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• Reliable detection of somatic mutations in solid tissues by laser-capture microdissection and low-input DNA sequencing

  Somatic mutations accumulate in healthy tissues as we age, giving rise to cancer and potentially contributing to ageing. To study somatic mutations in non-neoplastic tissues, we developed a series of protocols to sequence the genomes of small populations of cells isolated from histological sections. Here, we describe a complete workflow that combines laser-capture microdissection (LCM) with low-input genome sequencing, whilst circumventing the use of whole-genome amplification (WGA). The protocol is subdivided broadly into 4 steps: tissue processing, LCM, low-input library generation and mutation calling and filtering. The tissue processing and LCM steps are provided as general guidelines which may require tailoring based on the specific requirements of the study at hand. Our protocol for low-input library generation utilises enzymatic rather than acoustic fragmentation to generate WGA-free whole-genome libraries. Finally, the mutation calling and filtering strategy has been adapted from previously published protocols to account for artefacts introduced via library creation. To date, we have used this workflow to perform targeted and whole-genome sequencing of small populations of cells (typically 100-1,000 cells) in thousands of microbiopsies from a wide range of human tissues. The low-input DNA protocol is designed to be compatible with liquid handling platforms and make use of equipment and expertise standard to any core sequencing facility. However, obtaining low-input DNA material via LCM requires specialized equipment and expertise. The entire protocol from tissue reception through whole-genome library generation can be accomplished in as little as a week, though 2-3 weeks would be a more typical turnaround time.

  Dataset EGAD00001006088

• A108757B

  Whole genome sequencing for single cells for library A108757B 1644 cells; filetype=bam

  Dataset EGAD00001007086

• Phylogenetic reconstruction of adult blood cancer reveals early origins and lifelong evolution - WGS

  Mutations in cancer-associated genes drive tumour outgrowth. However, the timing of driver mutations and dynamics of clonal expansion that lead to human cancers are largely unknown. We used 580,133 somatic mutations from whole-genome sequencing of 1013 clonal haematopoietic colonies to reconstruct the phylogeny of haematopoiesis, from embryogenesis to clinical disease, in 12 patients with myeloproliferative neoplasms which are blood cancers more common in older age. JAK2V617F, the pathognomonic mutation driving the majority of these cancers, was acquired in utero or childhood, with upper estimates of age of acquisition from 33 weeks gestation to 10.8 years, in all 5 patients in whom JAK2V617F was either the only or the first driver event.  Driver mutations associated with age-related clonal haematopoiesis occurred prior to or following JAK2V617F,  as independent clonal expansions in JAK2V617F-mutated patients, and as large clonal expansions in JAK2V617F-unmutated patients . These mutations were also acquired in utero or childhood, with DNMT3A mutations occurring by 8 weeks of gestation to 7.6 years across 4 patients, and PPM1D mutation occurring by age 5.8yrs in a patient with MPN lacking phenotypic driver mutations. Sequential driver mutation acquisition was common, separated by decades across life, and often outcompeted ancestral clones. The mean latency between JAK2V617F acquisition and clinical presentation was 30 years (range 11-54 years). Rates of clonal expansion were inferred from phylogenetic trees and varied substantially (3% to 190% expansion/year), were affected by additional driver mutations, and were predictive of latency to clinical presentation. Driver mutations and rates of expansion would have been detectable in blood one to four decades before clinical presentation. This study reveals how driver mutation acquisition early in life with life-long growth and evolution underlie adult myeloproliferative neoplasms, providing opportunities for early detection and intervention and a new paradigm for cancer development.

  Dataset EGAD00001007714

• GDAP - Genome Diversity in Africa Project (2021-02-12)

  The Genomic Diversity in Africa Project (GDAP) started with the plan to develop a genomic resource from African populations, characterise genomic diversity and population history, and facilitate clinical studies in Africa. Currently, 25 individuals from 24 ethnolinguistic groups have been whole-genome sequenced at high depth totalling 585 individuals. An additional 41 individuals have been sequenced with 10X Genomics libraries. At this stage, the initial curation of this dataset has been finished and we are performing the analysis in coordination with our collaborators. The current state of the GDAP represents a very diverse panel of African populations that maximizes geographical and ethnic variation and represents a great starting point to achieve the aforementioned goals. However, southern sub-Saharan countries, Bantu speakers and hunter gatherer groups are currently underrepresented, despite being crucial to understand the evolutionary history of the continent. After extensive effort to collate studies documentation, we finally have the opportunity to sequence 600 new individuals from these groups, including countries as Gabon, Rwanda and Zambia, and address these deficiencies. We aim to proceed with the same strategy: to sequence at high depth 25 individuals with standard PCR free libraries, with 2 additional individuals with 10X Genomics Chromium libraries per ethnolinguistic group. The former allows a good representation of variants down to low frequency in any given population, and the latter allows accurate phasing and the analysis of structural variation. By including these new populations, we want to investigate three crucial questions in African history in addition to the initial objectives: the Bantu expansion, the evolutionary history of hunter gatherers and the transatlantic slave trade. Additionally, the expanded dataset will help us better discover the genetic variation present in Africa and characterize the African pangenome. 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 2021-02-12.

  Dataset EGAD00001006965

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• Discovering the Genetic Basis of Human Neuroblastoma: A Gabriella Miller Kids First Pediatric Research Program (Kids First) Project

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Children with disseminated neuroblastoma have a very high risk of treatment failure and death despite receiving intensified chemotherapy, radiation therapy and immunotherapy. The long-term goal of our research program is to ultimately improve neuroblastoma cure rates by first comprehensively defining the genetic basis of the disease. The central hypothesis to be tested here is that neuroblastoma arises largely due to the epistatic interaction of common and rare heritable DNA variation. Here we will perform a comprehensive whole genome sequencing of 563 quartets of neuroblastoma patient germline and diagnostic tumor DNAs and germline DNAs from both parents. The case series was recently collected through a Children's Oncology Group epidemiology clinical trial and is robustly annotated with complete demographic (age, sex, race, ethnicity), clinical (e.g. age at diagnosis, stage, risk group), epidemiologic (parental dietary and exposure questionnaire) and biological (e.g. tumor MYCN status and multiple other tumor genomic measures) co-variates. Subjects were consented for genetic research and DNA is immediately available for shipment for sequencing. We propose Illumina-based whole genome sequencing in the 593 "trio" germline samples (Aim 1; due to missing parent: 487 full neuroblastoma triads, 106 child-single parent dyads = 1673 whole genome sequences) and matched diagnostic tumor DNA (Aim 2; N=366) at 30x sequencing depth (N=2039 whole genome sequences). Also in Aim 2 we will perform whole exome (100x) and RNA sequencing on the 366 tumor DNA and 228 tumor RNA samples from this cohort. Finally, we propose a pilot study of structural variation using long-range sequencing in 10 non-overlapping tumor samples chosen based on potentially relevant chromosomal alterations discovered with conventional NGS. Thus, a total of 2277 individual samples and 2655 sequences will be generated. We will use our established analytic pipeline that is currently being used to study the germline genomes of all cases sequenced through the NCI supported Therapeutically Applicable Research to Generate Effective Treatments program. We plan a three-stage analytic approach, first focusing on classic de novo and inherited Mendelian damaging alterations. We will next integrate our extensive epigenomic data from human neuroblastoma cell lines and genome-wide association study data (N=5,703 neuroblastoma cases to date) to guide a comprehensive assessment of noncoding variants that influence tumor initiation with a recently established analytic pipeline. Finally, we will utilize the tumor DNA analyses to inform relevance via somatic gain or loss of function effects at the sequence and/or copy number levels. All data generated in this project will be immediately placed into the Genomic Data Commons (GDC) and we will compute within this environment by importing our analytic pipelines into the GDC. These data will be fully integrated into the Kids First Data Resource and freely shared with all academically qualified petitioners. This comprehensive data set derived from a large and richly phenotyped series of neuroblastoma DNA quartets will be integrated with existing germline and/or tumor genomic data from over 6,000 neuroblastoma subjects (but none with matched patient-parent germline sequencing data) to provide an unparalleled opportunity to comprehensively discover the genetic basis of neuroblastoma.

  Study phs001436

• The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension

  Study Design: The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension contains N=797 participants. At the time of recruitment individuals ranged from 15-84 years of age and had varying ethnic backgrounds (Whites, African-Americans and Hispanics). The cohort was developed to study the genetic basis of autonomic dysfunction in human essential hypertension (reviewed in Zhang et al. Curr Hypertens Rep. 2011 13: 36-45. PMID: 21104344) and contains cross-sectionally collected phenotypes related to blood pressure regulation.  Phenotypes: Phenotype data specifically focuses on the sympathetic branch of the autonomic system, which is a key regulator of blood pressure. Data includes circulating concentrations of the secretory "quantum" (catecholamines, neuropeptides, chromogranins) which influence vascular responses to sympatho-adrenal activation resulting in blood pressure. Other measures on the twins and a subset of family members include a standard urine panel with absolute concentrations of potassium, sodium, uric acid, phosphorous; DynaPulse acquired heart rate and blood pressure measures including systolic and diastolic blood pressure, mean arterial pressure, heart rate, Left Ventricular (LV) ejection time, LV contractility, cardiac output, stroke volume data including; and absolute concentrations of plasma aldosterone, endothelin, leptin, and insulin. Phenotype data is included for the 697 individuals sequenced here and 100 family members.  Whole Genome Sequence Data Generation: Whole genome sequencing (20X coverage) was conducted on plasma-derived DNA. The WGS was generated using the Illumina HiSeq X Ten sequencing platform with variants called by Human Longevity, Inc (HLI; Ref 1). Preparation of sample libraries was conducted by mechanical shearing of gDNA (100-800 bp), isolation of DNA fragments 300-500bp in size, followed by library PCR amplification employing high-fidelity, low-bias PCR to amplify library fragments consisting of appropriate adapter sequences on both ends. BAM files were created by alignment of sequence reads to the reference genome (GRCh38) using iSAAC-01.14.02.06 (Python2.7). Note: The user may want to realign the sequence data and recall variants. In total, the WGS dataset contains 30 monozygotic twin pairs represented by one member of the twin pair, 133 monozygotic twin pairs with both individuals sequenced, 7 dizygotic twin pairs represented by one member of the twin pair, 67 dizygotic twin pairs with both individuals sequenced, 8 twin pairs with undetermined zygosity (both individuals sequenced) and 1 triplet with mother (all individuals sequenced). The dataset also contains sequences of 260 family members of twins including their mother, father, non-twin sibling, and offspring. Table 1 describes the composition of families ("sibling" refers to a non-twin sibling in the cohort). Note: We suggest users check gender and family relatedness. Table 1: Description of the families with sequence data                                                                                                               Units with:Twin PairsTwin (no pairs)No twinSibshipsMother and Father511Mother only1222Father only113One or more siblings22133Mother and siblings132Father and siblings(s)211Offspring of twins11Non twin siblings2Sib with unknown relative1Only one member of8The UCSD/O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension is now being managed at the UCSD School of Medicine's Institute for Genomic Medicine (IGM). The TSP Resource has collected a wide range of information (phenotypic, genotypic, demographic, metabolomic) and reagents (blood, plasma, urine, DNA, human induced pluripotent stem cells). For additional information about the resource, including other data types available as well as references and links to data from studies with previous collaborators, please go to the resource website at IGM [http://igm.ucsd.edu/research/twins-resource.shtml] or contact Dr. Brinda Rana at UCSD [bkrana@ucsd.edu]. 

  Study phs002230

• Functional Multiomics of Cellular Therapy and Immune Checkpoint Blockade Therapy for Solid Tumors

  Tumor samples were collected from a patient with synovial sarcoma, which acquired resistance to ACT targeting NY-ESO-1. Biopsies (n=3; primary, metastasis, and recurrence) were subjected to bulk tumor DNA and RNA sequencing, as well as high-dimensional spatial profiling of RNA and protein targets. Bulk tumor whole exome and RNA sequencing corresponding to all three tumor specimens and whole exome sequencing from patient-matched normal blood are made available through this accession.

  Study phs002762

• Genome Sequencing of Familial Cholangiocarcinoma for the Identification of Germline Risk Alleles

  In this study, sequencing data (WES, WGS, linked-read WGS) was used to identify candidate causal germline variants in a family with inherited cholangiocarcinoma. Candidate causal SNVs and indels were identified from the germline WES data of eight siblings (four affected, four unaffected), then somatic second hits were identified from matched tumor/normal pairs. Second hits were verified using haplotype information derived from linked-read WGS of the tumor data.

  Study phs001593

• FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data

  We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts such as misalignments or random pairing of transcript fragments and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including 8 cancers with and without known rearrangements.

  Study phs000311