Institution
Broad Institute
Nonprofit•Cambridge, Massachusetts, United States•
About: Broad Institute is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 6584 authors who have published 11618 publications receiving 1522743 citations. The organization is also known as: Eli and Edythe L. Broad Institute of MIT and Harvard.
Topics: Population, Genome-wide association study, Genome, Gene, Chromatin
Papers published on a yearly basis
Papers
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Lundbeck1, Aarhus University2, Broad Institute3, Harvard University4, Cardiff University5, Karolinska Institutet6, Statens Serum Institut7, QIMR Berghofer Medical Research Institute8, deCODE genetics9, University of Iceland10, Mental Health Services11, Charité12, Semel Institute for Neuroscience and Human Behavior13, University of California, Los Angeles14, University of Queensland15, Oslo University Hospital16, King's College London17, University of Toronto18, VU University Amsterdam19, Radboud University Nijmegen20, Veterans Health Administration21, Yale University22, Children's Hospital of Philadelphia23, University of Bergen24, Haukeland University Hospital25, University of Pennsylvania26, I.M. Sechenov First Moscow State Medical University27, University of Würzburg28, Maastricht University29, Goethe University Frankfurt30, Universidade Federal do Rio Grande do Sul31, Icahn School of Medicine at Mount Sinai32, University of North Carolina at Chapel Hill33, Emory University34, University of Copenhagen35, Aarhus University Hospital36, State University of New York Upstate Medical University37
TL;DR: A genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls identifies variants surpassing genome- wide significance in 12 independent loci and implicates neurodevelopmental pathways and conserved regions of the genome as being involved in underlying ADHD biology.
Abstract: Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.
1,436 citations
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TL;DR: Coexpression of EGFRvIII and PTEN by glioblastoma cells is associated with responsiveness to EGFR kinase inhibitors, and effects of the molecular abnormalities in vitro are identified.
Abstract: Background The epidermal growth factor receptor (EGFR) is frequently amplified, overexpressed, or mutated in glioblastomas, but only 10 to 20 percent of patients have a response to EGFR kinase inhibitors. The mechanism of responsiveness of glioblastomas to these inhibitors is unknown. Methods We sequenced kinase domains in the EGFR and human EGFR type 2 (Her2/neu) genes and analyzed the expression of EGFR, EGFR deletion mutant variant III (EGFRvIII), and the tumor-suppressor protein PTEN in recurrent malignant gliomas from patients who had received EGFR kinase inhibitors. We determined the molecular correlates of clinical response, validated them in an independent data set, and identified effects of the molecular abnormalities in vitro. Results Of 49 patients with recurrent malignant glioma who were treated with EGFR kinase inhibitors, 9 had tumor shrinkage of at least 25 percent. Pretreatment tissue was available for molecular analysis from 26 patients, 7 of whom had had a response and 19 of whom had rap...
1,433 citations
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TL;DR: In this article, the authors profiled transcriptomes of ∼6,000 single cells from 18 head and neck squamous cell carcinoma (HNSCC) patients, including five matched pairs of primary tumors and lymph node metastases.
1,425 citations
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TL;DR: In this paper, the authors performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data.
Abstract: Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
1,424 citations
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TL;DR: The intellectual foundations of genetic mapping of Mendelian and complex traits in humans are discussed, lessons emerging from linkage analysis of MendELian diseases and genome-wide association studies of common diseases are examined, and questions and challenges that lie ahead are discussed.
Abstract: Genetic mapping provides a powerful approach to identify genes and biological processes underlying any trait influenced by inheritance, including human diseases We discuss the intellectual foundations of genetic mapping of Mendelian and complex traits in humans, examine lessons emerging from linkage analysis of Mendelian diseases and genome-wide association studies of common diseases, and discuss questions and challenges that lie ahead
1,421 citations
Authors
Showing all 7146 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
Albert Hofman | 267 | 2530 | 321405 |
Frank B. Hu | 250 | 1675 | 253464 |
David J. Hunter | 213 | 1836 | 207050 |
Kari Stefansson | 206 | 794 | 174819 |
Mark J. Daly | 204 | 763 | 304452 |
Lewis C. Cantley | 196 | 748 | 169037 |
Matthew Meyerson | 194 | 553 | 243726 |
Gad Getz | 189 | 520 | 247560 |
Stacey Gabriel | 187 | 383 | 294284 |
Stuart H. Orkin | 186 | 715 | 112182 |
Ralph Weissleder | 184 | 1160 | 142508 |
Chris Sander | 178 | 713 | 233287 |
Michael I. Jordan | 176 | 1016 | 216204 |
Richard A. Young | 173 | 520 | 126642 |