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• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB22_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB22_M

  Dataset EGAD00001001708

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB24_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB24_F

  Dataset EGAD00001001713

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB52_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB52_M

  Dataset EGAD00001001765

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB57_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB57_M

  Dataset EGAD00001001771

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_C

  Dataset EGAD00001001775

• Dataset for Direct Detection of Early-Stage Cancers using Circulating Tumor DNA

  The dataset for Direct Detection of Early-Stage Cancers using Circulating Tumor DNA includes 602 bam files from next-generation sequencing on the Illumina HiSeq2500 or MiSeq. The samples analyzed include cancer cell lines as well as plasma and tissue specimens from healthy individuals and patients with cancer.

  Dataset EGAD00001003601

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW14_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW14_M

  Dataset EGAD00001001789

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW15_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW15_C

  Dataset EGAD00001001790

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW18_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW18_C

  Dataset EGAD00001001793

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_C

  Dataset EGAD00001001826

• Exome sequencing of patients with rare neurological disorders

  Exome sequencing of patients and their families with diverse rare neurological disorders. Some families have prior linkage data identifying a specific chromosomal interval or interest, other families do not have linkage data available. Many of these families come from special populations whose demography or preference for consanguineous marriages make them particularly tractable for genetic studies.

  Dataset EGAD00001000346

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW4_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW4_M

  Dataset EGAD00001001834

• BLUEPRINT release August 2015, DNase-Hypersensitivity for macrophage, on genome GRCh38

  DNase-Hypersensitivity data for 16 macrophage sample(s). 20 run(s), 16 experiment(s), 16 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001581

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW20_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW20_M

  Dataset EGAD00001001798

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW24_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW24_C

  Dataset EGAD00001001802

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_F

  Dataset EGAD00001001827

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_C

  Dataset EGAD00001001823

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW27_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW27_C

  Dataset EGAD00001001805

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW2_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW2_F

  Dataset EGAD00001001812

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB55_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB55_F

  Dataset EGAD00001001767

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB62_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB62_C

  Dataset EGAD00001001778

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW18_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW18_F

  Dataset EGAD00001001794

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW29_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW29_C

  Dataset EGAD00001001808

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_F

  Dataset EGAD00001001821

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days B-glucan, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days B-glucan sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002314

• BLUEPRINT release August 2016, RNA-Seq for monocyte - None, on genome GRCh38

  RNA-Seq data for 4 monocyte - None sample(s). 4 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002338

• BLUEPRINT release August 2016, DNase-Hypersensitivity for erythroblast, on genome GRCh38

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

  Dataset EGAD00001002350

• Whole exome sequencing data from CD4 T cells, NK cells, monocytes and granulocytes from Alzheimer's disease patients and control individuals

  This dataset contains whole exome sequencing data for 343 samples collected from 118 male donors, including 66 Alzheimer disease patients and 52 controls. DNA for sequencing was extracted from FACS-isolated CD4 T cells, NK cells and myeloid cells (monocytes or granulocytes).

  Dataset EGAD00001015757

• BLUEPRINT release August 2016, DNase-Hypersensitivity for Acute Lymphocytic Leukemia - CTR, on genome GRCh38

  DNase-Hypersensitivity data for 2 Acute Lymphocytic Leukemia - CTR sample(s). 2 run(s), 2 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002499

• HPS1 patient monocyte-derived macrophage and control macrophage RNAseq with and without infection

  4 HPS1 patient monocyte-derived macrophages and 4 controls were RNA sequenced at baseline and after Salmonella Typhimurium infection. We used paired end sequencing on an Illumina HiSeq 4000. Each sample was run on 3 lanes for sequencing depth, which we combined for our analysis.

  Dataset EGAD00001006978

• NHLBI Family Heart Study (FamHS-Visit1 and FamHS-Visit2)

  The Family Heart Study (FamHS) was funded by the National Heart, Lung, and Blood Institute (NHLBI). It was begun in 1992 with the ascertainment of 1,200 families, half randomly sampled, and half selected because of an excess of coronary heart disease (CHD) or risk factor abnormalities as compared with age- and sex-specific population rates (Higgins et al. 1996). The families, with approximately 6,000 individuals, were sampled on the basis of information on probands from four population-based parent studies: the Framingham Heart Study, the Utah Family Tree Study, and two Atherosclerosis Risk in Communities (ARIC) centers (Minneapolis, and Forsyth County, NC). A broad range of phenotypes were assessed at a clinic examination in broad domains of CHD, atherosclerosis, cardiac and vascular function, inflammation and hemostasis, lipids and lipoproteins, blood pressure, diabetes and insulin resistance, pulmonary function, and anthropometry (FamHS Visit 1). Approximately 8 years later, study participants belonging to the largest pedigrees were invited for a second clinical exam (FamHS Visit 2). A total of 2,756 Caucasian subjects in 508 extended families were examined. A two-phase design was adopted for the genome wide association (GWA) study. In phase-1, 1007 subjects were chosen, equally distributed between the upper and lower quartile of age- and sex-adjusted values for coronary artery calcification, assessed by CT scan in Visit 2. These subjects were chosen to be largely unrelated; 34% of the subjects were from unique families, while 200 other subjects had 1 or more siblings selected into the sample, yielding a sample of 465 unrelated subjects. The remaining family members (N=1749) were genotyped in the phase-2 for replication of the top hits from the phase-1. The results presented here represent those for the analysis of the phase-1 case-control sample for variables assessed in FamHS Visit 1 (from 1992 to 1995) and for the variables assessed in FamHS Visit 2 (from 2002 to 2003). All subjects were typed on the Illumina HumMap 550 chip (Phase 1 genotype). Of these, 33 (3.3%) were excluded due to technical errors, call rates below 98%, and discrepancies between reported sex and sex-diagnostic markers. The final sample of 974 subjects have Visit 2 phenotypes, approximately 100 of these do not have Visit 1 phenotypes. There was no significant plate-to-plate variation in allele frequencies. The covariate adjustments were performed separately by sex using cubic polynomial age and clinical centers, and retaining the terms in the stepwise regression analysis that were significant at the 5% level. Extreme outliers (>4 SD from the mean) were set aside, temporarily, for the adjustments. The final phenotypes were computed for all individuals using the best mean regression models and standardizing to 0 mean and unit variance. The FamHS has contributed GWA results in many phenotype domains (antropometric and adiposity, atherosclerosis and coronary heart disease, lipid profile, diabetes and glicemic traits, metabolic syndrome etc) to meta-analyses and various consortia, including Heard-Costa et al. 2009, Köttgen et al. 2010, Teslovich et al. 2010, Nettleton et al. 2010, Lango et al. 2010, Heid et al. 2010, Speliotes et al. 2010, Dupuis et al. 2010, Kraja et al. 2011.

  Study phs000221

• Detection of rare mutations, copy number variation, and DNA methylation in the same template DNA molecules

  The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics in the future.

  Study EGAS00001006839

• Longitudinal Study of Genetic Causes of Intrahepatic Cholestasis

  This longitudinal observational study will investigate the natural history and progression of four genetic causes of intrahepatic cholestasis of childhood, including alpha-1 antitrypsin deficiency (α1-AT), Alagille syndrome (AGS), progressive familial intrahepatic cholestasis (PFIC), and bile acid synthesis defects (BAD). This study will be conducted as part of the Cholestatic Liver Disease Consortium (CLiC), an NIH-funded multi-centered Rare Disease Clinical Research Consortium. In this study, we will collect defined data elements in a uniform fashion at fixed intervals for five years over a relatively large number of patients with these rare disorders. In addition, a biobank of patient specimens and DNA samples will be established for use in ancillary studies to be performed in addition to this study. By comparing outcome measures between the four liver diseases (i.e., using each disorder as a disease-control for the other disorders), the full impact of each disorder can best be determined in comparison to the other liver diseases. Using the longitudinal database in this fashion, this study will provide an improved understanding of the effects of the cholestatic liver during childhood irrespective of the underlying etiology as well as to the pathophysiology, outcome, and complications of each of the disorders. This initial characterization will allow calculation of sample sizes for future therapeutic intervention clinical trials and provide the baseline to which interventions should be compared.

  Study phs001288

• Organoid BulkRNAseq

  Here, we aim to understand early determinants of brain organoid quality and generated a set of brain organoids from 12 different hPSC lines using an adaptation of the unguided Lancaster protocol (Lancaster & Knoblich, 2014). All cell lines were obtained from healthy donors. 12 donor hPSC lines were differentiated into brain organoids and a set of 72 individual organoids (6 per line) were randomly selected morphologically diverse organoids and subjected to bulk RNA sequencing for gene expression analysis. RNA was extracted using the RNeasy kit (Qiagen, Germany) according to the manufacturer’s instructions. A total of 1000 ng per sample was sent for RNA sequencing to Azenta Life Sciences (Genewiz Leipzig, Germany) for sequencing. Library preparation was performed using the NEBNext Ultra II RNA Library Prep Kit for Illumina following manufacturer’s instructions (NEB, Ipswich, MA, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End (PE) configuration v1.5 at a depth of >20 million reads in each sample. Raw sequence data (.bcl files) generated from Illumina NovaSeq6000 was converted into fastq files and de-multiplexed using Illumina bcl2fastq program version 2.20. The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014).

  Study EGAS50000000659

• Ultrasensitive detection of minor allele fractions in maternal samples based on microhaplotype analysis

  Background: Tools for quantifying and studying genetic variation in heterogeneous cell populations have a limited sensitivity. However, many biological problems require ultrasensitive genetic deconvolution of cell mixtures. The detection of fetal cells in cervical samples from pregnant women presents such a challenge. Methods: Using deep sequencing of microhaplotype loci and analysis of the error profiles in genetically pure and mixed samples, we increase the sensitivity for minor allele detection and demonstrate the potential for paternal allele detection in cervical samples from pregnant women. Results: Using artificial DNA mixtures, we show that unique alleles can be readily identified in mixtures with a ratio of 1 in 10,000 (0.01%). By applying this method on ten non-invasive cervical samples from pregnant women we could identify the presence of paternal alleles in half of the samples. Unexpectedly, we demonstrate those alleles not to be of fetal origin but of sperm. Conclusions: We developed a novel method that can deconvolute genetic mixtures to at least 0.01%, which can be applied to study minor allele fractions in genetic mixtures and, for example, could aid in trophoblast cell detection in maternal blood or cervix. Our results demonstrate the importance of discriminating fetal and paternal alleles when analyzing cervical fetal cells and may pinpoint a potential cause of erroneous results in previous trophoblast isolation studies.

  Study EGAS00001007057

• UK10K NEURO ASD TAMPERE

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Tampere Autism sample set consists of samples from Finnish subjects with ASD (autism spectrum disorders) with IQs over 70 recruited from a clinical centre for the diagnosis and treatment of children with ASD. For further information on this cohort please contact either Terho Lehtimaki (terho.lehtimaki@uta.fi) or Kaija Puura (kaija.puura@pshp.fi).

  Study EGAS00001000115

• Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

  ChIP-Seq - CEBPE - REH. The ETV6/RUNX1 translocated acute lymphoblastic leukaemia cell line REH was used to perform ChIP-Seq using a CEBPE antibody. Cells were fixed in 1% formaldehyde for 10mins, prior to preparation of chromatin using Active Motif Express ChIP-IT. 2ug of antibody (anti CEBPE Atlas Antibodies HPA002928)was added to 25ug of chromatin O/N at 4C with rotation. Duplicate reactions were pooled and purified. 10ng of ChIPd and input DNA used for Illumina NGS preparation (NEBNext ChIP-Seq Library kit; New England Biolabs), CEBPE and Input DNA ChIP samples were sequenced on a MiSeq using 150bp Kit v3 paired end and a HiSeq 2500 using 2x101 version 4 paired end (Illumina) respectively. Reactions performed in duplicate. shCEBPE RNA-Seq - REH. REH cells were lentivirally transduced with a pTRIPZ shRNA vector for transcriptional profiling of CEBPE. Two controls (empty and non-targeting) and two CEBPE shRNAs (V3THS_150517(A13), V3THS_404312(G3) Dharmacon, GE) were transduced into REH cells. Cells were treated with 1ug/ml doxycyclin for 144hrs and total RNA purified. Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit and sequenced on an Illuimna HiSeq 2500 using 2x101 version 4 paired end chemistry. 3 biological replicates of each samples were prepared.

  Study EGAS00001002877

• Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target

  High-risk sarcomas, such as metastasized and relapsed Ewing and CIC-rearranged sarcomas, currently lack effective curative systemic treatment options. Precision medicine approaches offer hope and among them ex vivo drug response profiling of patient-derived tumor cells emerges as a promising novel tool to identify effective drugs for individual patients. Here, we established ex vivo culture conditions to propagate Ewing and CIC::DUX4-sarcomas as tumoroids in a co-clinical manner. These models retained their original molecular and functional characteristics, including recurrent ARID1A mutations, and serve as tumor avatars suitable for large-scale drug testing. Indeed, screening a large drug library on a small living biobank of such tumors not only revealed distinct response differences between the two entities, but also identified an exceptional dependency of CIC::DUX4-sarcoma cells on MCL1. Mechanistically. MCL1 was identified as a direct target gene of the CIC::DUX4 fusion protein. Genetic and pharmacological inhibition of MCL1 induced rapid apoptosis in CIC::DUX4-sarcoma cells and inhibited tumor growth in a xenograft model. Thus, MCL1 represents a potential novel therapeutic target for CIC::DUX4-sarcoma. Overall, our study highlights the feasibility of co-clinical drug response profiling for individual sarcoma cases and suggests that prospective tests, to evaluate its clinical benefit are highly warranted.

  Study EGAS00001008039

• Whole Genome Sequencing of clonal expansions of single healthy somatic cells (human, female)

  HSC73_clone: Bone marrow mononuclear cells from the healthy 73 years old female were thawed and labeled with Alexa-Fluor 488-conjugated anti-CD34 (581, Biolegend), Alexa-Fluor 700-conjugated anti-CD38 (HIT2, eBioscience), a cocktail of APC-conjugated lineage antibodies consisting of anti-CD4 (RPA-T4), anti-CD8 (RPA-T8), anti-CD11b (ICRF44), anti-CD20 (2H7), anti-CD56 (B159, all BD Biosciences), anti-CD14 (61D3), anti-CD19 (HIB19) and anti-CD235a (HIR2, all eBiocience) and 1 micro-gram/ml propidium iodide (Sigma). Using a BD FACSAria cell sorter, single Lin-CD34+CD38-PI- cells were individually sorted into low-adhesion 96-well tissue culture plates (Corning) containing 100micro-litre of StemSpan Serum-Free Expansion Medium (Stemcell technologies) supplemented with 100ng/ml of human SCF and FLT-3L, 50ng/ml of human TPO, 20ng/ml of human IL-3, IL-6 and G-CSF (all cytokines from Peprotech) and 50U/ml of penicillin and 50μg/ml of streptomycin (Sigma). Cells were incubated at 37 degrees C in a humidified atmosphere with 5% CO2 in air. After 5 days in culture, another 100micro litres of cytokine-containing medium were added. 13 days after seeding, clones B6 and G2 had expanded to approx. 105 cells and were selected for whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) after tagmentation-based library preparation (see Extended Experimental Procedures) for clone B6 and standard library preparation for clone G2. For germline-control ~106 unsorted bone marrow mononuclear cells from the same donor were used for sequencing. An average of 30-fold sequence coverage for each the clones and the matching control were obtained.L4clone: A progenitor cell clone was raised from a peripheral blood sample of the 39 year old healthy female. Frozen peripheral blood mononuclear cells (PBMCs) were isolated from 2 ml heparinised peripheral blood via Ficoll Paque density centrifugation. A methylcellulose assay was performed as described earlier (Weisse et al., 2012). In brief, non-adherent mononuclear cells were incubated in the presence of the recombinant human cytokines IL-3, IL-5 and GM-CSF (R&D systems) over 14 days to induce colony formation. Colonies were detected under an inverted light microscope, and plucked by a pipette when colonies had approximately 10,000 cells/CFU. Each colony was washed three times in PBS and finally frozen as a cell pellet in -80 degrees C. Genomic DNA was isolated using the QIAamp DNA micro kit according to the instructions of the manufacturer (Qiagen, Hilden, Germany). Whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) was performed for colony 4 after tagmentation-based library preparation and resulted in 15-fold sequence coverage for each the colony and the matching whole blood.

  Dataset EGAD00001000666

• ProHealth: Kaiser Permanente Genome-wide Association Study of Prostate Cancer

  A genome-wide association study (GWAS) of prostate cancer (PCa) was conducted in Kaiser Permanente (KP) Northern California health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) [PMID: 26034056]. The data for these members were drawn from three KP cohort studies: Research Program in Genes, Environment and Health (RPGEH) ProHealth, and California Men's Health Study (CMHS) (described further under Study History). Four custom arrays were designed for genotyping, one for each of the four major race-ethnicity groups in the RPGEH cohort: African Americans, East Asians, Latinos, and Non-Hispanic Whites. The number of SNPs and SNP content varied by array, with SNP content designed to maximize the genome-wide coverage of low frequency and more common variants specific to the different race-ethnicity groups, including newly identified SNPs from sequencing projects, and SNPs with established associations with disease phenotypes and risk factors [PMIDs: 21565264, 21903159]. Within the total study cohort, n=34,736 completed a consent which permitted deposition of data to NIH. Genotyping followed the same general procedure described in [PMIDs: 26092718, plus additional quality control (QC) steps for the additional men, in order to control for potential batch and kit effects, described in [PMID: 26034056. Briefly, we first repeated the filters described in [PMID: 26092718] for all four arrays (EUR, LAT, EAS, AFR). Then, on an array-wise basis, we removed SNPs with MAF<0.01, with a call rate<95%, or with Hardy-Weinberg Equilibrium (HWE) p-value in homogeneous groups<1x10ˆ-5. Furthermore, on the EUR array, to adjust for potential kit effect, we conducted a GWAS of kit, and removed those kit associated SNPs with p<1x10ˆ-6; we also re-genotyped each of the new samples (those not genotyped with the original GERA data) with some of the original GERA data, and removed SNPs with >13/1,268 (1%) mismatches. For the AFR array, to adjust potential plate batch issues, we conducted a GWAS of whether an individual was in the original GERA data vs. in the newly genotyped data and removed those batch-associated SNPs with p<0.05 (we used a stronger threshold than that used for the EUR array because there were fewer individuals on the AFR array); we also re-genotyped each of the new samples with the original GERA data and removed SNPs with >2/78 (2.6%). After the QC described above, imputation was performed as described in [PMID: 26034056]. Imputation was performed on an array-wise basis, pre-phasing with SHAPE-IT v2.5 [PMID: 22138821], and imputing from the 1000 Genomes Project October 2014 release as a cosmopolitan reference panel with IMPUTE2 [PMID: 22384356]. In addition to the GWAS described above, a nested exome-wide association study (EWAS) of PCa was also conducted (7,489 cases, 7,323 controls; 78% non-Hispanic white, 9% African-American, 3% East Asian, 6% Latino, 4% Other). A custom EWAS array primarily focused on rare variants was designed for genotyping that complemented the GWAS arrays [PMID: 26034056]. The EWAS array content included missense and loss-of-function mutations, and rare exonic mutations from The Cancer Genome Atlas (TCGA) and dbGaP prostate cancer tumor exomes [PMID: 26544944; PMID: 26544944]. Much of the EWAS array design content overlapped with the probesets on the UK Biobank Affymetrix Axiom array [PMID: 30305743]. Genotyping and QC steps taken to filter out samples exhibiting low quality and variants with low call rates are described in Emami et al., 2020 [biorXiv]. The resulting EWAS array genotypes are provided here.

  Study phs001221

• Search for new loci and low-frequency variants influencing glioma risk by exome-array analysis

  To identify protein altering variants (PAVs) for glioma we analysed Illumina HumanExome BeadChip exome array data on 1,882 glioma cases and 8,079 controls from three independent European populations. In addition to single variant tests we incorporated information on the predicted functional consequences of PAVs and analysed sets of genes with a higher likelihood of having a role in glioma on the basis of the profile of somatic mutations documented by large-scale sequencing initiatives. Globally tThere was a strong relationship between effect size and SNPs predicted to be damaging (P=2.29x10-49); however, these variants which are most likely to impact on risk, are rare (MAF<5%). While no single variant showed an association which was statistically significant at the genomewide threshold a number of represented promising Notable associations - were seen with the BRCA2:c.9976A>T, p.(Lys3326Ter)BRCA2 p.Lys332X variant, which has been shown documented to influence breast and lung cancer risks (OR=2.3, P=4.00x10-4 for glioblastoma [GBM]) and IDH2:c.782G>A, p.(Arg261His)IDH2 p.Arg261His (OR=3.21, P=7.67x10-3, for non-GBM). Additionally, gene burden tests revealed a statistically significant association for HARS2 and risk of GBM (P=2.20x10-6). Genome scans of low frequency PAVs represent a complementary strategy to identify disease-causing variants compared with scans based on tagSNPs. Strategies to lessen the multiple testing burden by restricting analysis to PAVs with higher priors affords an opportunity to maximise study power.

  Study EGAS00001001258

• DAC for achalasia risk consortium (arc)

  Dac EGAC00001000394

• DAC for aflatoxin project Qidong patient samples

  Dac EGAC00001000679

• DAC for the cfSort study (tissue deconvolution)

  Dac EGAC00001003222

• DAC for seminoma genomic data

  Dac EGAC00001000231

• DAC for study WES of pituitary tumors

  Dac EGAC00001000443

• Data Access Committee for Hui Lab

  Dac EGAC00001000454

• INRP - Italian Network for Research on Psychoses

  Dac EGAC00001000576

• DAC for MOMA, Short QT Exome data

  Dac EGAC00001000730

• DAC for Dissecting Tumor Microenvironment with Tumorgrafts

  Dac EGAC00001000809