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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Memorial Sloan Kettering Cancer Center1, Bilkent University2, SRI International3, Université libre de Bruxelles4, Ontario Institute for Cancer Research5, New York University6, National Institutes of Health7, National Autonomous University of Mexico8, Boston University9, Cold Spring Harbor Laboratory10, Johns Hopkins University11, University of Toronto12, Rothamsted Research13, University of Rennes14, Cell Signaling Technology15, Broad Institute16, Food and Drug Administration17, Virginia Tech18, Oregon Health & Science University19, United States Environmental Protection Agency20, Argonne National Laboratory21, University of Connecticut22, Harvard University23, National Institute of Standards and Technology24, University of Cambridge25, Konrad Lorenz Institute for Evolution and Cognition Research26, National University of Ireland, Galway27, Maastricht University28, University of Auckland29, Syngenta30, Stanford University31, Yale University32, Loyola Marymount University33, St. John's University34, Columbia University35, SRA International36, Novartis37, University of Ottawa38, Vertex Pharmaceuticals39, Medical College of Wisconsin40, Gladstone Institutes41, Cornell University42, Takeda Pharmaceutical Company43, University of Chicago44, Total S.A.45, Kyoto University46, California Institute of Technology47
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Abstract: Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
673 citations
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TL;DR: PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes, is reported, which measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely-related subspecies, and improves consistency between phylogenetic and taxonomic groupings.
Abstract: New microbial genomes are constantly being sequenced, and it is crucial to accurately determine their taxonomic identities and evolutionary relationships. Here we report PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes. This method measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely related subspecies and improves consistency between phylogenetic and taxonomic groupings. PhyloPhlAn improved taxonomic accuracy for existing and newly sequenced genomes, detecting 157 erroneous labels, correcting 46 and placing or refining 130 new genomes. We provide examples of accurate classifications from subspecies (Sulfolobus spp.) to phyla, and of preliminary rooting of deep-branching candidate divisions, including consistent statistical support for Caldiserica (formerly candidate division OP5). PhyloPhlAn will thus be useful for both phylogenetic assessment and taxonomic quality control of newly sequenced genomes. The final phylogenies, conserved protein sequences and open-source implementation are available online.
672 citations
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TL;DR: How a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project is described.
Abstract: The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.
669 citations
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TL;DR: Seven prostate cancer risk variants are identified, five of them previously undescribed, spanning 430 kb and each independently predicting risk for prostate cancer (P = 7.9 × 10−19 for the strongest association), and common genotypes that span a more than fivefold range of susceptibility to cancer in some populations are defined.
Abstract: After the recent discovery that common genetic variation in 8q24 influences inherited risk of prostate cancer, we genotyped 2,973 SNPs in up to 7,518 men with and without prostate cancer from five populations. We identified seven risk variants, five of them previously undescribed, spanning 430 kb and each independently predicting risk for prostate cancer (P = 7.9 x 10(-19) for the strongest association, and P < 1.5 x 10(-4) for five of the variants, after controlling for each of the others). The variants define common genotypes that span a more than fivefold range of susceptibility to cancer in some populations. None of the prostate cancer risk variants aligns to a known gene or alters the coding sequence of an encoded protein.
668 citations
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TL;DR: The first calibrated population genetic model is presented and it is shown that, while still arbitrary, it successfully generates simulated data that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation.
Abstract: Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.
667 citations
Authors
Showing all 7146 results
Name | H-index | Papers | Citations |
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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 |