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• CosMX spatial transcriptomics

  This study consists of the spatial transcriptomics analyis data generated by : CosMxTM Human Universal Cell Characterization RNA Panel (1000-plex); NanoString, USA for n=4 SLD-HCC and n=4 non-SLD-HCC. The goal of this data is to compare SLD-HCC vs non-SLD-HCC as well as response to immunotherapy

  Dataset EGAD50000001507

• Targeted Sequencing of Human Myeloid Malignancies

  This study involves targeted sequencing of samples from myeloid malignancies at different timepoints to assess clonal evolution of malignancy a. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002225

• Clinical Proteomic Tumor Analysis Consortium (CPTAC) Proteogenomic Confirmatory Study of Breast, Colon, Lung, and Ovarian Tumors

  Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.). The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein-protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the second phase of the CPTC initiative in September 2011. Renamed the Clinical Proteomic Tumor Analysis Consortium, CPTAC is beginning to leverage its analytical outputs from Phase I to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach is to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. The data contained in this dataset are derived from samples designed to confirm CPTAC findings from the TCGA samples. These confirmatory samples contain breast, ovarian, colon, and lung tumors collected via a protocol optimized for proteomics. Specifically, ischemic time of the sample was controlled and restricted to less than 30 minutes. ACGT, Inc. produced whole exome, mRNAseq, and miRNAseq for these samples. Corresponding proteomic data are available at: https://cptac-data-portal.georgetown.edu/cptacPublic/ The study design was to profile colon, breast, ovarian, and lung tumors both genomically and proteomically. Germline DNA was obtained from blood. Normal control samples for proteomics varied by organ site: adjacent colon tissue for colon cases, contralateral breast tissue for some breast cases, and Fallopian tube fimbria for some ovarian cases. Lung cases had no normal control for proteomic analysis. All cancer samples were derived from primary and untreated tumors.

  Study phs000892

• Genetic Factors associated with Conversion from Active Surveillance to Treatment for Prostate Cancer

  This is a genome-wide association study (GWAS) of conversion from active surveillance (AS) for prostate cancer (PC) to treatment. AS is the monitoring with screening tests and/or biopsies over time of men usually with low-risk, early-stage PC to avoid harmful side effects of PC treatment. "Conversion" refers to patients who withdraw from AS (switch to active treatment) due to changes in their cancer development, anxiety about delayed treatment, or other reasons. Patients who converted because of anxiety alone were censored in the GWAS analysis. The overall goal of this study is to develop an easily measurable, germline-based panel of genetic variants to aid in selecting PC patients appropriate for AS and/or the intensity of monitoring. In total, 6,324 PC patients from 28 collaborating institutions are included in this study. Patients are those with PC who are either on AS or who have previously converted from AS to treatment. In this study, 70% of all subjects were diagnosed with very low-risk, low-risk, or low-volume intermediate-risk PC (Kryvenko and Epstein, 2016; PMID: 26709152) and have been enrolled in an AS program. DNA was extracted from blood, saliva, or formalin-fixed, paraffin-embedded (FFPE) tissue collected from the study subjects at the participating sites. Genotyping, quality control processes, and imputation were performed at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University. CIDR used the Illumina Infinium Multi-Ethnic Global Array (MEGA) version 1, which included genome-wide tag SNPs and 10,000 SNPs selected with custom content. Quality control and assurance (QC/QA) checks on genotyped data involved four domains: sample-level checks, batch effects, SNP-level checks, and preliminary association tests. Samples and SNPs passing the QC/QA checks were imputed to appropriate reference panels (HRC r1.1 for patients of European ancestry) using the Michigan Imputation Server (PMID: 27571263). Unmeasured genetic variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) Imputation Server, with 97,256 reference samples and 308,107,085 SNPs. Variants with imputation quality (INFO) score < 0.3 were excluded, leaving a total of 22,691,641 SNPs successfully imputed. After QC steps, a total of 5,936 samples genotyped at CIDR remained for inclusion in the GWAS.

  Study phs002056

• TRACERx 100: RRBS data from a subset of the first 100 TRACERx tumours

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). This study looks at multi-region RRBS data from the TRACERx100 cohort selected for quantity of material available and high tumour purity. RRBS data was collected for 122 tumour regions across 36 patients each with matched normal. A subset of this cohort was used in a sister study looking at tumour neo-antigen promoter methylation status in NSCLC (71 tumour regions across 28 patients for a total of 99 samples).

  Study EGAS00001003484

• EDi018-A / SAMEA4771918 WGS data

  The iPSC line EDi018-A / SAMEA4771918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001027

• EDi019-A / SAMEA4774918 WGS data

  The iPSC line EDi019-A / SAMEA4774918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001029

• RCi009-A / SAMEA4339688 WGS data

  The iPSC line RCi009-A / SAMEA4339688 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi009-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001030

• EDi016-A / SAMEA4562366 WGS data

  The iPSC line EDi016-A / SAMEA4562366 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001032

• EDi017-A / SAMEA4768918 WGS data

  The iPSC line EDi017-A / SAMEA4768918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001033

• EDi019-C / SAMEA4777168 WGS data

  The iPSC line EDi019-C / SAMEA4777168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001036

• EDi010-A / SAMEA4459354 WGS data

  The iPSC line EDi010-A / SAMEA4459354 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi010-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001038

• BIONi010-C / SAMEA3158050 WGS data

  The iPSC line BIONi010-C / SAMEA3158050 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001039

• UKKi019-C / SAMEA17626918 WGS data

  The iPSC line UKKi019-C / SAMEA17626918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001042

• EDi011-B / SAMEA4459359 WGS data

  The iPSC line EDi011-B / SAMEA4459359 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001044

• EDi011-C / SAMEA4459360 WGS data

  The iPSC line EDi011-C / SAMEA4459360 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001045

• EDi012-A / SAMEA4459361 WGS data

  The iPSC line EDi012-A / SAMEA4459361 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001046

• EDi015-A / SAMEA4459373 WGS data

  The iPSC line EDi015-A / SAMEA4459373 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001047

• EDi015-C / SAMEA4459376 WGS data

  The iPSC line EDi015-C / SAMEA4459376 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001048

• EDi017-B / SAMEA4770418 WGS data

  The iPSC line EDi017-B / SAMEA4770418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001049

• EDi011-A / SAMEA4459357 WGS data

  The iPSC line EDi011-A / SAMEA4459357 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001050

• EDi013-B / SAMEA4459367 WGS data

  The iPSC line EDi013-B / SAMEA4459367 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001052

• BIONi010-A / SAMEA3105765 WGS data

  The iPSC line BIONi010-A / SAMEA3105765 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001054

• UKKi019-B / SAMEA17626168 WGS data

  The iPSC line UKKi019-B / SAMEA17626168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001096

• UKKi019-A / SAMEA17624668 WGS data

  The iPSC line UKKi019-A / SAMEA17624668 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001094

• EDi019-B / SAMEA4776418 WGS data

  The iPSC line EDi019-B / SAMEA4776418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001091

• EDi016-C / SAMEA4768168 WGS data

  The iPSC line EDi016-C / SAMEA4768168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001090

• UOXFi008-B / SAMEA103887561 WGS data

  The iPSC line UOXFi008-B / SAMEA103887561 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UOXFi008-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001089

• EDi017-C / SAMEA4771168 WGS data

  The iPSC line EDi017-C / SAMEA4771168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001087

• EDi016-B / SAMEA4767418 WGS data

  The iPSC line EDi016-B / SAMEA4767418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001086

• WTSIi009-A / SAMEA2593858 WGS data

  The iPSC line WTSIi009-A / SAMEA2593858 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line WTSIi009-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001085

• UKKi022-C / SAMEA103988349 WGS data

  The iPSC line UKKi022-C / SAMEA103988349 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi022-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001084

• UKKi021-B / SAMEA103988346 WGS data

  The iPSC line UKKi021-B / SAMEA103988346 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi021-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001083

• UKKi020-C / SAMEA103988344 WGS data

  The iPSC line UKKi020-C / SAMEA103988344 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi020-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001082

• EDi018-C / SAMEA4774168 WGS data

  The iPSC line EDi018-C / SAMEA4774168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001081

• EDi018-B / SAMEA4773418 WGS data

  The iPSC line EDi018-B / SAMEA4773418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001080

• UOXFi007-A / SAMEA103988274 WGS data

  The iPSC line UOXFi007-A / SAMEA103988274 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UOXFi007-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001079

• RCi007-C / SAMEA4084916 WGS data

  The iPSC line RCi007-C / SAMEA4084916 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi007-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001072

• RCi005-A / SAMEA3961534 WGS data

  The iPSC line RCi005-A / SAMEA3961534 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi005-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001071

• RCi004-B / SAMEA3106205 WGS data

  The iPSC line RCi004-B / SAMEA3106205 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi004-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001070

• RCi004-A / SAMEA3106011 WGS data

  The iPSC line RCi004-A / SAMEA3106011 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi004-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001069

• UKKi018-C / SAMEA103988380 WGS data

  The iPSC line UKKi018-C / SAMEA103988380 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi018-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001067

• EDi015-B / SAMEA4459375 WGS data

  The iPSC line EDi015-B / SAMEA4459375 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001066

• EDi013-C / SAMEA4459368 WGS data

  The iPSC line EDi013-C / SAMEA4459368 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001065

• EDi012-C / SAMEA4459364 WGS data

  The iPSC line EDi012-C / SAMEA4459364 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001064

• EDi012-B / SAMEA4459363 WGS data

  The iPSC line EDi012-B / SAMEA4459363 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001063

• UKKi017-C / SAMEA17621668 WGS data

  The iPSC line UKKi017-C / SAMEA17621668 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi017-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001061

• EDi013-A / SAMEA4459365 WGS data

  The iPSC line EDi013-A / SAMEA4459365 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001057

• EDi010-B / SAMEA4459356 WGS data

  The iPSC line EDi010-B / SAMEA4459356 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi010-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001056

• RBi001-A / SAMEA3368212 WGS data

  The iPSC line RBi001-A / SAMEA3368212 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RBi001-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001058

• RCi006-A / SAMEA3962402 WGS data

  The iPSC line RCi006-A / SAMEA3962402 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi006-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001059

• BIONi010-B / SAMEA3158000 WGS data

  The iPSC line BIONi010-B / SAMEA3158000 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001055

• Blastic plasmacytoid dendritic cell neoplasm (BPDCN)_cases

  Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy that is most similar in expression profiles to plasmacytoid dendritic cells. However, patients often exhibit features of AML and can progress to AML. In this project, we will determine the differentially and commonly expressed genes between BPDCN and AML specimens. Available BPDCN and TET2-mutated AML specimens were taken for transcriptome microarray analysis.

  Dataset EGAD00010001727

• Smartseq2 data of single immune cells in hepatocellular carcinoma

  The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. This dataset is part of Smartseq2 data

  Dataset EGAD00001005960

• Transcriptome sequencing of Gingivo-buccal Cancer : ICGC-India Project_YR03

  Transcriptome Sequencing of Gingivo-buccal Cancer: ICGC-India ProjectAs a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Transcriptome sequencing of paired RNA samples – isolated from the tumor and adjacent normal tissues of 12 patients – have been performed. Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from adjacent normal and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Sequencing is being performed at the National Institute of Biomedical Genomics, Kalyani.

  Study EGAS00001002851

• Transcriptome sequencing of Gingivo-buccal Cancer : ICGC-India Project_Batch05

  Transcriptome Sequencing of Gingivo-buccal Cancer: ICGC-India ProjectAs a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Transcriptome sequencing of paired RNA samples – isolated from the tumor and adjacent normal tissues of 28 patients – have been performed. Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from adjacent normal and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Sequencing is being performed at the National Institute of Biomedical Genomics, Kalyani.

  Study EGAS00001003285

• InTEAM Consortium - Alcoholic Hepatitis

  Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we describe the transcriptional programs involved in disease progression. We uncovered that development of AH is characterized by the defective activity of liver-enriched transcription factors (LETFs). The PPARG predicted activation state was found increased in early forms of ALD, while AH was associated by a marked decrease in HNF4A-dependent gene expression along with a marked expression of the fetal HNF4A isoform (P2). TGFB1, a key upstream transcriptome regulator in AH, induced the use of HNF4a P2 promoter in hepatocytes, which resulted in abnormal bile acid synthesis and defective metabolic and synthetic functions. PPARG agonists partially prevented this effect. We conclude that targeting TGFB1 and epigenetic drivers that modulate HNF4A-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH. The study was conducted thanks to a multicenter collaboration under the National Institute of Alcohol Abuse and Alcoholism (NIAAA)-funded consortium: Integrated Approaches for Identifying Molecular Targets in Alcoholic Hepatitis (InTEAM).

  Study phs001807

• National Cancer Institute Clinical and Laboratory Analysis of Familial Cancer

  The purpose of this study is to continue the analysis begun on 09C0079 which was focused on identification of the genetic mutation associated with a new gastric polyposis syndrome, Gastric Adenocarcinoma and Proximal Polyposis of the Stomach (GAPPS). GAPPS, first described in 2012, is an autosomal dominant gastric polyposis syndrome that confers a substantial risk for gastric adenocarcinoma. The GAPPS phenotype consists of a carpeting of greater than 100 fundic gland polyps (FGPs) in the oxyntic mucosa of the gastric body and fundus, with antral sparing with some FGPs displaying high-grade dysplasia. This is in contrast to sporadic FGPs that are benign, most often are not associated with high-grade dysplasia though low-grade dysplasia has been reported, and are fewer in number. Gastric FGPs have been found to be associated with Familial Adenomatous Polyposis (FAP) and attenuated FAP, which is an autosomal dominant condition associated with germ line mutations in APC which confers a phenotype consisting of multiple (>100) adenomatous polyps in the colon and rectum developing after the first decade of life. FGPs are reported to occur in 12-84% of patients with FAP, whereas sporadic FGPs are identified in ~5% of patients undergoing upper gastrointestinal endoscopy. Gastric adenocarcinoma arising from FGPs in individuals with FAP have been reported. Other manifestations of FAP and AFAP include polyps in the upper gastrointestinal tract, congenital hypertrophy of retinal pigment epithelium, osteomas and epidermoid cysts, supernumerary teeth, desmoid formation, and other malignancies such as thyroid, small bowel cancer, hepatoblastoma, and medulloblastoma. Thus far there are no reports in GAPPS of polyposis arising in the esophagus, gastric antrum, pylorus, small intestine, or colon.

  Study phs001935

• Bone Microarchitecture

  Osteoporosis affects more than 28 million people in the United States and the lifetime risk for osteoporosis-related morbidity is higher than a woman's combined risk for breast cancer, endometrial cancer and ovarian cancer. Health care expenditures for osteoporotic patients in this country are currently nearly 13 billion dollars per annum and are predicted to increase markedly in the next decades due the aging of the population; therefore, it is important to understand the factors that contribute to bone strength and fracture risk. With the advent of skeletal imaging modalities such as high resolution peripheral quantitative computed tomography (HR-pQCT), it is now possible to study the genetics of more highly refined peripheral skeletal microarchitecture phenotypes. Because these refined phenotypes have not been measured in many cohort studies, this project was a collaborative effort to include almost all the existing data on HR-pQCT of the radius and tibia, and genetics from around the world. Cohorts involved in the discovery included: 1) Framingham Osteoporosis Study, 2) Mayo Clinic cohort; 3) Geneva Retirees cohort; 4) OFELY cohort from Lyon France; 5) STRAMBO cohort from Lyon France; 6) Swedish Male cohorts. Replication cohorts undergoing de novo genotyping include: 1) QUALYOR cohort from Lyon France; 2) CaMOS cohort from Canada. Other than the Framingham cohort and the Swedish male cohorts, all discovery cohorts underwent genome wide genotyping with the Affymetrix Axiom Biobank array that had common variants along with exome content based on the early findings from the Exome Sequencing Project. All genotype data from the discovery cohorts were then imputed using the Haplotype Reference Consortium reference panel. Variants for replication genotyping using the Kasp technology were selected based on novelty compared with previously identified loci using DXA phenotypes, minor allele frequency, underlying LD structure within a locus, and likelihood of a variant being functional as assessed using various algorithms for coding and non-coding variants.    

  Study phs002102

• HCA Female Reproductive Adult WSSS RNA

  HCA Endometrium_LM The endometrium regenerates monthly and its transformation is executed through dynamic changes in states and interactions of multiple cell types. Using transcriptomics methods we seek to profile changes of the endometrium across the menstrual cycle. Our map will have implications in women's health and cancer, by enabling the interpretation of GWAS analyses or the studying functional consequences of somatic mutations. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001007909

• Identification of drug resistance genes in melanoma by mRNA gene expression

  We aim to sequence the mRNA transcriptome of 22 human melanoma cell lines in biological triplicate in order to define the gene expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003244

• Identification of drug resistance genes in melanoma by small RNAs expression analysis

  We aim to sequence the small RNAs of 22 human melanoma cell lines in biological triplicate in order to define the microRNAs expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003245

• Whole-Genome Sequencing Analysis of Extrachromosomal DNA with Amplicon Architect

  The study is related to our manuscript entitled "Single-cell genetic heterogeneity linked to immune infiltration in glioblastoma". Using multiplexed FISH analysis of 3-4 tumor cores from 17 glioblastoma formalin-fixed paraffin-embedded (FFPE) tumors, we identified two classes of tumors based on single cell co-occurrence of EGFR and CDK4 amplification. Tumors with a low odds ratio for finding EGFR and CDK4 amplified in the same cell (ORlow tumors) had higher EGFR copy number and higher diversity of copy number in cells harboring EGFR amplification. This suggested that in ORlow tumors EGFR is likely present on extrachromosomal DNA. To test this hypothesis, we performed whole genome sequencing on DNA extracted from FFPE slides from 3 ORlow samples and 3 ORhigh samples and used AmpliconArchitect to investigate the structure of the EGFR amplicon. We found multiple breakpoints and high complexity structure in the 3 tested ORlow samples, but not in the ORhigh samples, supporting the possibility of an extrachromosomal nature of EGFR amplification in the first group.

  Study phs003100

• EGA account management

  How to manage your EGA account Welcome to the documentation page for creating an EGA user. The European Genome-Phenome Archive (EGA) is a platform for archiving, managing, and sharing human genomic and phenotypic data. To submit, access, and manage requests for the data hosted on the EGA platform, users are required to register for an account. This documentation page provides step-by-step instructions for creating an EGA user account, as well as information on the different types of EGA user accounts and their associated privileges. You can check the EGA data flow for a better understanding. Manage EGA account step-by-step guide Contact Information Public Keys Password Authenticator Federated Identity Sessions Request an EGA account step-by-step guide Fill in this form.You cannot use a generic email account (e.g., Gmail). Make sure you use your institutional email account.Your email is by default your username.The criteria for a strong password is: It should contain between 8 and 32 charactersIt should contain at least one uppercase letterIt should contain at least one numberIt should contain at least one special characterYou will need to wait for the Helpdesk team to check your identity. Please allow two business days for this step.After the Helpdesk validation step, you will receive an email with a link to validate your institutional email.Click the link to verify your email. It will redirect you to the EGA website.As soon as your account has been validated, you will be able to start adding more information to your EGA profile. For that, you first need to log in using your credentials from step 1. Manage EGA account step-by-step guide As a logged-in user, go to your profile by clicking on Welcome > My Profile. To modify it, click the Edit button.You will see the following options: Contact Information In this section, you can modify the personal information of your EGA account. Public Keys Adding a public key is necessary for downloading data via Live Outbox distribution. If you don’t know how to generate your key, you can follow the instructions available on How do I create a key? Password In the Password tab, you can change your password anytime to ensure the security of your account. However, if you receive an email notifying you that your password was modified and you did not initiate the change, it could be a sign that your account has been compromised. In this case, you should immediately contact our Helpdesk team to report the issue and receive assistance in restoring your account security. Authenticator You can set your two-factor authentication (2FA) system for your EGA account as an identity and access management security method that requires two forms of identification to access resources and data. To obtain your code, please follow the instructions below, also available in the Authenticator tab in your profile. Sessions The Sessions tab displays a list of all devices where you are currently logged in as an EGA user. This includes any open sessions you have on your computer, phone, or other devices. By reviewing the list of active sessions, you can monitor the devices that have access to your account and ensure that there are no unauthorised logins. If you notice any suspicious activity, you can close the sessions from this page to immediately terminate access to your account from the unauthorised device.

  Documentation how-to-manage-your-account

• RNA-sequencing of tumors from 45 patients with recurrent or metastatic gastric cancer treated with immune checkpoint inhibitors

  Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this work, we used our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identified four molecular subtypes that were prognostic for survival. We then built a support vector machine with linear kernel and the binary classifier to generate a risk score that is prognostic for 5-year overall survival and validated the risk score using three independent datasets. We also found that the molecular subtypes predicted response to adjuvant 5-fluorouracil and platinum after gastrectomy and immune checkpoint inhibitors in patients with metastatic or recurrent disease. The 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner. This EGA archive includes both St.Mary hospital cohort samples (n=28) and Yonsei immunotherapy patient samples (n=17).

  Study EGAS00001005588

• The British Autozygosity Populations BioResource (2019-08-14)

  This data set contains whole exome sequences of individuals with self-stated parental relatedness from the East London Genes & Health cohort. Rare frequency functional variants in these healthy individuals will be studied with respect to the genetic health of the participants and loss-of-function analysis of human genes. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-14.

  Dataset EGAD00001005253

• Gene Characterization in Carbohydrate metabolic alterations (neonatel diabetes & congenital hyperinsulinemic) in early childhood (2018-03-14)

  Whole Exome Sequencing of trios (proband + parents) or probands only with Neonatal Diabetes Mellitus (NDM) or Congenital Hyperinsulinism of Infancy (CHI) of unknown genetic origin. 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 2018-03-14.

  Dataset EGAD00001004040

• TTV018 RORC IBD-associated genotype effects on RORgT expression and function in ex vivo T cells (2019-02-15)

  RNA sequencing of peripheral immune cells from patients +/- an IBD risk variant. Peripheral immune cells +/- in vitro test compound treatment. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-02-15.

  Dataset EGAD00001004777

• Genome-Wide Association Study of Amyotrophic Lateral Sclerosis in Finland

  The genetic etiology of amyotrophic lateral sclerosis (ALS) is not well understood. Finland has one of the highest incidence of ALS in the world, making it an ideal population for study. To identify genetic risk factors for this fatal neurodegenerative disease, we undertook a genome-wide association study of 405 Finnish patients diagnosed with ALS and 497 Finnish controls. Two loci that exceeded the Bonferroni threshold for genome-wide significance were identified. One was located on chromosome 21q22, corresponding to the known autosomal recessive D90A allele of the SOD1 gene. The other was detected on the short arm of chromosome 9, which had been previously identified in linkage studies of families with ALS. Together, these two loci account for most of the increased incidence of ALS observed in this population.

  Study phs000344

• Alveolar Rhabdomyosarcoma case report

  Rhabdomyosarcoma (RMS) is a mesenchymal malignancy phenocopying muscle and is among the leading causes of death from childhood cancer. Metastatic alveolar rhabdomyosarcoma is the most aggressive subtype with an 8% five-year disease-free survival rate when a chromosomal fusion is present, and 40% five-year disease-free survival rate when negative for a fusion event. The underlying biology of PAX-fusion negative alveolar rhabdomyosarcoma remains largely unexplored and is exceedingly rare in Li-Fraumeni syndrome patients. Here, we present the case of an 11-year old male with fusion-negative alveolar rhabdomyosarcoma studied at end of life with a comprehensive functional genomics characterization, resulting in identification of potential therapeutic targets for broader investigation.

  Study EGAS00001004828

• DAC for CTOS colorectal cancer organoids

  This Data Access Committee (DAC) is responsible for reviewing and approving access requests to controlled-access human sequencing data generated from colorectal cancer patient-derived organoids established using the CTOS method. The dataset includes whole-exome sequencing (WES) and RNA sequencing (RNA-seq) data. Access requests are evaluated for consistency with the original informed consent and the approved research purposes.

  Dac EGAC50000000915

• Mapping genetic variants that underlie gene regulation in intestinal cell types to identify novel, validated, Crohn’s disease drug targets

  Biopsies from the terminal ileum and rectum of healthy individuals and terminal ileum and blood from Crohns disease patients are collected and processed single cells and processed for single-cell RNA-sequencing using 10X Genomics 3' v3 and v3.1 and Illumina sequencing. 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/

  Study EGAS00001003647

• Whole Genome Sequences of 18th century African descended individuals from Charleston, South Carolina.

  This study presents Whole Genome Sequencing results from the Anson Street African Burial Ground Project, which is a community-based initiative aimed at understanding the histories of 37 Ancestors found during construction in Charleston, South Carolina. Here we report fastq files for all 37 Ancestors. DNA was extracted at the University of Tennessee-Knoxville following Dabney et al. 2013, and dual index libraries prepared using a modified NEBNext Ultra II kit with partial USER enzyme digestion. Libraries were then enriched for human genomic DNA (MyBaits) and sequenced on Illumina Platforms.

  Study EGAS00001006693

• CPC-GENE Prostate Cancer Heterogeneity Study

  Herein we describe a molecular portrait of potentially curable, Gleason 7 and intermediate risk prostate cancer based on genome-wide CNV profiles of 96 patients, and subsequent whole-genome sequencing of 28 tumours from 10 patients, using DNA quantities that are achievable in diagnostic biopsies (50 ng). We show that Gleason 7 cancer is highly heterogeneous at the SNV, CNV, and intra-chromosomal translocation levels, and is characterized by a very low number of recurrent SNVs but significant structural variation. We identified a novel recurrent MYCL1 amplification, which was strongly associated with TP53 deletion and prognostic for biochemical recurrence in this cohort. Moreover, we identified clear evidence of divergent tumour evolution in multi focal cancer and, in 2/5 cases evaluated, multiple tumours of independent clonal origin. Taken together, these data represent the first systematic evaluation of the differential genomics of potentially curable prostate cancer, and strongly suggest that a more robust understanding of the relationship between genetic heterogeneity and clinical outcomes is required to effectively develop biomarkers of prognosis based on tumour genomics.

  Study EGAS00001000549

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Population Architecture Using Genomics and Epidemiology (PAGE): Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project

  As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of >150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU's overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold: Replicate genome-wide association study (GWAS)- identified variants in European Americans; Identify population-specific and trans-population genotype-phenotype associations; Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt's biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin's lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

  Study phs000559

• The Institute for Genomic Medicine at Nationwide Children's Hospital Pediatric Cancer and Blood Disorder Project

  The aim of this study is to enable the unification of the clinical and research arms of comprehensive genomic profiling in the setting of cancer and hematologic disease. Increasingly, studies of the genomic etiology of cancer and hematologic diseases are being utilized for patient management, including prognostication, informing diagnosis, and evaluating eligibility for targeted therapeutics and clinical trials. Clinically available testing can be limited in scope and follow-up, and thus assessment of the impact on patient outcomes is difficult to ascertain. N-of-1 studies have the ability to more broadly impact our understanding of the mechanisms of cancer and hematologic disease, and can have benefit for the individual as well as the larger population of patients with these diseases. Thus, we seek to study and characterize the genomic underpinnings of cancer and hematologic disease through genomic profiling studies bridging both clinical and research components. We will rigorously perform and vet research sequencing and genomic studies protocols for the purpose of transitioning these assays to clinically valid testing for use in clinical patient care. We have been enrolling patients onto this comprehensive genomic profiling study since January 2018. As part of the genomic workup for these patients, we have been performing tumor and normal whole genome sequencing, whole exome sequencing and RNA-Seq. The project is largely supported by the Nationwide Children's Foundation, and we anticipate adding another 50-70 cases per year. This protocol is significant in that it expands our understanding of the etiology of cancer and hematologic disease through a comprehensive analysis aimed at detecting a broad scope of genomic aberrations. Additionally, it improves our understanding of the clinical utility of genomic profiling and has the potential to ultimately improve patient management and surveillance at both level of an individual and general populations of patients affected by these diseases. Primary Objectives: Aim 1: Use diagnostic, relapse, and/or germline samples for augmented whole exome sequencing to characterize the spectrum of germline and somatic genomic alterations in pediatric malignancies and hematologic disease. Aim 2: Examine gene expression and fusion events using whole RNA-seq "transcriptome" analysis or targeted RNA-seq analysis such as the Archer FusionPlex technology. Aim 3: Compare the results of our enhanced molecular profiling vs. that which is detectable by traditional methods as related to cytogenetic, molecular, and minimal residual disease analyses performed as part of standard of care. Aim 4: Examine the utility of comprehensive genomic profiling by collection and evaluation of outcomes data as related to enhancement/alteration in diagnosis, prognosis, treatment management, and clinical benefit. Secondary Objective: Aim 1: Use and clinically validate expanded laboratory methodologies and bioinformatic approaches to more broadly assess genomic complexity (e.g. whole genome sequencing, transcriptome sequencing, in vitro analyses, minimal residual disease testing, clonality assays, proteomics) in the setting of hematologic and oncologic disease. In so doing, we aspire to make novel discoveries and associations, as related to disease phenotypes and subtypes, variant causality, therapeutic targeting, and cancer biology.

  Study phs001820

• Altered oligodendrocyte heterogeneity in Multiple sclerosis revealed by single nuclei RNA sequencing

  This study is currently hosted by the European Nucleotide Archive. To access the data contained within the Study please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Oligodendrocyte (OL) pathology is increasingly implicated in neurodegenerative diseases, as they are involved in metabolic support of axons and functional cross-talk with other brain cells. Rodent OLs are heterogeneous, with developmental and biological differences, but the extent of heterogeneity in the normal human brain and its contribution to any changes to disease remains unknown. Here we performed single nuclei RNA-sequencing (snRNA-seq) from white matter (WM) areas of post mortem human brain both in control (Ctr) and multiple sclerosis (MS) patients. We identified several sub-clusters of oligodendroglia in the Ctr human WM, some similar to those in mouse, and defined new markers for these cell states. Strikingly, some of these sub-clusters were under-represented in MS tissue, while others were more prevalent than in controls. We found a lack of OL precursor cells (OPCs) and an OL subcluster in an intermediate stage of differentiation in MS lesions and in normal appearing white matter (NAWM), suggesting either depletion by the disease or by a regenerative response. The differences in mature OL sub-clusters indicate different functional states of OLs in MS tissue and, as this is similar in NAWM to lesions, that MS is a more diffuse brain disease than the focal demyelinating lesions suggest. We were also able to identify new putative markers of different MS lesion subtypes. Our findings of an altered heterogeneity of oligodendroglia in MS may have an important contribution to our understanding of disease progression and may alter therapeutic approaches to MS.

  Study EGAS00001003412

• Molecular subtypes of pancreatic neuroendocrine tumors mutated in MEN1/DAXX/ ATRX explain biological variability

  Pancreatic Neuroendocrine Tumors (PanNETs) most probably arise from the islets of Langerhans, although direct evidence is still lacking. About 40% of non-functioning (NF) PanNETs harbour mutations in MEN1, often in conjunction with alterations in DAXX/ATRX. Despite the homogeneous genetic background, the ADM (mutation in DAXX/ATRX and MEN1) group is heterogeneous regarding molecular features and response to treatment. To explore the molecular and clinical heterogeneity within this group, we integrated transcriptomic and DNA methylation data from 36 and 93 samples, respectively. Using a multi-omic approach we described a two-level hierarchy separating PanNET subtypes mutated in MEN1, DAXX and ATRX. First, DNA methylation discriminates ADM-PanNET from PanNET mutated in MEN1 only (α-like). Compared to α-like, all ADM tumors exhibit DNA methylation changes at enhancer and lowly methylated regions as well as at peri-centromeric and telomeric regions, associated with alternative lengthening of telomeres, increased chromosomal instability, and enhanced proliferative capacity. At a second level, transcriptomic analysis revealed three distinct ADM subtypes: ADM hypoxic, ADM NST (No Special Type), and ADM immunosuppressive. The ADM hypoxic subtype is characterized by strong hypoxia signature, likely regulated epigenetically. The ADM NST subtype appears to be primarily driven by epigenetic changes that promote proliferation. The ADM immunosuppressive is highly enriched in immune component and strong metastasis-like signature. While all the three subtypes are highly associated with liver metastasis formation and invasion, the ADM immunosuppressive are significantly smaller (<2.5 cm p.value=0.023) despite displaying aggressive molecular features. By defining these three novel ADM subtypes, our study provides a refined framework for understanding PanNET heterogeneity. This classification underscores potential diagnostic markers and highlights distinct biological vulnerabilities that may inform the development of subtype-tailored therapeutic strategies.

  Study EGAS00001008272

• Paraganglioma Smart-Seq2 Single Nuclei Sequencing Data

  Paraganglioma is a rare neuroendocrine tumor that originates from chromaffin cells of the adrenal medulla or extra-adrenal paraganglia. This study aims to investigate the heterogeneity of this tumor and to identify potential biomarkers associated with its malignancy. To achieve this, we performed single-nucleus RNA sequencing of human paraganglioma samples using the SMART-Seq2 protocol.

  Study EGAS50000001184

• Study of Addiction: Genetics and Environment (SAGE)

  This study is part of the Gene Environment Association Studies initiative (GENEVA) funded by the National Human Genome Research Institute. The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. The focus of this proposal is a case-control design of unrelated individuals for a genetic association study of addiction. Cases are defined as individuals with DSM-IV alcohol dependence (lifetime) and potentially other illicit drug dependence. In addition to the categorical diagnosis, we have data on ordinal measurements of number of DSM-IV symptoms for alcohol, nicotine, marijuana, cocaine, opiates and other drugs so that we will able to construct quantitative measurements of addiction severity over a wide range of substances. Controls are defined as individuals who have been exposed to alcohol (and possibly to other drugs), but have never met lifetime diagnosis for alcohol dependence or dependence on other illicit substances. Analyses that include refinement of the phenotype and incorporation of important demographic and environmental factors into association studies will be pursued. Cases and controls were selected from three large, complementary datasets: the Collaborative Study on the Genetics of Alcoholism (COGA), the Family Study of Cocaine Dependence (FSCD), and the Collaborative Genetic Study of Nicotine Dependence (COGEND). COGA: COGA was initiated in 1989 and is a large-scale family study that has had as its primary aim the identification of genes that contribute to alcoholism susceptibility and related characteristics. COGA is funded through the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Subjects were recruited from 7 sites across the U.S. Alcohol dependent probands were recruited from treatment facilities and assessed by personal interview. After securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through the alcohol dependent probands. Assessment involved a comprehensive personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Families with three or more first-degree relatives who were alcohol dependent were invited for more extensive testing, including neurophysiology evaluations (ERPs and EEGs) and a battery of neuropsychological assessments. Blood was obtained for genetic studies. Institutional Review Boards at all sites approved the protocols, including sharing in the NIAAA national repository. COGA has four Co-Principal Investigators Bernice Porjesz, Victor Hesselbrock, Howard Edenberg, and Laura Bierut. COGA includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-investigators are: University of Connecticut (Victor Hesselbrock); Indiana University (Howard Edenberg, John Nurnberger, Jr., Tatiana Foroud); University of Iowa (Samuel Kuperman); SUNY Downstate (Bernice Porjesz); Washington University in St. Louis (Laura Bierut, Alison Goate, John Rice); University of California at San Diego (Marc Schuckit); Howard University (Robert Taylor); Rutgers University (Jay Tischfield); Southwest Foundation (Laura Almasy). Q. Max Guo serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Family Study of Cocaine Dependence (FSCD): This project was initiated in 2000 as a case-control family study of cocaine dependence funded through the National Institute on Drug Abuse (NIDA; PI: Laura Bierut). The primary goal was to increase the understanding of the familial and non-familial antecedents and consequences of cocaine dependence. Cocaine dependent individuals were systematically recruited from chemical dependency treatment units (both public and private; residential and outpatient) in the greater St. Louis metropolitan area. Community based control subjects were identified through a Missouri Driver's License Registry (maintained at Washington University for research purposes) and matched by age, race, gender, and residential zip code. As a supplement to this project, blood samples were collected for future genetic analysis and were included in the NIDA Genetics Consortium. Phenotypic data, DNA, and cell lines are in the NIDA Center for Genetics Studies. Collaborative Genetic Study of Nicotine Dependence (COGEND): COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. All subjects were recruited from Detroit and St. Louis. Nicotine dependent cases and non-dependent smoking controls were identified and recruited. In addition, one sibling for each case and control subject was recruited in a subset of the sample. Over 56,000 subjects aged 25-44 years were screened by telephone, over 3,100 subjects were personally interviewed, and over 2,900 donated blood samples for genetic studies. All three studies (COGA, COGEND, FSCD) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, educational attainment, religious participation, and family structure. Other important covariates and/or potential moderators of genetic effects include comorbid addictions and age at initiation of use for cigarettes, alcohol and drugs. The assessments also include measures of various life stressors, such as physical and sexual abuse, which have been implicated in gene-environment interactions for several disorders. Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease"(HHSN268200782096C). Note for Publications Related to Study: The Study of Addiction: Genetics and Environment (SAGE) has not yet generated publications. Below is a listing of publications related to the three studies from which the SAGE sample was selected. COGA has over 228 publications listed at www.niaaagenetics.org This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000092

• Combination_therapies_for_personalized_cancer_medicine

  It is the ambition of the team formed by members of the Netherlands Cancer Institute (NKI) and the Cancer Genome Project at the Wellcome Trust Sanger Institute (WTSI) to unravel the genomic and phenotypic complexity of human cancers in order to identify optimal drug combinations for personalized cancer therapy. Our integrated approach will entail (i) deep sequencing of human tumours and cognate mouse tumours; (ii) drug screens in a 1000+ fully characterized tumour cell line panel; (iii) high-throughput in vitro and in vivo shRNA and cDNA drug resistance and enhancement screens; (iv) computational analysis of the acquired data, leading to significant response predictions; (v) rigorous validation of these predictions in genetically engineered mouse models and patient-derived xenografts. This integrated effort is expected to yield a number of combination therapies and companion-diagnostics biomarkers that will be further explored in our existing clinical trial networks.

  Study EGAS00001000655

• Host Response to Respiratory Infections

  In this study, we describe a strategy to identify undiagnosed respiratory viral infections through screening nasopharyngeal (NP) swabs for the chemokine CXCL10, which is highly induced in the nasopharynx as part of the host antiviral response. Using this strategy, we conducted two screens: first, we screened NP swabs collected at Yale New Haven Hospital (YNHH) in January 2017 from patients with undiagnosed respiratory viral infections and identified patients infected with seasonal coronaviruses and the relatively uncommon influenza C virus. Second, we screened samples from the beginning of March 2020 in the weeks before YNHH tested for SARS-CoV-2 and identified four patients that had undiagnosed SARS-CoV-2 infections. Sequences uploaded as part of this study include samples with rhinovirus, human coronavirus NL63, and 8 samples as part of the January 2017 screen for undiagnosed respiratory viral infections.

  Study phs002442

• OncoArray: Oral and Pharynx Cancer

  Genetic factors play an important role in susceptibility to head and neck cancer and could also explain differences in response to treatment and outcome. The goal of this project is to investigate genetic variation involved in oral and oropharyngeal cancer susceptibility by performing the largest GWAS on these diseases, using a novel genotyping tool specifically designed for cancer studies by the OncoArray Network. This project encompasses data from approximately 6000 oral and pharynx cancer cases and 4000 controls derived from 12 epidemiological studies most of case-control design. The genotyping was centralized at the Center for Inherited Disease Research (CIDR), and the epidemiological data harmonization and analysis were completed at the International Agency for Research on Cancer (IARC). This study was supported by National Institute of Dental and Craniofacial Research (NIDCR) with direct funding to CIDR.

  Study phs001202

• TCELL PILOT ATAC-SEQ

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001001317

• Family Investigation of Nephropathy and Diabetes (FIND) Study

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

  Study phs000333

• Disease specific alterations in the olfactory mucosa of patients with Alzheimer’s disease

  OOlfactory dysfunction manifests early in several neurodegenerative disorders. Olfaction is orchestrated by olfactory mucosal cells located in the upper nasal cavity. However, it is unclear how this tissue reflects key neurodegenerative features in Alzheimer’s disease. Here we report that Alzheimer´s disease olfactory mucosal cells secrete toxic amyloid-beta. We detail cell-type-specific gene expression patterns, unveiling 147 differentially expressed disease-associated genes compared to the cognitively healthy controls, and 5 distinct populations in globose basal cell -, myofibroblast-, and fibroblast/ stromal – like cells in vitro. Overall, coordinated alteration of RNA and protein metabolism, inflammatory processes and signal transduction were observed in multiple cell populations, suggesting a key role in pathophysiology. Our results demonstrate the potential of olfactory cell cultures in modelling Alzheimer´s disease. Moreover, for the first time we provide single cell data on olfactory mucosa in Alzheimer´s disease for investigating molecular and cellular mechanisms associated with the disease.

  Study EGAS00001006019

• Discordant_Monozygotic_Twins_ALS(Transcriptomics)

  Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by motor neuron degeneration. Despite known genetic contributions to both familial and sporadic ALS cases, much of ALS heritability remains unexplained, suggesting the involvement of other genetic and epigenetic factors. To address this gap, we used a comprehensive multi-omics approach, in a pair of Italian monozygotic twins discordant for ALS, integrating DNA methylation, transcriptomics, and whole exome sequencing (WES). Here we share transcriptomic data derived from bulk RNA-seq on RNA extracted from PBMCs of the twin pair. This analysis revealed distinct transcriptomic profiles underlying the discordant disease phenotypes.

  Study EGAS50000000909

• HipSci___RNAseq___Rare_BBS

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS cells generated from skin biopsies or blood samples from rare disease patients diagnosed with Bardet-Biedl syndrome 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/

  Study EGAS00001001318

• HipSci___RNAseq___Rare_Monogenic Diabetese

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS cells generated from skin biopsies or blood samples from rare disease patients diagnosed with Monogenic Diabetes. 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/

  Study EGAS00001001137

• Childhood Cancer Data Initiative (CCDI): Single-Cell Atlas of NF1 Nerve Sheath Tumors

  The worst outcome of NF1 is the development of the aggressive and highly metastatic malignant peripheral nerve sheath tumors (MPNST), which transform from benign plexiform neurofibroma (PN) and lack effective treatment. In this study, we performed single-cell RNA sequencing of 63 clinically annotated NF1-associated peripheral nerve sheath tumors, including 24 PN, 34 premalignant atypical neurofibromas, and 5 MPNSTs. These results improve the ability of identifying high-risk neurofibromas and provide a unique opportunity for early detection of MPNST.

  Study phs003519

• Whole genome sequencing in myasthenia gravis

  This dataset contains whole genome sequencing data on 25 individuals with myasthenia gravis. The data was generated using Illumina sequencing technology and is presented as BAM files for each sample.

  Dataset EGAD00001005262

• RNA-Seq data of de novo assembly individual EGYPT

  This is the blood RNA-Seq read data used for expression analysis such as haplotypic expression (mapped against GRCh38).

  Dataset EGAD00001006036

• Single_Cell_Targeted_Sequence_Capture

  This study is to ascertain whether it is feasible to extract single cell from a tumour, perform amplification, generate a library and sequence a targeted pulldown.

  Study EGAS00001000435

• Whole genome sequencing data of paediatric KMT2A-rearranged acute lymphoblastic leukemia

  Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

  Study EGAS50000001079

• RNA sequencing data of pediatric KMT2A-rearranged acute lymphoblastic leukemia

  Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

  Study EGAS50000001799

• Exome sequencing of early and late passage Patient Derived Xenogratf Tumoroids with matched Patient Derived Xenogratfs and matched normal liver

  The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. This specific dataset is focused on a comparison of early and late passage tumoroids transcriptomes. In this study we focused our attention on early (passage 3) and late (passage 8-12) PDXTs with their matched PDXs and normal liver

  Study EGAS50000000185

• Clinical and genetic analysis of retinopathy of prematurity - GWAS

  This project aims to identify genetic variants that contribute to the development of retinopathy of prematurity (ROP). ROP is a leading cause of childhood blindness worldwide. We have performed a genome wide association study (GWAS) using Illumina Global Screening Array (GSA) BeadChip. For this GWAS design we assembled the largest group of ROP patients described to date: around 1000 ROP patients in total. Demographic and phenotypic information associated with each sample includes gestational age, gender, ethnicity, multiple birth status, presence of ROP, and birth weight. The results of this study will help advance our understanding of the genetic susceptibility to ROP and may result in novel treatment options to slow the progression of the disease.

  Study phs002020

• NSD2 E1099K Drives Relapse in Pediatric Acute Lymphoblastic Leukemia by Disrupting 3D Chromatin Organization

  NSD2 p.E1099K (EK) is a mutation shown to be enriched in patients that relapse with pediatric Acute Lymphoblastic Leukemia (ALL). This work aims to uncover 3D chromatin architecture-related mechanisms responsible for drug resistance or therapy failure in these patients. We investigated this by performing Hi-C, ATAC-Seq, and RNA-Seq on three B-ALL cell lines heterozygous for EK either expressing a NSD2 targeting small RNA (sRNA) or a non-targeting small hairpin RNAs (shRNA). We also performed RNA-Seq on three matched diagnosis/relapse B-ALL samples with the relapse enriched EK mutation.

  Study phs003195

• Center for Craniofacial and Dental Genetics (CCDG): Genetics of Orofacial Clefts and Related Phenotypes

  The purpose of this study is to investigate the genetics of orofacial clefts (OFCs) in a large study population, and importantly, to incorporate subclinical phenotypic features into these studies. Orofacial clefts (OFCs) comprise a significant fraction of human birth defects (about 1/700 live births (Rahimov et al. 2012) and represent a major public health challenge, as individuals with these anomalies require surgical, nutritional, dental, speech, medical and behavioral interventions, thus imposing a substantial economic and personal burden (Berk and Marazita 2002*). The most common forms include OFCs of the lip alone (CL, Figure 1A), CL plus cleft palate (CL+CP, Figure 1B) or of the palate only (CP, Figure 1C). Individuals born with OFC may have their first surgical repair at age 3 months, but this initial surgery is just the beginning of a lifetime of health burdens. An individual born with an OFC has a hospital use rate increased for most ages (up to 233% increase for children ages 0-10 years and 16% for middle aged adults (Wehby et al. 2012). Healthcare costs for children with OFCs are estimated to be 800% greater compared with their unaffected peers (Boulet et al. 2009). Data from Denmark show that people born with CL with or without CP (CL/P) have an increased mortality up to age 55, which may be attributed to an increased risk of suicide and/or certain cancers (Christensen et al. 2004). The focus of most OFC genetic research has been CL and/or CP. Furthermore, the majority of OFC, i.e. about 70% of CL/P and 50% of CP is considered "nonsyndromic" (Jones 1988), i.e. isolated anomalies with no other apparent cognitive or structural abnormalities. Figure 1 Sample OFC Types A: Bilateral Cleft Lip; B:Cleft Lip plus Cleft Palate; C:Cleft Palate Alone The factors leading to the majority of nonsyndromic OFCs are still unclear, particularly at an individual family level. As is true for many complex traits, substantial progress in gene identification has occurred in the OFC field in the last two years (Dixon et al. 2011; Marazita 2012). Genome Wide Association Studies (GWAS) and sequencing studies to date by our research team and others have focused on genetic risk factors for overt CL/P and CPO-and have been very successful. A major finding from this work is that OFCs exhibit significant genetic heterogeneity, i.e., multiple genetic regions have been implicated (Beaty et al. 2010; Ludwig et al. 2012). Thus, approaches are needed to understand this genetic heterogeneity. Are there GxG interactions at work? Are there subsets of families, each due to a different gene? Our research group has shown that a promising approach to dissect the etiology of OFC is to focus on subclinical phenotypic features within entire cleft families (not just in affected cases, but also in their non-cleft relatives). These subtle features are believed to represent mild manifestations of the same underlying genetic susceptibility responsible for OFCs; as such, their inclusion in case-control and family-based genetic studies can help to clarify and refine the relationship between genotype and phenotype. The study population comprises a large number of families and individuals (~12,000 individuals) from multiple populations worldwide (Caucasians from the US and Europe, Asians from China and the Philippines, Mixed Native American/Caucasians from South America, and Africans from Nigeria and Ethiopia). There are cases, case families (nuclear families and extended kindreds), as well as controls with no history of OFC nor other developmental defects. *Berk NW, Marazita ML (2002) Costs of Cleft lip and Palate: Personal and Societal Implications. In: Wyszynski DF (ed) Cleft Lip and Palate: From Origin to Treatment. Oxford University Press, Inc., New York, pp 458-467.

  Study phs000774