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• Single cell transcriptional evolution of myeloid leukaemia of Down syndrome – scRNA

  Abstract: Children with Down syndrome have a 150-fold increased risk of developing myeloid leukaemia (ML-DS). Unusually for a childhood leukaemia, ML-DS arises from a preleukaemic state, termed transient abnormal myelopoiesis (TAM), and via a conserved sequence of mutations. Here, we examined the relationship between the genetic and transcriptional evolution of ML-DS from a rich collection of primary patient samples through single cell mRNA sequencing, complemented by phylogenetic analyses in progressive disease. We distilled the transcriptional consequence of each genetic step in the evolution of ML-DS, showing that TAM-defining GATA1 mutations account for most of the ML-DS transcriptome, including those of progressive disease. This transcriptional backbone may thus represent a common vulnerability in ML-DS. We extracted the transcriptional difference between TAM and ML-DS which, unexpectedly, reflected features shared across childhood leukaemia. Our approach delineates the transcriptional evolution of ML-DS and provides an analytical blueprint for distilling consequences of mutations directly from patient samples.

  Dataset EGAD00001015452

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - DNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009812

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Dataset EGAD00001006576

• FASTQ files of the small RNA-Seq dataset from the POPS cohort

  Placental biopsies were collected within 30 minutes of birth and flash frozen in RNAlater(ThermoFisher). For each biopsy,total placental RNA was extracted from approximately 5 mg of tissue using the “mirVana miRNA Isolation Kit” (Ambion) followed by DNase treatment (“DNA-free DNA Removal Kit”, Ambion). RNA quality was assessed with the Agilent Bioanalyzer and all the samples with RIN values ≥ 7.0 were used in the downstream experiments. Total RNA-libraries were prepared from 300-500ng of total placental RNA with the “TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat”(Illumina). Small RNA-libraries were prepared from 150ng of total placental RNA with the “NEBNext Multiplex Small RNA Library Prep Kit for Illumina”(New England Biolabs)and concentrated using the “QIAquick PCR purification kit”(Qiagen). Paired libraries were combined and size selected using the Pippin Prep and 3% Agarose Gel Cassette with marker F (Sage Science),pooled and sequenced (single-end, 50bp) using a Single End V4 cluster kit and HiSeq4000 instrument.

  Dataset EGAD00001004860

• 300BCG study ATAC-seq data: human population variation of trained immunity

  ATAC-seq dataset for the paper: Title Multi-omics analysis of human population variation in immune function and in vivo response to BCG vaccination Abstract Immune responses are tightly regulated, yet highly variable between individuals. To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live attenuated vaccine induces not only an adaptive immune response against tu-berculosis, but also triggers innate immune activation and memory. We established personal immune profiles and chromatin accessibility maps over a time course of BCG vaccination in 323 individuals. This large resource uncovered genetic and epigenetic predictors of baseline immunity and BCG vaccine response. We found that BCG vaccination enhances the innate immune response only in individuals with dormant immune states at baseline, suggesting that exogeneous induction of trained immunity is not a universal booster of innate immunity, but specifically elevates weak innate immune responses. This study advances our understanding of BCG’s heterologous immune-stimulatory effects and trained immunity in humans. Moreover, our results highlight the value of epigenetic cell states as an “endo-phenotype” that connects immune function with genotype and the environment.

  Dataset EGAD50000000123

• Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

  Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. We now define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. We leverage the insulin response heterogeneity and use transcriptomics and proteomics to identify molecular pathways of specific nutrient responsiveness. Surprisingly, we find robust insulin secretion to fatty acid stimulus in ~8% of donors, challenging the idea that fat has negligible effects on insulin release. Distinct islet proteomes with differences in metabolic signalling networks convey this hyper-responsiveness to fat relative to carbohydrate. By comparing human islets to human embryonic stem cell-derived islet clusters, we show that, unlike glucose-responsiveness, fat hyper-responsiveness is equivalent and may be a hallmark of functionally immature cells. Our study represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people’s islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition.

  Dataset EGAD50000000519

• TSO500 STJAN33, BRAF mutated CUP

  The data set contains FASTQ files (filetype) of a NEXTSEQ550DX run (instrument). De FASTQ files are from DNA and RNA sample. The Library prep is a NGS TSO500 library prep (Illumina) (technology). A Study to Examine the Clinical Value of Comprehensive Genomic Profiling Performed by Belgian NGS Laboratories: a Belgian Precision Study of the BSMO in Collaboration With the Cancer Centre (BALLETT) This 2-year study involves the consortium of 9 cooperating Belgian NGS laboratories and will enroll 936 metastatic or locally advanced cancer patients coming from 13 different Belgian hospitals and cancer centers. Upon inclusion, all cancer patients will be offered 'comprehensive genomic profiling' (CGP) using Illumina's TSO500 NGS panel. This targeted NGS panel of 523 genes allows for the detection of single nucleotide variants, small indels, copy number variations and fusions, as well as for the determination of the 'tumor mutational burden' (TMB) and the 'microsatellite-instability' status (MSI). Both the wet lab execution of the CGP as well as the biological and clinical classification of the variants will be performed in a fully standardized way among the 9 participating Belgian local NGS laboratories.

  Dataset EGAD50000000689

• Spatial predictors of response to immunotherapy in microsatellite stable metastatic colorectal cancer

  Microsatellite stable (MSS) colorectal cancers (CRC) are largely unresponsive to immune checkpoint inhibition (ICI), prompting investigation into strategies to enhance sensitivity. The MAYA trial, which utilized temozolomide (TMZ) in MGMT-silenced MSS mCRC, hypothesized that TMZ-induced hypermutation could sensitize tumors to ICI. This phase II trial met its primary endpoint, demonstrating durable clinical responses with TMZ combined with ipilimumab and nivolumab. To elucidate factors in uencing response heterogeneity, we conducted multi-omic spatial pro ling of samples from patients who participated in the MAYA trial, including baseline and on-treatment tissue and blood specimens. While increased neoantigen load following TMZ exposure did not consistently predict for deep responses, spatial profiling revealed key determinants. Lymphocyte proportions, particularly CD8+KI67+ cells, within stromal and tumor compartments, along with macrophage composition (CD68+CD163+ cells) at the tumor-stromal interface, were predictive of response. Treatment pressures dynamically altered the tumor microenvironment composition and activated peripheral immune cells. This study is the first to identify spatial predictors of response to this promising novel treatment approach for MSS CRC.

  Dataset EGAD50000002244

• An Integrated Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia

  As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we developed and validated a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of RNA-Seq to whole genome and exome sequencing revealed that as a standalone assay, RNA-Seq offered the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data were used to develop a novel risk score which, when combined with molecular risk guidelines, allowed for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identified a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase (PTK2), demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

  Dataset EGAD00001006701

• PopCol 16S gut microbiome sequencing

  PopCol is a cohort study in Stockholm, Sweden that includes a data-rich set of individuals with data available from bowel symptoms questionnaires, gastroenterology visits and biospecimens (genotype and 16S sequencing from blood and stool samples, respectively). Genotyping was carried out using the Illumina HumanOmniExpressExome-8v1 arrays at the SciLifeLab NGI facility in Uppsala, Sweden. Fecal DNA was extracted from samples kept at -80°C using Qiagen 5 QIAamp DNA Stool Mini Kits and analyzed using 16S rRNA gene amplicon sequencing (in the V1-V2 hypervariable region). This was performed on the Illumina MiSeq platform at the Institute of Clinical Molecular Biology (IKMB) in Kiel, Germany. Of these, six PopCol participants were PPI users and 12 used antibiotics. The study was approved by the local Committee of Research Ethics (Forskningskommitté Syd) at Karolinska Institutet, Stockholm, in November 2001. Written informed consent was obtained from all participants

  Dataset EGAD00001007071

• Glioblastoma stem cell lines RNA-seq (Guilhamon et al.)

  RNA was extracted from GSCs using the Qiagen RNeasy Plus kit. RNA sample quality was measured by Qubit (Life Technologies) for concentration and by Agilent Bioanalyzer for RNA integrity. All samples had RIN above 9. Libraries were prepared using the TruSeq Stranded mRNA kit (Illumina). Two hundred nanograms from each sample were purified for polyA tail containing mRNA molecules using poly-T oligo attached magnetic beads, then fragmented post-purification. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. This is followed by second strand cDNA synthesis using RNase H and DNA Polymerase I. A single “A” base was added and adapter ligated followed by purification and enrichment with PCR to create cDNA libraries. Final cDNA libraries were verified by the Agilent Bioanalyzer for size and concentration quantified by qPCR. All libraries were pooled to a final concentration of 1.8nM, clustered and sequenced on the Illumina NextSeq500 as a pair-end 75 cycle sequencing run using v2 reagents to achieve a minimum of ~40 million reads per sample.

  Dataset EGAD00001006813

• bulk RNA sequencing, single cell RNA sequencing and nanopore sequencing of T-ALL patients with TCF7-SI1 fusion

  This dataset contains all sequencing data of the publication "Oncogenic cooperation between the TCF7-SPI1 fusion and NRAS(G12D) requires β-catenin activity to drive T-cell acute lymphoblastic leukemia." This is bulk RNA sequencing of 4 T-ALL patients (X09, XB37, XB41 and XB47) of which X09 has a TCF7-SPI1 fusion, single cell RNA sequencing of these 4 patients toghether with a PDX model of the X09 patient and two patients from another cohort (SJTALL030263 and SJTALL031201) which also have a TCF7-SPI1 fusion, and nanopore sequencing of all patients with the TCF7-SPI1 fusion. Moreover these patient samples with the fusion where treated with PKF 118-310, and bulk RNA sequencing was performed in triplicate to determine the differentially expressed genes.

  Dataset EGAD00001007010

• Neuroblastoma patient Total RNA Seq data

  Tumor Total RNA Seq data of primary neuroblastomas. This is an update of the „Berlin Neuroblastoma Dataset” (EGAS00001004022). This data was used for the analysis of circular RNA expression and regulation in neuroblastoma.

  Dataset EGAD00001008124

• Dataset for the Pearl study

  Biomarkers to identify patients without benefit from adding everolimus to endocrine treatment in metastatic breast cancer (MBC) are needed. We report the results of the Pearl trial conducted in five Belgian centers assessing 18F-FDG-PET/CT non-response (n=45) and ctDNA detection (n=46) after 14 days of exemestane-everolimus (EXE-EVE) to identify MBC patients who will not benefit. Metabolic non-response rate was 66.6%. Median PFS in non-responding patients (using as cut-off 25% for SUVmax decrease) was 3.1 months compared to 6.0 months in those showing response (HR: 0.77, 95% CI: 0.40-1.50, p=0.44). Difference was significant when using a “post-hoc” cut-off of 15% (PFS 2.2 months vs 6.4 months). ctDNA detection at D14 was associated with PFS: 2.1 months vs 5.0 months (HR-2.5, 95% CI: 1.3-5.0, p=0.012). Detection of ctDNA and/or the absence of 18F-FDG-PET/CT response after 14 days of EXE-EVE identifies patients with a low probability of benefiting from treatment. Independent validation is needed.

  Dataset EGAD00001007979

• Single-Cell Atlas of Common Variable Immunodeficiency shows germinal center-associated epigenetic dysregulation in B cell responses

  Common variable immunodeficiency (CVID) is the most prevalent primary immunodeficiency. Here the authors perform single cell omics analyses in CVID discordant monozygotic twins and show epigenetic and transcriptional alterations associated with activation in memory B cells.

  Dataset EGAD00001008575

• Ovarian Cancer Organoid Biobank - RNASeq data

  Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004509

• Analysis of the B cell receptor repertoire in six immune-mediated diseases

  Our understanding of the BCR repertoire in the context of immune-mediated diseases is incomplete, and defining this could provide new insights into pathogenesis and therapy. Here, we compared the BCR repertoire in systemic lupus erythematosus, anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, Crohn’s disease, Behçet’s disease, eosinophilic granulomatosis with polyangiitis, and immunoglobulin A (IgA) vasculitis by analysing BCR clonality, use of immunoglobulin heavy-chain variable region (IGHV) genes and—in particular—isotype use. An increase in clonality in systemic lupus erythematosus and Crohn’s disease that was dominated by the IgA isotype, together with skewed use of the IGHV genes in these and other diseases, suggested a microbial contribution to pathogenesis. Different immunosuppressive treatments had specific and distinct effects on the repertoire; B cells that persisted after treatment with rituximab were predominately isotype-switched and clonally expanded, whereas the inverse was true for B cells that persisted after treatment with mycophenolate mofetil. Our comparative analysis of the BCR repertoire in immune- mediated disease reveals a complex B cell architecture, providing a platform for understanding pathological mechanisms and designing treatment strategies.

  Dataset EGAD00001005431

• Genetic landscape of SMM

  Identifying high risk smoldering myeloma patients and progression mechanism is a prerequisite to implement effective inception strategies and curve myeloma related morbidity and mortality. We hypothesize that genomics may help identify determinants of progression that may help predict outcome and offer effective chemo preventive targets. Eighty-two patients underwent a custom targeted panel sequencing with an additional capture for the translocation loci. These results were compared to 223 newly diagnosed patients (EGAS00001003223 and EGAD00001004117) and 17 MGUS and 10 early myeloma patients. DNA was obtained from either CD138+ cells from the bone marrow of multiple myeloma patients (tumor) or from stem cell harvests or peripheral blood cells from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using the HyperPlus kit (KAPA Biosystems). After PCR amplification the libraries were hybridized with probes against either a targeted panel consisting of 140 genes and chromosomal regions (Nimblegen) using SeqCap reagents (Nimblegen). Hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads. Overall, these data highlight the importance of dysregulation of the MAPK pathway in the progression to MM.

  Dataset EGAD00001005285

• Chromatin accessibility maps of chronic lymphocytic leukemia identify subtype-specific epigenome signatures and transcription regulatory networks

  Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research.

  Dataset EGAD00001002110

• Somatic mutation and clonal evolution normal breast tissue TGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005787

• Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging

  In this study we characterised the consequences of DNMT3A- and TET2-mutations in human clonal haematopoiesis. We screened 195 bone marrow samples for mutations by targeted DNA sequencing, and sequenced 99 paired peripheral blood samples to compare mutation detection between bone marrow and blood. We performed single-cell analysis with TARGET-seq+ on 13 samples (4 controls and 9 clonal haematopoiesis samples), which combines single-cell genotyping for targeted mutations with whole transcriptome sequencing on FACS sorted cells. Single-cell genotyping and transcriptomes were sequenced separately and linked by common single-cell identifiers.

  Study EGAS00001007358

• The Thrifty Microbiome: The Role of the Gut Microbiota in Obesity in the Amish

  Emerging evidence that the gut microbiota may contribute in important ways to human health and disease has led us and others to hypothesize that both symbiotic and pathological relationships between gut microbes and their host may be key contributors to obesity and the metabolic complications of obesity. Our "Thrifty Microbiome Hypothesis" poses that gut microbiota play a key role in human energy homeostasis. Specifically, constituents of the gut microbial community may introduce a survival advantage to its host in times of nutrient scarcity, promoting positive energy balance by increasing efficiency of nutrient absorption and improving metabolic efficiency and energy storage. However, in the presence of excess nutrients, fat accretion and obesity may result, and in genetically predisposed individuals, increased fat mass may result in preferential abdominal obesity, ectopic fat deposition (liver, muscle), and metabolic complications of obesity (insulin resistance, hypertension, hyperlipidemia). Furthermore, in the presence of excess nutrients, a pathological transition of the gut microbial community may occur, causing leakage of bacterial products into the intestinal lymphatics and portal circulation, thereby inducing an inflammatory state, further aggravating metabolic syndrome traits and accelerating atherosclerosis. This pathological transition and the extent to which antimicrobial leakage occurs and causes inflammatory and other maladaptive sequelae of obesity may also be influenced by host factors, including genetics. In the proposed study, we will directly test the Thrifty Microbiome Hypothesis by performing detailed genomic and functional assessment of gut microbial communities in intensively phenotyped and genotyped human subjects before and after intentional manipulation of the gut microbiome. To address these hypotheses, five specific aims are proposed: (1) enroll three age- and sex-matched groups from the Old Order Amish: (i) 50 obese subjects (BMI > 30 kg/m2) with metabolic syndrome, (ii) 50 obese subjects (BMI > 30 kg/m2) without metabolic syndrome, and (iii) 50 non-obese subjects (BMI < 25 kg/m2) without metabolic syndrome and characterize the architecture of the gut microbiota from the subjects enrolled in this study by high-throughput sequencing of 16S rRNA genes; (2) characterize the gene content (metagenome) to assess the metabolic potential of the gut microbiota in 75 subjects to determine whether particular genes or pathways are correlated with disease phenotype; (3) characterize the transcriptome in 75 subjects to determine whether differences in gene expression in the gut microbiota are correlated with disease phenotype, (4) determine the effect of manipulation of the gut microbiota with antibiotics on energy homeostasis, inflammation markers, and metabolic syndrome traits in 50 obese subjects with metabolic syndrome and (5) study the relationship between gut microbiota and metabolic and cardiovascular disease traits, weight change, and host genomics in 1,000 Amish already characterized for these traits and in whom 500K Affymetrix SNP chips have already been completed. These studies will provide our deepest understanding to date of the role of gut microbes in terms of 'who's there?', 'what are they doing?', and 'how are they influencing host energy homeostasis, obesity and its metabolic complications? PUBLIC HEALTH RELEVANCE: This study aims to unravel the contribution of the bacteria that normally inhabit the human gastrointestinal tract to the development of obesity, and its more severe metabolic consequences including cardiovascular disease, insulin resistance and Type II diabetes. We will take a multidisciplinary approach to study changes in the structure and function of gut microbial communities in three sets of Old Order Amish patients from Lancaster, Pennsylvania: obese patients, obese patients with metabolic syndrome and non-obese individuals. The Old Order Amish are a genetically closed homogeneous Caucasian population of Central European ancestry ideal for genetic studies. These works have the potential to provide new mechanistic insights into the role of gut microflora in obesity and metabolic syndrome, a disease that is responsible for significant morbidity in the adult population, and may ultimately lead to novel approaches for prevention and treatment of this disorder.

  Study phs000258

• Prematurity and Respiratory Outcomes Program (PROP) Core Database Protocol (PROP-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: The Prematurity and Respiratory Outcomes Program (PROP) was performed to identify suitable predictors of respiratory outcomes that may serve as surrogate endpoints in future trials of prevention and therapy of respiratory diseases in preterm infants.Background: Acute and chronic respiratory morbidities are common in premature births, and can pose significant risk to the infant's health, particularly during the first two years of life. One such condition is bronchopulmonary dysplasia (BPD) where infants require oxygen therapy due to abnormal repair and impaired lung development after acute lung injury. BPD poses a high mortality risk, even in infants that survive the initial hospitalization. Regardless of BPD diagnosis, preterm infants frequently return for medical care due to symptoms of post-prematurity respiratory disease (PRD), which includes intermittent or chronic wheezing, cough without cold, poor growth, apnea and cyanosis, and lower respiratory tract infections. Impaired lung function can persist into adulthood, contributing to chronic respiratory diseases including asthma and emphysema.However, at the time of the PROP study, there were no objective measures to predict which preterm infants would have persistent respiratory problems after discharge from the hospital. Furthermore, improved survival rates of premature infants and the high prevalence of lasting respiratory morbidities highlight the need for more comprehensive strategies to address both short-term and long-term respiratory complications. Thus, the Prematurity and Respiratory Outcomes Program (PROP) was formed to investigate multiple research hypotheses on the molecular mechanisms that contribute to respiratory disease risk of premature neonates over the first year of life. Specifically, PROP investigators hypothesized that in survivors of extreme prematurity to 36 weeks postmenstrual age (PMA), specific biologic, physiologic and clinical data obtained during the initial hospitalization will predict respiratory morbidity between discharge and 1 year corrected age. Participants: A total of 835 infants were enrolled.Design: PROP was a collaboratively developed multicenter prospective cohort study of very preterm infants from birth through the time of discharge from the NICU and up to one year corrected age.Clinical data included maternal and infant demographics, co-morbidities, and daily infant respiratory, nutritional, and medication data until discharge. These were prospectively collected from birth using medical record review and family interviews. After discharge, phone interviews were conducted with the infant's main caregiver at 3, 6, 9 and 12 months corrected age to assess domains of respiratory morbidity. At 6 and 12 months corrected age, a survey of environmental respiratory irritant exposures and an assessment for gastroesophageal reflux disease were also completed. A standardized physical exam was performed at 36-40 weeks PMA and again at one year corrected age. The exam focused on anthropometrics, vital signs, and respiratory system signs. At 34-41 weeks PMA and within one week of anticipated discharge based on physiologic stability, a set of non-invasive respiratory assessments were performed to assess physiologic biomarkers in infants that were not mechanically ventilated or receiving non-invasive positive pressure ventilation. Respiratory inductance plethysmography (RIP) was performed before and after inhaled albuterol to assess potential airway reactivity. During the RIP study, continuous pulse oximetry was performed during quiet sleep in order to analyze oxygen desaturations and apneas. Standardized oxygen requirement challenge tests were performed at about 36 weeks PMA, and at about 40 weeks PMA if the infant was still hospitalized and was not eligible for, or failed, the previous challenge. Failure was defined as SpO2 20 seconds at any point in the testing. Infants breathing ambient air or that passed the 36 week challenge test underwent a hypoxia challenge consisting of a 15-minute trial of FiO2 of 0.15. Failure was defined as SpO2 The primary outcome was respiratory morbidity, as measured by the presence or absence of substantial post-prematurity respiratory disease (PRD) up to one year corrected age. PRD was classified as a positive response in at least one of four morbidity domains during at least two separate parental interviews. The domains were respiratory medications, hospitalizations for cardiopulmonary cause, respiratory symptoms, and home technology dependence (oxygen, ventilator, or CPAP/BiPAP). Mortality from a respiratory cause was also incorporated.

  Study phs004117

• SEARCH for Diabetes in Youth Study - Genetic Risk Score

  Genetic risk scores (GRS) quantify polygenic disease risk into a single measure and can aid in disease classification. GRS studies have focused primarily on adult populations of recent European ancestry. We aimed to assess the utility of GRS in classification of diabetes type among racially/ethnically diverse youth in the United States. We used data from SEARCH baseline visits and follow up visits for which key data elements were available. 2260 participants are included. Phenotypic Data to be submitted here include: Self-reported sex, race and ethnicity Diabetes type determined by provider Etiologic diabetes type T1D (antibody positive, or antibody negative/missing and insulin sensitive) T2D (antibody negative and insulin resistant) Diabetes duration (years) Age at diagnosis (years) Measures from baseline, SEARCH 3 (follow-up) and SEARCH 4 (follow-up): Age Height Weight Waist circumference HbA1c Fasting/non-fasting glucose C-peptide Triglycerides HDL LDL Systolic Blood Pressure Diastolic Blood Pressure Measures from SEARCH 3 and 4:Arterial stiffness as characterized by PWVRetinopathy Measures from SEARCH 4 only: LV structure and diastolic function measurements from echocardiogram GAD autoantibody IAD autoantibody ZnT8 autoantibody We performed genotyping using the Illumina Multi-Ethnic Global Array (MEGA) array with 1,697,069 genotyped variants including 748,291 with minor allele frequency <0.01. Genotyping and preliminary quality control checks were performed at the Colorado Center for Personalized Medicine. After additional quality control, 2,238 samples and 900,743 variants remained for analyses. Samples genotyped on the MEGA array were categorized using SEARCH etiologic type and consisted of predominantly type 1 diabetes cases (n=2,051) but also those with other diabetes (n=133 type 2 diabetes, n=52 other diabetes including monogenic diabetes; genetic confirmation with either a genetic clinical test or test performed as an ancillary study to SEARCH). The median reported age at DNA collection was 11.2 years (interquartile range 7.6 – 14.1) with a minimum age of 1.9 years and maximum of 21.9 years. We used additional data genotyped on the Affymetrix 500K imputation scaffold chip with 239,279 genotyped variants. This cohort consisted of predominantly type 2 diabetes cases (n=417) but also those with type 1 (n=104), and other diabetes types (n=16). After additional quality control, 537 samples and 235,967 variants remained. The median reported age at collection was 11.2 years (quartile range 8.1 – 14.2) with a minimum of 2.0 years and a maximum of 21.1 years. About 100 type 2 diabetes cases were also genotyped using the MEGA chip to ensure concordance between the data sets and facilitate strand alignment between the two chips before genotypic imputation. The data sets had n=230,228 genotyped variants in common and concordance between the genotypes was high (mean correlation r2 for SNPs used in GRS=0.95). We used the 1000 Genomes reference panel to impute each data set separately, resulting in a total of 34.5M and 27.8M well-imputed variants (r2>0.8) for the MEGA and Affymetrix data sets respectively. We combined high quality imputed variants for analysis.

  Study phs002703

• Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. ObjectivesHypertension Study: To determine whether treatment with a calcium channel blocker or an angiotensin-converting enzyme inhibitor lowers the incidence of coronary heart disease (CHD) or other cardiovascular disease (CVD) events vs treatment with a diuretic.Lipid Study: To determine whether pravastatin compared with usual care reduces all-cause mortality in older, moderately hypercholesterolemic, hypertensive participants with at least an additional CHD risk factor. BackgroundHypertension Study: Antihypertensive therapy is well established to reduce hypertension-related morbidity and mortality, but the optimal first-step therapy is unknown.Lipid Study: Studies have demonstrated that statins administered to individuals with risk factors for coronary heart disease (CHD) reduce CHD events. However, many of these studies were too small to assess all-cause mortality or outcomes in important subgroups.ParticipantsHypertension Study: A total of 33,357 participants. Lipid Study: A total of 10,355 participants. Baseline mean total cholesterol was 224 mg/dL; LDL-C, 146 mg/dL; high-density lipoprotein cholesterol, 48 mg/dL; and triglycerides, 152 mg/dL. Mean age was 66 years; 49% were women, 38% black and 23% Hispanic, 14% had a history of CHD, and 35% had type 2 diabetes. DesignHypertension Study: A randomized, double-blind, active-controlled clinical trial conducted from February 1994 through March 2002. Participants were randomly assigned to receive chlorthalidone, 12.5 to 25 mg/d (n = 15,255); amlodipine, 2.5 to 10 mg/d (n = 9048); or lisinopril, 10 to 40 mg/d (n = 9054) for planned follow-up of approximately 4 to 8 years. The primary outcome was combined fatal CHD or nonfatal myocardial infarction, analyzed by intent-to-treat. Secondary outcomes were all-cause mortality, stroke, combined CHD (primary outcome, coronary revascularization, or angina with hospitalization), and combined CVD (combined CHD, stroke, treated angina without hospitalization, heart failure (HF), and peripheral arterial disease).Lipid Study: Multicenter (513 primarily community-based North American clinical centers), randomized, non-blinded trial conducted from 1994 through March 2002 in a subset of participants from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). The interventions in the trial were pravastatin, 40 mg/d, vs usual care. The primary outcome was all-cause mortality, with follow-up for up to 8 years. Secondary outcomes included non-fatal myocardial infarction or fatal CHD (CHD events) combined, cause-specific mortality, and cancer.ConclusionsHypertension Study: Thiazide-type diuretics are superior in preventing one or more major forms of CVD and are less expensive. They should be preferred for first-step antihypertensive therapy.Lipid Study: Pravastatin did not reduce either all-cause mortality or CHD significantly when compared with usual care in older participants with well-controlled hypertension and moderately elevated LDL-C. The results may be due to the modest differential in total cholesterol (9.6%) and LDL-C (16.7%) between pravastatin and usual care compared with prior statin trials supporting cardiovascular disease prevention.

  Study phs004021

• The Pioneer 100 Wellness Project (P100)

  108 individuals (age 21-89+ years; 59% males, 41% females; 89% Caucasian; not recruited based on any specific phenotype) participated in an IRB-approved study from April 2014 to January 2015. Each individual had the genome sequenced in full. Blood was collected in clinics every three months. Additionally, participants completed at-home collections of saliva, stool, and first morning void urine every three months. Stool and saliva samples were shipped directly to the vendor by the participant, while urine was given back to the study coordinators for distribution to the proper sample vendor. For each successful participant in each round we carried out 218 clinical laboratory tests, measured up to 643 metabolites and 262 proteins, and measured the abundance of 4616 operational taxonomic units (OTUs) in the gut microbiome using 16S rRNA sequencing. The P100 study had four objectives: 1) Establish cost-efficient procedures for generating, storing and analyzing multiple sources of health data obtained over time from participants and analyzed in combination with genomic data; 2) Develop and deploy analytic tools for integrating these diverse datasets and deriving actionable information from their observed interrelationships; 3) Identify novel patterns within the streams of health data that indicate either wellness, or transitions between wellness and disease; 4) Learn how to best interface with and present longitudinal health information to individuals by studying the reactions and feedback from participants as they are presented actionable information.

  Study phs001363

• Cellular Indexing of Transcriptomes and Epitopes Sequencing (CITE-Seq) Analysis to Investigate the Impact of Granulocyte-Colony Stimulating Factor on CRISPR/Cas9 Gene Edited Human Hematopoietic Stem Cell Function

  Our research using in-vivo preclinical xenograft mouse models demonstrated that G-CSF impedes engraftment of CRISPR-Cas9 gene edited human hematopoietic stem cells (HSCs). In this CITE-seq study, we sought to investigate the mechanisms by which G-CSF negatively impacts long-term repopulating functions of CRISPR-Cas9 gene edited human HSCs. Accordingly, human CD34+ cells were obtained from a healthy volunteer and edited with Cas9 alone (Cas9 group), Cas9+AAVS1-targeting gRNA (RNP group), or Cas9+AAVS1-targeting gRNA+GSE56 (RNP/GSE56 group). Then, these edited cells were transplanted into busulfan-conditioned NSG mice, and these mice subsequently received 3 days of phosphate buffered saline (PBS) or G-CSF. Each mouse was subsequently euthanized and harvested for bone marrow cells and human CD34+cells were isolated using immunomagnetic beads. Isolated CD34+ cells from a total of 6 mice were individually stained with Total seq B CITE-seq antibodies (CD38, CD45RA, CD90, CD49f) + hashtag oligo antibodies 1, 2, 3, 4, 5 and 6. Fluorescence activated cell sorting (FACS) was used to purify ~2,500 cells from each group that were subsequently pooled for CITE-seq library preparation and sequencing. Seurat was used to demultiplex data into 6 individual groups and differential gene expression analysis was performed to evaluate the impact of G-CSF within Cas9, RNP and RNP/GSE56 groups.

  Study phs003277

• Biomarkers in Transplant Recipients

  Biliary atresia causes jaundice and liver failure in newborn children. In this study, single nucleotide polymorphisms were characterized in children with biliary atresia and identified ARF6 as a susceptibility gene for this disease. Knockdown of this gene in zebrafish embryos impaired formation of bile ducts and excretion of bile from the zebrafish liver. Subject enrollment: Children with biliary atresia were enrolled at Children's Hospital of Pittsburgh (CHP) after obtaining written consent from parents for the University of Pittsburgh IRB approved protocol number 0405628. At the Center for Applied Genomics (CAG), all subjects were enrolled after obtaining written consent from parents per Children's Hospital of Philadelphia IRB approval number 06-004886 Genotyping array: Genotyping of DNA obtained from blood was performed with the Infinium HumanHap550K BeadChip (Illumina, San Diego, CA). Statistical methods and software: SNP genotype frequencies were compared between cases and controls with a chi-square test statistic applied in Plink [Purcell et al, 2007] for SNPs with at least 90% call rate and 1% minor allele frequency (MAF). Cases and controls: All DNA samples had excellent data quality with call rates > 98%. A total of 63 Caucasian BA cases and 1907 controls were compared at > 550000 SNPs, and demonstrated a very low genomic inflation factor of 1.00627. Description of Cases: Biliary atresia patients had received a Kasai procedure and demonstrated histopathological features of biliary obstruction, and were recruited at evaluation for transplantation or after liver transplantation.

  Study phs000960

• Machine learning to detect the SINEs of cancer

  We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing). The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.

  Study EGAS00001007169

• Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis

  Targeted therapy interventions using tyrosine kinase inhibitors (TKIs) provide encouraging treatment responses in ALK-rearranged lung adenocarcinomas, yet resistances occur almost inevitably. Apart from tumor cell-intrinsic resistance mechanisms, accumulating evidence supports a role of cancer-associated fibroblasts (CAFs) in affecting the therapeutic vulnerability of lung cancer cells. Here, we aimed to investigate underlying molecular networks shaping the therapeutic susceptibility of ALK-driven lung adenocarcinoma cells via tumor microenvironmental cues using three-dimensional (3D) spheroid co-culture settings. We show that CAFs promote therapy resistance of lung tumor cells against ALK inhibition by reducing apoptotic cell death and increasing cell proliferation. Using single-cell RNA-sequencing analysis, we show that genes involved in lipogenesis constitute the major transcriptional difference between TKI-treated homo- and heterotypic lung tumor spheroids. CAF-conditioned medium and CAF-secreted factors HGF and NRG1 were both able to promote resistance of 3D-cultured ALK-rearranged lung tumor cells via AKT signaling, which was accompanied by enhanced de novo lipogenesis and supression of lipid peroxidation. Notably, simultaneous targeting of ALK and SREBP-1 was able to overcome the established CAF-driven lipid metabolic-supportive niche of TKI-resistant lung tumor spheroids. Our findings highlight a crucial role of CAFs in mediating ALK-TKI resistance via lipid metabolic reprogramming and suggest new ways to overcome resistance towards molecular directed drugs by targeting vulnerabilities downstream of oncogenic signaling.

  Study EGAS50000000135

• Characterizing the Role of the Immune Microenvironment in Multiple Myeloma Progression at a Single Cell Level

  The plasma cell disease multiple myeloma (MM) is highly dependent on its bone marrow microenvironment (BMME). Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could hold the key to identifying novel therapeutic approaches, yet the intrinsic variability in cellular populations between patients together with differences in sample processing and analysis methods have made it difficult to identify consistent changes between datasets. Here, we used single-cell RNA sequencing of BM cells from precursor stages, monoclonal gammopathy of unknown significance (MGUS), smoldering MM (SMM) and newly diagnosed MM and analyzed our data in combination with a previously published dataset that used a similar patient population and sample processing. Despite vast interpatient heterogeneity, some alterations were seen consistently in both datasets. We identified changes in immune cell populations as disease progressed that were characterized by a substantial decrease in memory and naïve CD4 T cells and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of non-clonal memory B cells and an increase of CD14 and CD16 monocytes in MM compared to its precursor stages. These results provide crucial information of the immune changes associated with the progression to clinical MM and can help to develop immune based strategies for patient stratification and early therapeutic intervention. 

  Study phs002756

• VESPA: Vanderbilt Electronic Systems for Pharmacogenomic Assessment

  The Vanderbilt Electronic Systems for Pharmacogenomic Assessment (VESPA) Project is a large electronic medical record (EMR)- and biobank-based initiative for translational pharmacogenomics discoveries. Key research resources utilized in this effort include the BioVU DNA databank and associated Synthetic Derivative database of clinical information, and software tools developed to identify drugs and clinical events using EHR-derived structured and unstructured ("free text") data. Cohorts include subjects with primarily drug-response phenotypes, and most cases and controls identified include three data types: ICD-9 codes, medication regimens, and medical test results. Advanced informatics methods, such as natural language processing, were also used for some phenotypes. Algorithms used event-sequence analyses to establish temporal relationships between drugs and phenotypes. Both cases and control algorithms excluded records that contained specific comorbidities and were refined to achieve positive predictive value (PPV) > 90%. For automated algorithms failing to meet this threshold, manual review was coupled with algorithms to validate that the included cases were true positives. A total of 11,639 subjects met phenotyping criteria for a least one of the 28 phenotypes investigated. Across all phenotypes cases and controls, 90% were reused as either a case or control for a least one other phenotype. Data are deposited for individual phenotypes in the VESPA substudies. Substudies: VESPA: Genome Wide Association Study of Serum Creatinine during Vancomycin Therapy and Vancomycin Pharmacokinetics - phs000894 VESPA: Genome Wide Study of Clinically Diagnosed Ace Inhibitor Cough - phs000992

  Study phs000991

• Cell-Free, Methylated DNA in Blood Samples Reveals Tissue-Specific, Cellular Damage from Radiation Treatment

  Radiation therapy is an effective cancer treatment, although damage to surrounding healthy tissues can also occur. Cell-type specific DNA methylation patterns can be used to decode the cellular origins of cell-free DNA fragments, allowing for minimally-invasive monitoring of tissue damage. To evaluate whether changes in cell-free DNA methylation can indicate damages to tissues in patients treated with radiation, we collected serum samples from 15 breast cancer patients at three timepoints during their standard-of-care radiation therapy after surgery. A baseline sample was taken for each patient before onset of radiation therapy and a second End-Of-Treatment (EOT) sample was taken 30 minutes after the last treatment fraction. Finally, a recovery sample was taken one month after completion of radiation therapy. From serum samples, cell-free DNA was isolated and bisulfite capture-sequencing DNA methylation data were generated. Bisulfite capture-sequencing DNA methylation data were also generated from buffy coat and tissue-specific endothelial gDNA samples as well as from serum/plasma samples of healthy controls. The analysis of serum samples from breast cancer patients undergoing radiation treatment revealed distinct tissue-specific epithelial and endothelial responses to radiation across multiple organs. In conclusion, as a proof of concept we show that cell-type specific methylation signatures can be applied to detect cellular injury from radiation treatment using minimally invasive cell-free DNA in blood samples.

  Study phs003290

• NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Lung Health Study of Chronic Obstructive Pulmonary Disease)

  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. The Lung Health Study I was a randomized multicenter clinical trial with 5887 participants carried out from October 1986 to April 1994, designed to test the effectiveness of smoking cessation and bronchodilator administration in smokers aged 35 to 60 with mild lung function impairment. Participants were randomly assigned to one of three groups: usual care, who received no intervention smoking intervention with the inhaled bronchodilator ipratroprium bromide smoking intervention with an inhaled placebo The effect of intervention was evaluated by the rate of decline of forced expiratory volume in one second (FEV1).

  Study phs000291

• NIMH (National Institute of Mental Health) De Novo Mutation Identification in Taiwanese Schizophrenia Trios

  The substantial reproductive impact of schizophrenia, for which affected individuals have fewer than half as many offspring as unaffected individuals do, implies that mutations of largest effect will frequently be de novo mutations. Ascertaining exome sequence variation in father-mother-offspring trios allows such mutations to be identified and distinguished from the far-larger amount of rare variation that is inherited by each individual. The pursuit of this approach in a large, well-powered cohort of trios can also provide lessons that inform the development of such gene discovery strategies more generally in human genetics. Schizophrenia trios from the Taiwanese population are being collected by Dr. Ming Tsuang (PI, UC San Diego, California) and investigators in Taiwan (PI, Dr. Hai Gwo Hwu; both funded by NIMH grant 1R01MH085560; Expanding Rapid Ascertainment Networks of Schizophrenia Families in Taiwan). A total of 3800 trios are anticipated to be collected by May 2013. This represents a highly homogenous national sample from the same ancestral population. DNA samples will be obtained from the NIMH Repository, Rutgers University Cell and DNA Repository (described below) and stored at the Broad Institute. Genetic and data analyses will be performed at the Broad Institute. We propose to sequence the whole exome of trios by hybrid capture and Illumina next generation sequencing and perform targeted genotyping and validation of variants (SNPs, indels and CNVs) using several molecular methods, to include emulsion-based PCR and Sanger sequencing.

  Study phs001196

• National Cancer Institute (NCI) Genome-Wide Association Study (GWAS) of Renal Cell Carcinoma in African Americans

  The NCI GWAS of renal cell carcinoma (RCC) in African Americans was undertaken to provide insight into genetic loci affecting susceptibility to this malignancy in a racial group known to be at elevated risk. We genotyped 1,136,723 single-nucleotide polymorphisms (SNPs) among 255 cases and 375 controls of African ancestry from the NCI Kidney Cancer Study, and further investigated 16 SNPs in a replication set consisting of 140 cases and 543 controls from a case-control study conducted at the University of Texas MD Anderson Cancer Center. The variant rs10771279 located on 12p11, 77kb from a European-ancestry RCC risk marker, was associated with RCC risk in the GWAS (P=1.2 x 10-7) but did not replicate (P=0.99). The variant 7105934 on 11q13.3, previously associated with RCC in a GWAS using European-ancestry samples, was associated with risk in both studies [meta-analysis odds ratio (OR)=0.76, 95% confidence interval (CI)=0.64-0.91; P=0.0022]. The frequency of this allele was higher than that observed in the European-ancestry GWAS (0.56 and 0.07 respectively among controls). The rs7105934 association was stronger for clear cell RCC (ccRCC: OR=0.56; P=7.4 x 10-7) and absent for cases of other or unknown histology (OR=1.02; P=0.86). In conclusion, this study provides evidence that rs7105934 is a susceptibility locus for RCC, and clear-cell RCC in particular, among African Americans.

  Study phs000863

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Multimodal Developmental Neurogenetics of Females

  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 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 phs002043

• MAITS in HCC

  Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity. We investigated the cellular composition of liver cancer patient samples (n=8) using single-cell RNA-seq. For this purpose, mononuclear cells were isolated from human liver cancer samples. Three locations within the tumor-bearing liver were used: adjacent liver, rim and tumor core. Then, cells were FACS sorted (either CD45+ cells or MAIT cells) and subjected to 10X Chromium-based scRNA-seq. We investigated immune cell changes and the cellular heterogeneity of the HCC TiME with a focus on MAIT cells. Annotated H5 files are provided. Clinical metadata including TMN stage, sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples. Further details on the study can be obtained in our paper once it’s published.A complementary dataset derived from ultrahighplex CO-Detection by indEXing (CODEX) from paired patient samples can be accessed through The Cancer Imaging Archives (TCIA) under DOI: https://doi.org/10.7937/bh0r-y074.

  Study phs003279

• Systems genetics in human endothelial cells identifies non-coding variants modifying enhancers, expression, and complex disease traits

  Identification of causal variants and mechanisms underlying complex disease traits in humans requires strategies to locate and fine-map functional regulatory variants in disease-relevant cell types. To discover functional regulatory variants in primary aortic endothelial cells (ECs) from humans, we collected genetic, transcriptomic, and four epigenomic phenotypes in a population of 153 human donors representing individuals of both sexes and three major ancestries. We found thousands of EC eQTLs that were not present in GTEx at all ranges of effect sizes, indicating novel functional variants not observable from tissue data. We performed several epigenetic assays across 53 donors' EC to enable molecular QTL mapping, which included chromatin accessibility, histone modification, and transcription factor binding of proteins ERG and NF-kB/p65 in two activation states. We discovered over 3000 regulatory elements where cis-regulatory variants associated to significant differences in epigenetic marks. Co-localization with our eQTLs, TF motif mutations, and 3D confirmation capture data in ECs enabled our discovery of hundreds of high-confidence functional regulatory elements that perturb endothelial molecular phenotypes. Furthermore, this set of variants is enriched at GWAS loci for Coronary Artery disease and other disease traits, with some SNPs demonstrating pleiotropic effects. Together, our dataset and analytical approach provide the genetics and vascular biology communities with specific variants affecting EC biology and serves as a proof-of-principle for how to detect functional regulatory variants in human cells with relevance to complex traits.

  Study phs002057

• International Consortium on the Genetics of Systemic Lupus Erythematosus (SLEGEN)

  The genetic makeup of an individual strongly influences the risk of developing systemic lupus erythematosus (SLE). The identification of genes that predispose an individual to SLE will lead to earlier and better diagnosis, better treatments, and possibly prevention. To this end, the International Consortium on the Genetics of Systemic Lupus Erythematosus (SLEGEN) was formed in 2005 and is composed of lupus researchers who agreed to pool their knowledge and resources to search for genes that predispose to lupus. Eight laboratories contributed DNA samples for genotyping at the Broad Institute and association with SLE was performed by the Data Coordinating Center (Wake Forest University), as part of a four stage study design. Stages one and two of this design were graciously funded by the Alliance for Lupus Research (www.lupusresearch.org). In this stage of the study, approximately 767 SLE patients (cases) were compared to approximately 383 non-SLE patients (controls) for differences among the Illumina HumanHap300. The affected individuals are all females of European decent. 82% of the cases are the index case from multiplex pedigrees for SLE and the remaining 18% have self-reported first degree relatives with SLE. A detailed summary of the methods and results can be found in the manuscript in Nature Genetics February 2008 by SLEGEN "Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants ITGAM, PXK, KIAA1542 and other loci". (Please see also Study Accession: phs000202.v1.p1)

  Study phs000216

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Genomics of Autism Spectrum Disorder (GASD)

  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 phs002509

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Homozygosity Mapping Consortium for Autism (HMCA)

  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 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 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 phs001894

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Simons Searchlight

  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 phs002512

• Indexing Genes Impacted by Copy Number Variation in Developmental Disorders

  A challenge in clinical genomics is to predict whether copy number variation (CNV) affecting a gene or multiple genes will manifest as disease. Increasing recognition of gene dosage effects in neurodevelopmental disorders prompted us to develop a computational approach based on critical-exon (highly expressed in brain, highly conserved) examination for potential etiologic effects. Using a large CNV dataset, our updated analyses revealed significant (P < 1.64 x 10-15) enrichment of critical-exons within rare CNVs in cases compared to controls. Separately, we used a weighted gene co-expression network analysis (WGCNA) to construct an unbiased protein module from prenatal and adult tissues and found it significantly enriched for critical exons in prenatal (P < 1.15 x 10-50, OR = 2.11) and adult (P < 6.03 x 10-18, OR = 1.55) development. WGCNA yielded 1,206 proteins for which we prioritized the corresponding genes as likely to have a role in neurodevelopmental disorders. We compared the gene lists obtained from critical-exon and WGCNA analysis and found 438 candidate genes associated with CNVs annotated as pathogenic, or variants of uncertain significance (VOUS), from among 10,619 developmental delay cases. We identified genes containing CNVs previously considered to be VOUS, to be new candidate genes for neurodevelopmental disorders (GIT1, MVB12B and PPP1R9A) demonstrating the utility of this strategy to index the clinical effects of CNVs. Reprinted from Uddin et al, 2016, with permission from Scientific Reports.

  Study phs001154

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Center of Excellence (ACE II)

  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 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 phs002042

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Study of Autism Genetics Exploration (SAGE)

  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 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 phs001740

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  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 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 phs002004

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Genetic Resource Exchange

  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 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 phs001766

• Detection and Targeting of Splicing Deregulation in Pediatric Acute Myeloid Leukemia Stem Cells

  This study evaluates the gene expression and splicing changes between pediatric patients with and without Acute Myeloid Leukemia (AML) and pediatric and adult patients with AML. We obtained bone marrow or peripheral blood from patients and isolated CD34+ stem and/or progenitor cells from pediatric patients with AML (10) or without AML (6) and adult patients with AML (5). In parallel, a targeted analysis for known, deleterious, genetic mutations in the KRAS, NRAS, and KMT2C genes identified 2, 5, and 6 AML patients with alterations, respectively. Additionally, Tapestri analysis further identified a mutation in the intronic region of the SF3B1 gene in multiple pediatric AML patients. RNA-Seq was performed on the purified cell populations at The Scripps Research Institute Next Generation Core on an Illumina NextSeq 500 sequencer with 150bp paired-end reads.These datasets revealed that, similar to adult AML, pediatric AML samples were enriched for Leukemia Stem Cells (LSC) markers. A deeper analysis showed widespread dysregulation of splicing in pediatric AML samples compared to age-matched non-leukemic samples. In addition, the splicing regulator RBFOX2 was downregulated with an accompanying increase in specific CD47 splice isoforms in the pediatric AML samples. Thus, the increased presence of LSCs in pediatric AML is possibly driven by global splicing deregulation, and the treatment with splicing modulators like Rebecsinib has a potential therapeutic value for eradicating LSCs in these patients.

  Study phs003196

• Tumor Mutation Burden, Expressed Neoantigen and Immune Microenvironment in Diffuse Gliomas

  A consistent correlation between tumor mutation burden (TMB) and tumor immune microenvironment has not been observed in gliomas as in other cancers. Driver germline and somatic mutations, TMB, neoantigen, and immune cell signatures were analyzed using whole exome sequencing (WES) and transcriptome sequencing of tumor and WES of matched germline DNA in a cohort of 66 glioma samples (44 IDH-mutant and 22 IDH-wildtype). Fourteen samples revealed a hypermutator phenotype (HMP). Eight pathogenic (P) or likely pathogenic (LP) germline variants were detected in 9 (19%) patients. Six of these 8 genes were DNA damage repair genes. P/LP germline variants were found in 22% of IDH-mutant gliomas and 12.5%of IDH-wildtype gliomas (p= 0.7). TMB was correlated with expressed neoantigen but showed an inverse correlation with immune score (R =−0.46, p= 0.03) in IDH-wildtype tumors and no correlation in IDH-mutant tumors. Antigen Processing and Presentation (APP) score correlates with immune score. The APP is higher in non-hypermutator phenotype (NHMP) samples versus HMP samples in the IDH-wildtype gliomas, but higher in HMP samples versus NHMP samples in the IDH-mutant gliomas. Conclusion: TMB was inversely correlated with the immune score in the IDH-wildtype gliomas, and showed no correlation in the IDH-mutant tumors. The APP was correlated with the immune score, and may be further investigated as a biomarker for response to immunotherapy in gliomas. Studies of germline variants in a large glioma cohort are warranted.

  Study phs002653