384 results for "PhD+2001+"cited+by"+18000+"Professor+of+Bioinformatics"+"2012"+"2019""

in 16.93 milliseconds.

• Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

  Study phs000948

• Miller Huang, PhD

  Dac EGAC00001001185

• T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis

  Data Access Committee means the following members of the Institute of Experimental Immunology, UZH, and investigators of the publication, to which the Data Access refers and who are responsible for making the data access decisions for the genotype and gene expression generated by UZH or respectively the epigenetic data, which had been generated at Karolinska Institute. Christian Münz, PhD; Co-Director of Institute of Experimental Immunology, University of Zurich Roland Martin, MD; Affiliated Group Leader (to Institute Münz) of Institute of Experimental Immunology Institute of Experimental Immunology Ivan Jelcic, PhD, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland Maja Jagodic, PhD, Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden

  Dac EGAC50000000464

• Exome trios in patients with gastroschisis (2019-04-08)

  Gastroschisis (MIM 230750) is a herniation of the intestines through a defect of the abdominal wall lateral to the umbilicus (usually on the right side), and it is not covered by a membrane [Ledbetter, 2012]. Gastroschisis is a congenital anomaly with increasing incidence, easy prenatal diagnosis and extremely variable postnatal outcomes. On the basis of clinical manifestations, epidemiologic charateristics, and the presence and type of additional malformations, gastroschisis could be considered a heterogeneous condition with no gene/s discovered yet. This congenital anomaly affects approximately 1-3 infancts per 10,000 live births [Calzolari et al.1995;Parker et al.,2010] Current knowledge about causative mutations/variants. To date, no single gene has been linked to gastroschisis. Some publications have tried to link this malformation to variants in genes (such as AEBP1 (adipocyte enhancer binding protein) gene [Feldkamp et al,. 2012] or the VEGF-NOS3 pathway [Lammer et al., 2008]. Previously, a Scribble mutant mouse model (circletail) was reported to exhibit gastroschisis, however recent studies demonstrated that the Scribble knockout fetus exhibits exomphalos phenotype of gastroschisis [Carnagham et al., 2013]. 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 2019-04-08.

  Dataset EGAD00001004942

• Dr. Fatima Mechta-Grigoriou, PhD, DR1 Head of Stress and Cancer Laboratory Deputy Director Genetic and Biology of Cancer Dpt Institut Curie - Inserm U830

  Dac EGAC00001000581

• Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer

  A subset of Cancer-Associated Fibroblasts (CAF-S1) mediates immunosuppression in breast cancers (BC), but its heterogeneity and its impact on immunotherapy response remain unknown. Here, we identify 8 CAF-S1 clusters by analyzing more than 18000 single CAF-S1 fibroblasts from BC. We validate the 5 most abundant clusters by flow cytometry and in silico analyses in other cancer types, highlighting their relevance. Myofibroblasts from clusters 0 and 3, characterized by extra-cellular matrix proteins and TGF signaling respectively, are indicative of primary resistance to immunotherapies. Cluster 0 up-regulates PD-1 and CTLA-4 protein levels in regulatory T lymphocytes (Tregs), which in turn increases CAF-S1 cluster 3 cellular content. Thus, our study highlights a positive feedback loop between specific CAF-S1 clusters and Tregs and uncovers their role in immunotherapy resistance.

  Study EGAS00001004030

• PAGE Dataset Mar 2019

  PAGE Dataset Mar 2019

  Dataset EGAD00001005007

• PAGE Dataset Mar 2019

  PAGE Dataset Mar 2019

  Dataset EGAD00001005008

• PAGE Dataset Mar 2019

  PAGE Dataset Mar 2019

  Dataset EGAD00001005006

• NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Registry (VU_AF)

  The Vanderbilt Atrial Fibrillation (AF) Registry was founded in 2001. Patients with AF and family members are prospectively enrolled. At enrollment a detailed past medical history is obtained along with an AF symptom severity assessment. Blood samples are obtained for DNA extraction. Patients are followed longitudinally along with serial collection of AF symptom severity assessments.

  Study phs001032

• PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  Dataset EGAD00001004840

• PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  Dataset EGAD00001004841

• PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  PAGE Dataset Apr 2018 (Ref: PAGE Lancet 2019)

  Dataset EGAD00001004839

• UK10K_NEURO_FSZ REL-2012-11-27

  UK10K_NEURO_FSZ REL-2012-11-27

  Dataset EGAD00001000318

• Whole Genome Sequencing of INTERVAL (2019-06-12)

  Whole genome sequencing of participants from the INTERVAL study. . This dataset contains all the data available for this study on 2019-06-12.

  Dataset EGAD00001005084

• UK10K_OBESITY_SCOOP REL-2012-01-13

  UK10K_OBESITY_SCOOP REL-2012-01-13

  Dataset EGAD00001000181

• VALCAP files for Ma et al. (2019) SCMC Hybrid

  VALCAP files for Ma et al. (2019) Genome Biology (accepted) titled “Analysis of error profiles in deep next-generation sequencing data"

  Dataset EGAD00001004595

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: kidney (2019-03-26)

  This dataset contains all the data available for this study on 2019-03-26.

  Dataset EGAD00001004867

• ORCADES 15x (2019-07-23)

  15x whole genome sequencing in samples from the isolated population of Orkney. This dataset contains all the data available for this study on 2019-07-23.

  Dataset EGAD00001005194

• Bisulphite MPN colonies (2019-09-05)

  Swift kit whole genome bisulphite of MPN colonies . This dataset contains all the data available for this study on 2019-09-05.

  Dataset EGAD00001005313

• UK10K_NEURO_FSZNK REL-2012-01-13

  UK10K_NEURO_FSZNK REL-2012-01-13

  Dataset EGAD00001000183

• UK10K_NEURO_ASD_TAMPERE REL-2012-11-27

  UK10K_NEURO_ASD_TAMPERE REL-2012-11-27

  Dataset EGAD00001000314

• UK10K_NEURO_FSZNK REL-2012-11-27

  UK10K_NEURO_FSZNK REL-2012-11-27

  Dataset EGAD00001000332

• Targeted analysis of chondrosarcoma cancer genes (2019-04-01)

  Targeted analysis of chondrosarcoma cancer genes . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004877

• UK10K_NEURO_ASD_FI REL-2012-01-13

  UK10K_NEURO_ASD_FI REL-2012-01-13

  Dataset EGAD00001000173

• UK10K_NEURO_UKSCZ REL-2012-01-13

  UK10K_NEURO_UKSCZ REL-2012-01-13

  Dataset EGAD00001000182

• UK10K_NEURO_UKSCZ REL-2012-07-05

  UK10K_NEURO_UKSCZ REL-2012-07-05

  Dataset EGAD00001000256

• UK10K_NEURO_ASD_MGAS REL-2012-11-27

  UK10K_NEURO_ASD_MGAS REL-2012-11-27

  Dataset EGAD00001000312

• UK10K_NEURO_ASD_SKUSE REL-2012-11-27

  UK10K_NEURO_ASD_SKUSE REL-2012-11-27

  Dataset EGAD00001000313

• UK10K_NEURO_ASD_BIONED REL-2012-11-27

  UK10K_NEURO_ASD_BIONED REL-2012-11-27

  Dataset EGAD00001000310

• UK10K_NEURO_ASD_FI REL-2012-11-27

  UK10K_NEURO_ASD_FI REL-2012-11-27

  Dataset EGAD00001000311

• UK10K_NEURO_ASD_GALLAGHER REL-2012-11-27

  UK10K_NEURO_ASD_GALLAGHER REL-2012-11-27

  Dataset EGAD00001000316

• UK10K_NEURO_IMGSAC REL-2012-11-27

  UK10K_NEURO_IMGSAC REL-2012-11-27

  Dataset EGAD00001000320

• UK10K_NEURO_IOP_COLLIER REL-2012-11-27

  UK10K_NEURO_IOP_COLLIER REL-2012-11-27

  Dataset EGAD00001000321

• UK10K_NEURO_UKSCZ REL-2012-11-27

  UK10K_NEURO_UKSCZ REL-2012-11-27

  Dataset EGAD00001000335

• UK10K_RARE_NEUROMUSCULAR REL-2012-01-13

  UK10K_RARE_NEUROMUSCULAR REL-2012-01-13

  Dataset EGAD00001000180

• UK10K_RARE_NEUROMUSCULAR REL-2012-02-22

  UK10K_RARE_NEUROMUSCULAR REL-2012-02-22

  Dataset EGAD00001000189

• UK10K_RARE_NEUROMUSCULAR REL-2012-07-05

  UK10K_RARE_NEUROMUSCULAR REL-2012-07-05

  Dataset EGAD00001000219

• UK10K_RARE_NEUROMUSCULAR REL-2012-11-27

  UK10K_RARE_NEUROMUSCULAR REL-2012-11-27

  Dataset EGAD00001000298

• UK10K_NEURO_ABERDEEN REL-2012-11-27

  UK10K_NEURO_ABERDEEN REL-2012-11-27

  Dataset EGAD00001000315

• UK10K_NEURO_EDINBURGH REL-2012-11-27

  UK10K_NEURO_EDINBURGH REL-2012-11-27

  Dataset EGAD00001000317

• UK10K_NEURO_GURLING REL-2012-11-27

  UK10K_NEURO_GURLING REL-2012-11-27

  Dataset EGAD00001000319

• Paediatric IBD Mosaicism (2019-06-10)

  We want to investigate mosaic mutations as a cause of childhood IBD . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005079

• Psoriatic arthritis WGS (2019-08-07)

  Whole genome sequencing of immune cells from patients diagnosed with psoriatic arthritis . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005232

• UK10K_OBESITY_GS REL-2012-11-27

  UK10K_OBESITY_GS REL-2012-11-27

  Dataset EGAD00001000309

• Effects of GATA4-inhibiting compound 3i-2012 on HB-243 hepatoblastoma cells

  In order to study the effects of GATA4 inhibition, HB-243 hepatoblastoma cells were treated with GATA4-inhibiting compound 3i-2012 for 24 hours. Total RNA was extracted and sequenced.

  Study EGAS50000000999

• UK10K_NEURO_MUIR REL-2012-01-13

  UK10K_NEURO_MUIR REL-2012-01-13

  Dataset EGAD00001000170

• UK10K_NEURO_MUIR REL-2012-11-27

  UK10K_NEURO_MUIR REL-2012-11-27

  Dataset EGAD00001000322

• Evolution of the cancer epigenome in myeloproliferative neoplasms. (2019-04-01)

  Evolution of the cancer epigenome in myeloproliferative neoplasms. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004879

• Infant Spindle Tumour Study (2019-04-11)

  We aim to describe the transcriptomic landscape of infant spindle cell tumours. . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004954

• UK10K_RARE_SIR REL-2012-01-13

  UK10K_RARE_SIR REL-2012-01-13

  Dataset EGAD00001000153

• UK10K_RARE_HYPERCHOL REL-2012-01-13

  UK10K_RARE_HYPERCHOL REL-2012-01-13

  Dataset EGAD00001000167

• UK10K_RARE_CILIOPATHIES REL-2012-01-13

  UK10K_RARE_CILIOPATHIES REL-2012-01-13

  Dataset EGAD00001000168

• UK10K_RARE_HYPERCHOL REL-2012-02-22

  UK10K_RARE_HYPERCHOL REL-2012-02-22

  Dataset EGAD00001000186

• UK10K_RARE_SIR REL-2012-02-22

  UK10K_RARE_SIR REL-2012-02-22

  Dataset EGAD00001000188

• UK10K_RARE_CILIOPATHIES REL-2012-02-22

  UK10K_RARE_CILIOPATHIES REL-2012-02-22

  Dataset EGAD00001000191

• UK10K_OBESITY_SCOOP REL-2012-02-22

  UK10K_OBESITY_SCOOP REL-2012-02-22

  Dataset EGAD00001000193

• UK10K_RARE_HYPERCHOL REL-2012-07-05

  UK10K_RARE_HYPERCHOL REL-2012-07-05

  Dataset EGAD00001000207

• UK10K_RARE_CILIOPATHIES REL-2012-07-05

  UK10K_RARE_CILIOPATHIES REL-2012-07-05

  Dataset EGAD00001000217

• UK10K_RARE_SIR REL-2012-07-05

  UK10K_RARE_SIR REL-2012-07-05

  Dataset EGAD00001000218

• UK10K_RARE_HYPERCHOL REL-2012-11-27

  UK10K_RARE_HYPERCHOL REL-2012-11-27

  Dataset EGAD00001000295

• UK10K_RARE_CILIOPATHIES REL-2012-11-27

  UK10K_RARE_CILIOPATHIES REL-2012-11-27

  Dataset EGAD00001000296

• UK10K_RARE_SIR REL-2012-11-27

  UK10K_RARE_SIR REL-2012-11-27

  Dataset EGAD00001000334

• UK10K_OBESITY_SCOOP REL-2012-11-27

  UK10K_OBESITY_SCOOP REL-2012-11-27

  Dataset EGAD00001000336

• UK10K_RARE_THYROID REL-2012-01-13

  UK10K_RARE_THYROID REL-2012-01-13

  Dataset EGAD00001000152

• UK10K_RARE_CHD REL-2012-01-13

  UK10K_RARE_CHD REL-2012-01-13

  Dataset EGAD00001000178

• UK10K_RARE_THYROID REL-2012-02-22

  UK10K_RARE_THYROID REL-2012-02-22

  Dataset EGAD00001000187

• UK10K_RARE_CHD REL-2012-02-22

  UK10K_RARE_CHD REL-2012-02-22

  Dataset EGAD00001000192

• UK10K_RARE_THYROID REL-2012-07-05

  UK10K_RARE_THYROID REL-2012-07-05

  Dataset EGAD00001000208

• UK10K_RARE_CHD REL-2012-07-05

  UK10K_RARE_CHD REL-2012-07-05

  Dataset EGAD00001000210

• UK10K_RARE_CHD REL-2012-11-27

  UK10K_RARE_CHD REL-2012-11-27

  Dataset EGAD00001000294

• UK10K_RARE_THYROID REL-2012-11-27

  UK10K_RARE_THYROID REL-2012-11-27

  Dataset EGAD00001000329

• ESGI - Whole Genome Sequencing of NSPHS samples (2019-08-19)

  Whole genome sequencing of sampels from the NSPHS cohort. The samples are sequenced using the Illumina HiSeq X Ten system. . This dataset contains all the data available for this study on 2019-08-19.

  Dataset EGAD00001005267

• Epilepsy related sudden death (2019-08-21)

  These samples include exome sequences of samples from patients who suffered Sudden Unexplained Death in Epilepsy. They all are of European descent. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005276

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

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

  Dataset EGAD00001004953

• Wilms Tumour organoid sequencing WGS (2019-09-05)

  This study investigates the genomic and transcriptomic characteristics of Wilm's tumour organoids . This dataset contains all the data available for this study on 2019-09-05.

  Dataset EGAD00001005312

• UK10K_RARE_COLOBOMA REL-2012-01-13

  UK10K_RARE_COLOBOMA REL-2012-01-13

  Dataset EGAD00001000179

• UK10K_RARE_COLOBOMA REL-2012-02-22

  UK10K_RARE_COLOBOMA REL-2012-02-22

  Dataset EGAD00001000185

• UK10K_RARE_COLOBOMA REL-2012-07-05

  UK10K_RARE_COLOBOMA REL-2012-07-05

  Dataset EGAD00001000206

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• UK10K_RARE_COLOBOMA REL-2012-11-27

  UK10K_RARE_COLOBOMA REL-2012-11-27

  Dataset EGAD00001000307

• UK10K_RARE_FIND REL-2012-01-13

  UK10K_RARE_FIND REL-2012-01-13

  Dataset EGAD00001000171

• UK10K_RARE_FIND REL-2012-02-22

  UK10K_RARE_FIND REL-2012-02-22

  Dataset EGAD00001000190

• UK10K_RARE_FIND REL-2012-07-05

  UK10K_RARE_FIND REL-2012-07-05

  Dataset EGAD00001000209

• UK10K_RARE_FIND REL-2012-11-27

  UK10K_RARE_FIND REL-2012-11-27

  Dataset EGAD00001000297

• Low input LC (WGS) (2019-04-01)

  R&D project to develop low input library construction methods. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004878

• CRISPR screen M14, NCI-H3122 (2019-08-28)

  Genome wide CRISPR screen was performed to find resistance to targeted drugs for melanoma and lung . This dataset contains all the data available for this study on 2019-08-28.

  Dataset EGAD00001005296

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ESGI - Whole Genome Sequencing of samples from the Cilentogen isolates (2019-08-19)

  Deep whole genome sequencing of sampels from the Cilento isolates. The samples are sequenced using the Illumina HiSeq X Ten system. . This dataset contains all the data available for this study on 2019-08-19.

  Dataset EGAD00001005266

• Somatic mutations in twin breast cancers (2019-04-03)

  The aim of this study is to investigate the somatic mutations in twins with BRCA1/2 negative breast cancer with no strong family history. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004890

• Exome sequencing of Non-syndromic Congenital Heart Defects (2019-04-24)

  Exome sequencing of ~1,800 patients with non-syndromic Congenical Heart Defects (CHD) to identify genes enriched for damaging rare variants that increase risk of CHD . This dataset contains all the data available for this study on 2019-04-24.

  Dataset EGAD00001004975

• Chromatin Profiling in Twins (2019-08-21)

  50 lymphoblastoid cell lines of adult female Twins which are part of the MuTHER study, processed with the FAIRE assay in order to generate maps of open chromatin. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005273

• University of Miami Study on Genetics of Autism and Related Disorders (AutismDisorders)

  Autism spectrum disorders (ASD) are neurodevelopmental in origin and characterized by impairments in reciprocal social interaction and communication accompanied by restricted and repetitive patterns of interest or behavior. With improved surveillance and a broadening of the diagnostic criteria, the most recent prevalence studies suggest that ASD may affect as many as 1/166 children in the US. Treatments are limited resulting in little impact on the profound morbidity. Little is known about the etiology of ASD, but there is a strong genetic component. Genetic studies over the past decade have failed to identify definitive ASD genes, but have clearly shown that the underlying genetics are more complex than anticipated with the likelihood that multiple genes are acting independently and/or interactively. With this realization the field of ASD genetics is at a critical juncture. To move forward we must embrace new and creative paradigms to successfully dissect the genetic etiology of this disease. Specifically the current study will: Extend our ascertainment to the full range of ASD. We will increase our ASD dataset by collecting 600 families who meet clinical diagnostic criteria for the three most common ASDs: autism (AUT), Asperger disorder (ASP) and Pervasive Developmental Disorder-NOS (PDD-NOS). Phenotype assessment will be expanded to include the Social Responsiveness Scale (SRS) and detailed seizure data. Ascertainment will focus on parent-child trios. Further characterize the ASD-associated variations in the Gamma-Aminobutyric Acid (GABA) receptor (GABR) subunits. We will complete our GABR subunit variant analysis, including coding, UTR, regulatory, and conserved intronic sequence, concentrating on GABRA4 and GABRB1. We will also investigate the role of variation in other GABR subunits. All AUT specific findings will be examined in the entire ASD dataset. Identify the chromosome 19p ASD gene. Multiple genome-wide screens have provided significant evidence for linkage to Chromosome 19p and we have found evidence for significant association in the linked families within an ~3 Mb region. We will genotype 768 SNPs spaced at ~4 kb to refine the candidate region and delineate specific candidate genes for further testing. We will screen the strongest associated candidate genes for additional variants and evaluate evidence for association in the entire ASD dataset (SA5). Develop and apply the Phenotypic Homogeneity Distinction (PHD) algorithm for identification of the phenotypic signature of linkage and/or association signals. The PHD algorithm identifies phenotypic covariates that discriminate a target sample subset (e.g. the positively linked families or families carrying the associated risk allele) from its complement. The resulting PHD discriminators will define a homogeneous subset from our newly-ascertained ASD dataset to use, as a confirmatory dataset and for localization within genes under study in our other aims. Test for gene-gene interactions. To fully explain the spectrum of autism risk, we will test for interactions between genetic variations associated with ASD (such as those in GABR subunit genes). We will build upon our experience using the MDR method and extend our analyses across biological pathways using the entire ASD data set.

  Study phs000436

• Genome-Wide Association Study of Lung Cancer Susceptibility in Never-Smoking Women in Asia

  To learn about the etiology of lung cancer among never-smoking women in Asia, we formed the Female Lung Cancer Consortium in Asia (FLCCA), which includes studies of lung cancer in Eastern Asia and conducted a GWAS. We analyzed a total of 5510 lung cancer cases and 4544 controls and identified 3 novel loci (Lan et al., 2012). Of these study subjects, 4922 lung cancer cases and 3959 controls are available for posting. Lan et al.., Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat Genet. 2012 Dec;44(12):1330-5. doi: 10.1038/ng.2456. Epub 2012 Nov 11. PubMed PMID: 23143601.

  Study phs000716

• Combination Therapies for Personalised Cancer Medicine in 11-18 (2019-06-10)

  This study involves mutagenizing 11-18 with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005081

• Chromatin Profiling in Twins (2019-08-21)

  50 lymphoblastoid cell lines of adult female Twins which are part of the MuTHER study, subjected to Chromatin Immunoprecipitation using an antibody for H3K4me1. H3K4me1 peaks mark distal regulatory elements. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005274

• Exome sequencing of Non-syndromic Congenital Heart Defects (2019-04-24)

  Exome sequencing of ~1,800 patients with non-syndromic Congenical Heart Defects (CHD) to identify genes enriched for damaging rare variants that increase risk of CHD . This dataset contains all the data available for this study on 2019-04-24.

  Dataset EGAD00001004972

• Benchmarking CRISPR Whole-genome Drop-out Screen - B&S (2019-08-07)

  This study is a benchmarking exercise to explore potential source of variation between different CRISPR drop out libraries. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005233

• Role of HPV and Interferon in APOBEC mutational signature (2019-04-11)

  The aim of this project is to test whether HPV oncogenes and /or interferon induce the APOBEC mutational signature in vitro, and to test the role of APOBEC3A in this process. . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004955