitor cell

  RNA-Seq data for 3 granulocyte monocyte progenitor cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000922

• BLUEPRINT release August 2014, Bisulfite-Seq for alternatively activated macrophage

  Bisulfite-Seq data for 1 alternatively activated macrophage sample(s). 10 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000920

• 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

• BLUEPRINT release August 2016, Bisulfite-Seq for Mantle Cell Lymphoma, on genome GRCh38

  Bisulfite-Seq data for 5 Mantle Cell Lymphoma sample(s). 65 run(s), 5 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002505

• BLUEPRINT release August 2016, Bisulfite-Seq for Multiple Myeloma, on genome GRCh38

  Bisulfite-Seq data for 5 Multiple Myeloma sample(s). 63 run(s), 7 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002521

• BLUEPRINT release August 2016, Bisulfite-Seq for Chronic Lymphocytic Leukemia, on genome GRCh38

  Bisulfite-Seq data for 6 Chronic Lymphocytic Leukemia sample(s). 84 run(s), 6 experiment(s), 12 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002396

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Myeloid Leukemia, on genome GRCh38

  Bisulfite-Seq data for 19 Acute Myeloid Leukemia sample(s). 338 run(s), 32 experiment(s), 38 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002419

• BLUEPRINT release August 2016, Bisulfite-Seq for osteoclast, on genome GRCh38

  Bisulfite-Seq data for 2 osteoclast sample(s). 88 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002427

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Lymphocytic Leukemia, on genome GRCh38

  Bisulfite-Seq data for 7 Acute Lymphocytic Leukemia sample(s). 132 run(s), 9 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002313

• BLUEPRINT release August 2016, DNase-Hypersensitivity for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  DNase-Hypersensitivity data for 28 CD14-positive, CD16-negative classical monocyte sample(s). 28 run(s), 28 experiment(s), 28 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002286

• BLUEPRINT release August 2016, Bisulfite-Seq for thymocyte, on genome GRCh38

  Bisulfite-Seq data for 1 thymocyte sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002440

• BLUEPRINT release August 2016, DNase-Hypersensitivity for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  DNase-Hypersensitivity data for 3 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 3 run(s), 3 experiment(s), 3 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002468

• Human Developmental Cell Atlas_HDCA - WGS (2019-04-11)

  Single cell + bulk genomics study for immune and hematopoietic organs during human fetal development . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004953

• BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 Summary Statistics of Most Significant Associations

  BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 summary statistics of most significant association data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each summary statistics file contains the most significant association for each phenotype ID.

  Dataset EGAD00001005200

• 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

• Cell type-specific transcriptomics of esophageal adenocarcinoma

  3' mRNA-Seq obtained from distinct isolated cell types (epithelia cells,immune cells, fibroblasts) of endoscopically obtained esophageal adenocarcinoma tissue as well as normal esophageal mucosa. Libraries for RNA-sequencing were prepared using the QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina according to the low input protocol. Libraries were sequenced on a HiSeq 4000 (Illumina) by 1x 50 bases.

  Dataset EGAD00001005508

• 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

• scRNA-seq raw data

  This dataset contains scRNA-seq fastq files (trimmed for quality and adapters using Trim Galore) for 3739 intestinal plasma cells of known or unknown antigen specificities from in total 12 individuals (4 untreated coeliac disease patients, 3 treated coeliac disease patients, 5 controls). The data was generated using the Smart-seq2 protocol and sequenced on the Illumina NextSeq500 platform with 75 bp paired-end reads.

  Dataset EGAD00001006436

• CRUK Accelerator: oesophageal adenocarcinoma whole exome and RNA-seq raw sequencing data

  Whole exome and RNASeq raw sequencing data for a cohort of 7 male patients with oesophageal adenocarcinoma. Median age at diagnosis was 68. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001008489

• ATAC-Seq of GM adipose tissue samples

  This dataset contains 28 fastq files (11 files for subcutaneous tissue and 17 files for visceral tissue) from nine individuals. All samples were initially sequenced by SEx50 mode (16 files) and some of them were also sequenced by PEx100 mode (12 files). Sequencing was done on the Illumina HiSeq2000 platform.

  Dataset EGAD00001010254

• Sequencing data for oesophageal and related samples - Ganguli et al (sWGS)

  Data supporting: "TBC" Ganguli et al (sWGS for 75 samples)

  Dataset EGAD00001011189

• RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

  Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

  Dataset EGAD00001010920

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• 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

• Whole-Genome Sequencing in Multiplex Epilepsy Families

  Epilepsy is one of the most common neurologic disorders, affecting approximately 4% of individuals at some time in their lives. More than 30% of people with epilepsy continue to have seizures despite treatment, and improved approaches to treatment and prevention are sorely needed. In the search for new strategies to reduce the burden of disease, the discovery of specific genes that influence risk offers a novel opportunity to clarify pathogenic mechanisms, identify susceptible individuals prior to seizure onset, and treat and prevent seizures in people at risk. Despite clear evidence of the importance of genetics in susceptibility to epilepsy, only limited progress has been made in identifying the specific genes that influence risk. One of the greatest challenges for genetic research on this disorder is its extreme clinical and genetic heterogeneity. Although epilepsy is broadly defined by recurrent unprovoked seizures, it is so variable in its clinical manifestations, natural history, and treatment response that most epileptologists view it as a collection of different syndromes ("epilepsies") with distinct etiologies. The genetic effects on susceptibility are also likely to be extremely variable, ranging from rare variants with high penetrance (some of which produce Mendelian patterns of inheritance) to common variants with low penetrance. Recent findings strongly suggest that rare gene variants play a major role in the genetic architecture of the epilepsies. The purpose of this study was to discover new genetic risk factors for epilepsy. The primary approach was to use whole-genome sequencing to interrogate classes of genetic variants, including very rare variants, in multiplex families. Our main hypothesis was that, in at least some proportion of these families, a single variant would explain all instances of epilepsy. Variants identified within the families could then be tested for cosegregation within the family and also validated by seeing enrichment in sporadic epilepsy cases. Furthermore, understanding the impact of rare variation in epilepsy also has the potential to provide insight into the genetic architecture of other complex human diseases. Our analysis focused on families containing multiple individuals with non-acquired (idiopathic or unknown cause) epilepsy under the hypothesis that these would be enriched for genetic control. The families studied had been previously collected and phenotyped in detail, and contain an average of 3.8 individuals per family with a range of different types of epilepsy. We selected one or two affected individuals from each family for sequencing. This work has generated NGS data on 60 samples from 29 multiplex epilepsy families. We have established that, under a monogenic model of inheritance, sequencing pairs of distantly related relatives is an effective method for reducing the number of candidate variants. Critically, we have found that no single variant can explain a large proportion of these epilepsy families. This work emphasizes that identifying causal variants among the many genetic candidates found in this work will require very large sample sizes and gene-based analyses.

  Study phs000690

• Data Access Commitee for schizophrenia and control cases sequencing data

  Dac EGAC00001002810

• The data access committee for TIGIT in MCL with CART

  Dac EGAC00001003167

• DAC for study characterisation of CpG islands in human tissues

  Dac EGAC00001000068

• DAC for the MDC-LSR-SAHLSIS ischemic stroke study

  Dac EGAC00001000226

• DAC for integrated genomics of metastatic prostate cancer

  Dac EGAC00001000230

• English Longitudinal Study for Ageing (ELSA) Genetic Data Access

  Dac EGAC00001000270

• Data access committee for the head and neck project

  Dac EGAC00001000374

• DAC for the Singapore Integrating Omics Study

  Dac EGAC00001000685

• DAC for AA HCC patient from Chang Gung Memorial Hospital

  Dac EGAC00001000707

• European Bank for induced pluripotent Stem Cells (EBiSC)

  Dac EGAC00001000768

• DAC for the project on epigenetic dysregulation in tuberculosis

  Dac EGAC00001000909

• Data Access Committee members for NETWork! project

  Dac EGAC00001000930

• Ludwig Data Access Committee for the Study of Oesophageal Cancer

  Dac EGAC00001000978

• Fecal Microbiota Transplantation for refractory immune checkpoint inhibitor-associated colitis

  Dac EGAC00001001021

• Search for bacteria in neural tissue from amyotrophic lateral sclerosis

  Dac EGAC00001001058

• DAC for WGS data Romanians and Roma/Rroma (Romania)

  Dac EGAC00001001184

• DAC for Analysis of mechanisms of CD19- relapse in CARPALL study

  Dac EGAC00001001257

• DAC for Genome-Wide Scans of Signals of Selection

  Dac EGAC00001001351

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001483

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001485

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001001615

• Epichaperome - Englander Institute for Precision Medicine - Weill Cornell Medicine DAC

  Dac EGAC00001001890

• DAC for the study EGAS00001005773

  Dac EGAC00001002411

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001002437

• DAC for COPD human sputum 16s rRNA gene sequencing data

  Dac EGAC00001002779

• Data access committee for sequencing data at Kyoto University

  Dac EGAC50000000045

• Data access committee for neural retina and retinal organoid data

  Dac EGAC50000000019

• DAC for access to array genotypes from the PREGO biobank.

  Dac EGAC00001003484

• Centre for Drug Repurposing and Medicines Research Data Access Committee

  Dac EGAC00001002764

• XClone for analyzing somatic copy number alterations Data Access Committee

  Dac EGAC00001003492

• Single cell multi-omics committee for CK-AML

  Dac EGAC00001003353

• Developmental and Stem Cell Biology department - Hospital for Sick Children

  Dac EGAC00001002951

• Data access committee for sequencing data generated by Wyatt Lab

  Dac EGAC50000000538

• University of Melbourne Centre for Cancer Research (UMCCR) Data Access Committee

  Dac EGAC00001003567

• AGLCD sequencing data

  sequencing datasets for AGLCD patient

  Dac EGAC50000000852

• MK Clinical Trial DAC

  DAC for MK Clinical Trial Data

  Dac EGAC50000000677

• DAC_TWINS

  DAC related to monozygotic twins discordant for ALS

  Dac EGAC50000000517

• WGBS and oxBS-seq for APL

  Study EGAS00001005610

• EGAD00010000819

  Summary statistics from meta-analysis for BP phenotypes

  Dataset EGAD00010000819

• Anne Eugster, Center for Regenerative Therapies Dresden, TU Dresden

  Dac EGAC50000000315

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: SPARK Simons Foundation Powering Autism Research for Knowledge

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002511

• National Heart Lung and Blood Institute Exome sequencing in SCID

  Analysis of the molecular etiologies of severe combined immunodeficiency (SCID) has led to important insights into the control of immune cell development. Most cases of SCID result from either X-linked or autosomal recessive inheritance of mutations in a known causative gene. However, in some cases, the molecular etiology remains unclear. To identify the cause of SCID in a patient known to lack the protein tyrosine phosphatase CD45, we utilized single nucleotide polymorphisms (SNP) arrays and whole exome sequencing. The patient's mother was heterozygous for an inactivating mutation in CD45, while the paternal alleles lacked mutations. The patient exhibited a single CD45 mutation identical to the maternal allele. Patient SNP array analysis revealed no change in copy number but loss of heterozygosity for the entire length of chromosome 1 (Chr1), indicating that disease was caused by uniparental disomy (UPD) with isodisomy of the entire maternal Chr1 bearing the CD45 mutation. Non-lymphoid blood cells and other mesoderm and ectoderm-derived tissues retained UPD of the entire maternal Chr1 in this patient who had undergone successful bone marrow transplantation. Exome sequencing revealed mutations in 7 additional genes bearing nonsynonymous SNPs predicted to have deleterious effects. These findings represent the first reported case of SCID caused by UPD and suggest UPD should be considered in SCID and other recessive disorders, especially when the patient appears homozygous for an abnormal gene found in only one parent. Evaluation for alterations in other genes affected by UPD should also be considered in such cases.

  Study phs000479

• Genetic Markers of Lipids in Indians: A Validation Study of Most Relevant Findings of Genome-Wide Association Studies

  We aimed to validate the GWAS loci (identified in European populations) related to serum lipid levels (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein cholesterol (VLDL-C)) in the Indian population originally recruited to examine the 20-year trend of cardiometabolic risk factors in rural and urban areas around National Capital region in India. Isolated DNA was processed for genotyping of 12 established GWAS variants using multiplex Sequenom Mass ARRAY technology. We examined the association of these genetic variants with different lipid levels and found association of eight variants for lipid levels. We also derived weighted genetic risk scores (wGRAS) using the validated variants. The wGRS for triglycerides and VLDL-C was derived based on four associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, rs4420638 at APOC1) which was associated with 36.31 mg/dL elevated triglycerides and VLDL-C (β=0.95, SE=0.16, pTRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL higher total cholesterol (β=1.01, SE=0.23, pTRPS1, rs4147536 at ADH1B, rs4420638 at APOC1 and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 and FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1 and rs7832643 at PLEC) was associated with 10.64 mg/dL lower HDL-C (β= -0.87, SE =0.14, pThe phenotypic and genotypic data will be made available on the dbGaP portal for access.

  Study phs003469

• ILyAD (Indolent Lymphoma And vitamin D)

  Indolent non-Hodgkin lymphomas are incurable diseases, and require intermittent and often morbid and expensive therapy during their prolonged natural history. Lower intensity and better-tolerated, cost-effective treatment strategies are needed for these patients. Low vitamin D levels at diagnosis of indolent lymphomas are strongly associated with inferior outcomes to treatment. Standard therapy for low tumor burden, indolent lymphoma includes a single agent monoclonal antibody (rituximab). In our double-blind, placebo-controlled, randomized trial we evaluated if vitamin D supplementation with 2000IU oral cholecalciferol daily for three years improved the progression-free survival of patients with indolent lymphoma treated with single agent rituximab. Stratification factors for randomization included histology and Follicular Lymphoma International Prognostic Index (FL-IPI) prognostic score. The primary endpoint of the trial was progression-free survival over three years; secondary endpoints included response at 13 weeks, and overall survival. Eligible participants were adults with biopsy proven follicular lymphoma (grades 1-3a), small lymphocytic lymphoma, marginal zone lymphoma and mucosal-associated lymphoid tissue with no prior anti-lymphoma systemic therapy. Participants had Ann Arbor stage II or greater, measurable disease defined by Lugano criteria. Participants with osteoporosis requiring prescription treatment, symptomatic primary hyperthyroidism, hypercalcemia, creatinine greater than 1.5 times the upper limit of normal or a history of calcium-related kidney stones were excluded. Germline DNA was isolated from saliva samples obtained from participants at baseline. Whole exome sequencing was performed and SNPs were analyzed using the Agilent SureSelectXT Human All Exon V6 +UTR array. SNP data from 185 participants are available through dbGaP.

  Study phs003503

• Multiple Myeloma Clinical Targeted Sequencing of Patient Samples

  Purpose: Molecular diagnostics for the risk stratification of patients with multiple myeloma (MM) have languished in the area of cytogenetics and fluorescence in situ hybidization (FISH). However, here we present the case for next generation sequencing of MM patient samples to not only identify high-risk markers, but also mutations and deletions relating to immunomodulatory drugs (IMiDs), and more importantly to guide the sequencing of immunotherapy regimens in response to intrinsic antigen escape as a means of treatment resistance and relapse. Experimental design: CD138+ cell selected samples (n=134) from bone marrow aspirates from patients with multiple myeloma were sequenced using a targeted panel in a clinical diagnostics laboratory. Data were analyzed for high-risk markers, treatment-related resistance mechanisms, and precision medicine targets to guide the future treatment of patients in the clinic. Results: High-risk markers including t(4;14), t(14;16), t(14;20), gain or amplification 1q, deletion of CDKN2C, and deletion or mutation of TP53 were identified in 15% of newly diagnosed myeloma patient samples. At relapse, alterations in the cereblon degradation pathway were found in 24.3% of IMiD treated patients. Deletions of 4p (CD38) were also enriched in patients who received anti-CD38 treatment (P=0.03) which were mostly monoallelic. Deletions and mutations were detected in TNFRSF17 encoding BCMA in patients treated with anti-BCMA regimens, and the information was used to change the treatment of the patients. Conclusions: Targeted sequencing of multiple myeloma patient diagnostic samples can be used for risk stratification, and also to monitor and adjust treatments as resistance mechanisms evolve.

  Study phs003908

• Analysis of mutational and proteomic heterogeneity of gastric cancer to monitor post-treatment tumor burden using circulating tumor DNA

  It remains unclear whether complete spectrum of tumor heterogeneity is reflected in circulating tumor DNA (ctDNA). Here, we performed multi-regional target sequencing of primary tumors from 10 patients who underwent surgical resection for gastric cancer. A total of 126 non-synonymous mutations were identified from 30 samples from 10 tumors. Of these, 16 (12.7%) were likely mutations that occurred early in tumor development (i.e., present in all three regions) and were designated founder mutations. Among those validated with highly sensitive digital PCR (dPCR), 93% of non-founder mutations were found in all three regions. Variant allele frequencies (VAFs) of founder mutations were significantly higher than those of non-founder mutations. Phylogenetic analysis demonstrated that five of eight tumors (62.5%, excluding two hypermutator) had at least one identifiable truncal/founder mutation. These findings led us to prioritize founder mutations for ctDNA detection with individually-designed primer/probe sets for dPCR. Marked proteomic diversity was present across the tumor regions both within and between patients independent of mutational status. In preoperative plasma, the average ctDNA VAF of founder mutations was significantly higher than that of non-founder mutations (p = 0.039). Our present data demonstrated that high VAF mutations in the tumor likely represent founder or truncal mutations. However, mutation patterns alone appear insufficient to predict protein heterogeneity. These results suggest that, in practice, mutations having high VAF identified without multi-regional sequencing may be useful for ctDNA monitoring but are inadequate to fully predict proteomic heterogeneity of the tumor.

  Study JGAS000231

• Sequencing and analysis of a South Asian-Indian personal genome

  India with over 1.3 billion people is estimated to carry three times more genetic diversity compared to Europe. Next-generation sequencing technologies have facilitated the understanding of diversity by enabling whole genome sequencing at greater speed and lower cost. While multiple genomes from people of European and a few of Asian descent have been sequenced, only recently a single male genome from the Indian subcontinent at sufficient depth and coverage was published. We have in this study sequenced and analyzed the genome of a South Asian Indian female (SAIF) from the Indian state of Kerala. We identified over 3.4 million SNPs in this genome including over 89,873 private variations. Comparison of the SAIF genome with several published personal genomes revealed that this individual shared ~50% of the SNPs with each of these genomes. Analysis of the SAIF mitochondrial genome showed that it is closely related to the U1a3 haplogroup which has been previously observed in Kerala. We assessed the SAIF genome for SNPs with health and disease consequences and found that the individual was at a higher risk for multiple sclerosis and a few other diseases. In assessing SNPs that modulate drug response we found a variation that predicts a favorable response to metformin used for treating diabetes. SNPs predictive of adverse reaction to warfarin indicated that the SAIF individual is not at risk for bleeding if treated with typical doses of warfarin. In addition to these, we report the presence of several other SNPs of medical relevance.

  Study EGAS00001000328

• Coding and regulatory somatic profiling of triple-negative breast cancer in Sub-Saharan African patients

  The burden of triple-negative breast cancer (TNBC) may be shaped by genetic factors, particularly inherited and somatic mutation profiles. However, data on this topic remain limited, especially for the African continent, where a higher TNBC incidence is observed. In the age of precision medicine, cataloguing TNBC diversity in African patients becomes imperative. We performed whole exome sequencing, including untranslated regions, on 30 samples from Angola and Cape Verde, which allowed to ascertain on potential regulatory mutations in TNBC for the first time. A high somatic burden was observed for the African cohort, with 86% of variants being so far unreported. Recurring to predictive functional algorithms, 17% of the somatic single nucleotide variants were predicted to be deleterious at the protein level, and 20% overlapped with candidate cis-regulatory elements controlling gene expression. Several of these somatic functionally-impactful mutations and copy number variation (mainly in 1q, 8q, 6 and 10p) occur in known BC- and all cancer-driver genes, enriched for several cancer mechanisms, including response to radiation and related DNA repair mechanisms. TP53 is the top of these known BC-driver genes, but our results identified possible novel TNBC driver genes that may play a main role in the African context, as TTN, CEACAM7, DEFB132, COPZ2 and GAS1. These findings emphasize the need to expand cancer omics screenings across the African continent, the region of the globe with highest genomic diversity, accelerating the discovery of new somatic mutations and cancer-related pathways.

  Study EGAS50000001013

• Nanopore sequencing of blood, saliva, buccal mucosa samples of patients with inherited retinal disorders

  X-linked genetic disorders typically affect females less severely than males due to the presence of a second X chromosome not carrying the deleterious variant. However, the phenotypic expression in females is highly variable, which may be explained by an allelic skew in X chromosome inactivation. Accurate measurement of X inactivation skew is crucial to understand and predict disease phenotype in carrier females, with prediction especially relevant for degenerative conditions. We propose a novel approach using nanopore sequencing to quantify skewed X inactivation accurately. By phasing sequence variants and methylation patterns, this single assay reveals the disease variant, X inactivation skew, its directionality, and is applicable to all patients and X-linked variants. Enrichment of X-chromosome reads through adaptive sampling enhances cost-efficiency. Our study includes a cohort of 16 X-linked variant carrier females affected by two X-linked inherited retinal diseases: choroideremia and RPGR-associated retinitis pigmentosa. As retinal DNA cannot be readily obtained, we instead determine the skew from peripheral samples (blood, saliva and buccal mucosa), and correlate it to phenotypic outcomes. This revealed a strong correlation between X inactivation skew and disease presentation, confirming the value in performing this assay and its potential as a way to prioritise patients for early intervention, such as gene therapy currently in clinical trials for these conditions. Our method of assessing skewed X inactivation is applicable to all long-read genomic datasets, providing insights into disease risk and severity and aiding in the development of individualised strategies for X-linked variant carrier females.

  Study EGAS50000000440