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Institution

Broad Institute

NonprofitCambridge, 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.


Papers
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Journal ArticleDOI
TL;DR: It is demonstrated that ATG16L1 is expressed in intestinal epithelial cell lines and that functional knockdown of this gene abrogates autophagy of Salmonella typhimurium, and these findings suggest thatAutophagy and host cell responses to intracellular microbes are involved in the pathogenesis of Crohn disease.
Abstract: We present a genome-wide association study of ileal Crohn disease and two independent replication studies that identify several new regions of association to Crohn disease. Specifically, in addition to the previously established CARD15 and IL23R associations, we identified strong and significantly replicated associations (combined P < 10(-10)) with an intergenic region on 10q21.1 and a coding variant in ATG16L1, the latter of which was also recently reported by another group. We also report strong associations with independent replication to variation in the genomic regions encoding PHOX2B, NCF4 and a predicted gene on 16q24.1 (FAM92B). Finally, we demonstrate that ATG16L1 is expressed in intestinal epithelial cell lines and that functional knockdown of this gene abrogates autophagy of Salmonella typhimurium. Together, these findings suggest that autophagy and host cell responses to intracellular microbes are involved in the pathogenesis of Crohn disease.

1,766 citations

Journal ArticleDOI
01 Mar 2008-Genetics
TL;DR: A new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping and takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows for substantially increase the computational speed and reliability of the results.
Abstract: Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

1,765 citations

Journal ArticleDOI
TL;DR: A haplotype-based tagging method is demonstrated that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency, and is robust to the completeness of the reference panel from which tags are selected.
Abstract: We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.

1,765 citations

Journal ArticleDOI
24 Mar 2006-Cell
TL;DR: A screen based on high-content imaging was developed to identify genes required for mitotic progression in human cancer cells and applied to an arrayed set of 5,000 unique shRNA-expressing lentiviruses that target 1,028 human genes, providing a widely applicable resource for loss-of-function screens.

1,760 citations

Journal ArticleDOI
09 Apr 2015-Nature
TL;DR: Six smaller Cas9 orthologues are characterized and it is shown that Cas9 from Staphylococcus aureus (SaCas9) can edit the genome with efficiencies similar to those of SpCas9, while being more than 1 kilobase shorter.
Abstract: The RNA-guided endonuclease Cas9 has emerged as a versatile genome-editing platform. However, the size of the commonly used Cas9 from Streptococcus pyogenes (SpCas9) limits its utility for basic research and therapeutic applications that use the highly versatile adeno-associated virus (AAV) delivery vehicle. Here, we characterize six smaller Cas9 orthologues and show that Cas9 from Staphylococcus aureus (SaCas9) can edit the genome with efficiencies similar to those of SpCas9, while being more than 1 kilobase shorter. We packaged SaCas9 and its single guide RNA expression cassette into a single AAV vector and targeted the cholesterol regulatory gene Pcsk9 in the mouse liver. Within one week of injection, we observed >40% gene modification, accompanied by significant reductions in serum Pcsk9 and total cholesterol levels. We further assess the genome-wide targeting specificity of SaCas9 and SpCas9 using BLESS, and demonstrate that SaCas9-mediated in vivo genome editing has the potential to be efficient and specific.

1,756 citations


Authors

Showing all 7146 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Albert Hofman2672530321405
Frank B. Hu2501675253464
David J. Hunter2131836207050
Kari Stefansson206794174819
Mark J. Daly204763304452
Lewis C. Cantley196748169037
Matthew Meyerson194553243726
Gad Getz189520247560
Stacey Gabriel187383294284
Stuart H. Orkin186715112182
Ralph Weissleder1841160142508
Chris Sander178713233287
Michael I. Jordan1761016216204
Richard A. Young173520126642
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202337
2022627
20211,727
20201,534
20191,364
20181,107