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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Howard Hughes Medical Institute1, Broad Institute2, Harvard University3, University of California, Berkeley4, University of California, Los Angeles5, Chinese Academy of Sciences6, Max Planck Society7, Columbia University8, Massachusetts Institute of Technology9, Cayetano Heredia University10, University of Pennsylvania11, University College London12, University of Bern13, Leiden University14, Nanyang Technological University15, University of Chicago16, Estonian Biocentre17, National University of La Plata18, University of Oxford19, University of Bergen20, Novosibirsk State University21, Moscow Institute of Physics and Technology22, Sofia Medical University23, Armenian National Academy of Sciences24, Wellcome Trust Sanger Institute25, Raja Isteri Pengiran Anak Saleha Hospital26, Case Western Reserve University27, University of Tartu28, Estonian Academy of Sciences29, Stony Brook University30, Illumina31, Gladstone Institutes32, University of Helsinki33, University of Washington34, Bashkir State University35, Jaramogi Oginga Odinga University of Science and Technology36, Pompeu Fabra University37, University of Arizona38, University of Cambridge39, Leidos40, Université de Montréal41, University of Utah42, Altai State University43, Council of Scientific and Industrial Research44
TL;DR: It is demonstrated that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.
Abstract: Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.
1,133 citations
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TL;DR: Recurrent somatic mutations in PIK3CA, TP53, AKT1, GATA3 and MAP3K1 are confirmed and a recurrent MAGI3–AKT3 fusion enriched in triple-negative breast cancer lacking oestrogen and progesterone receptors and ERBB2 expression is identified.
Abstract: Breast carcinoma is the leading cause of cancer-related mortality in women worldwide, with an estimated 1.38 million new cases and 458,000 deaths in 2008 alone. This malignancy represents a heterogeneous group of tumours with characteristic molecular features, prognosis and responses to available therapy. Recurrent somatic alterations in breast cancer have been described, including mutations and copy number alterations, notably ERBB2 amplifications, the first successful therapy target defined by a genomic aberration. Previous DNA sequencing studies of breast cancer genomes have revealed additional candidate mutations and gene rearrangements. Here we report the whole-exome sequences of DNA from 103 human breast cancers of diverse subtypes from patients in Mexico and Vietnam compared to matched-normal DNA, together with whole-genome sequences of 22 breast cancer/normal pairs. Beyond confirming recurrent somatic mutations in PIK3CA, TP53, AKT1, GATA3 and MAP3K1, we discovered recurrent mutations in the CBFB transcription factor gene and deletions of its partner RUNX1. Furthermore, we have identified a recurrent MAGI3-AKT3 fusion enriched in triple-negative breast cancer lacking oestrogen and progesterone receptors and ERBB2 expression. The MAGI3-AKT3 fusion leads to constitutive activation of AKT kinase, which is abolished by treatment with an ATP-competitive AKT small-molecule inhibitor.
1,132 citations
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TL;DR: Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples that are dominated by a relatively small number of genes, though few are exclusive to a particular tissue and vary more across tissues than individuals.
Abstract: Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.
1,131 citations
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TL;DR: Using an improved human mutation rate model, human protein-coding genes are classified along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.
Abstract: Summary Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organism’s function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved model of human mutation, we classify human protein-coding genes along a spectrum representing intolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.
1,128 citations
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Max Planck Society1, University of Marburg2, Broad Institute3, Trent University4, University of Toronto5, California State University, Long Beach6, University of British Columbia7, University of Georgia8, University of Louisville9, Utrecht University10, Saint Joseph's University11, Spanish National Research Council12, Cornell University13, CINVESTAV14, University of Kentucky15, Duke University16, University of Valencia17, Saint Louis University18, Bayer19, Exelixis20, Technische Universität München21
TL;DR: The discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi.
Abstract: Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.
1,120 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 |