Broad Institute

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.

Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals

Abstract: There is growing recognition that mammalian cells produce many thousands of large intergenic transcripts. However, the functional significance of these transcripts has been particularly controversial. Although there are some well-characterized examples, most (>95%) show little evidence of evolutionary conservation and have been suggested to represent transcriptional noise. Here we report a new approach to identifying large non-coding RNAs using chromatin-state maps to discover discrete transcriptional units intervening known protein-coding loci. Our approach identified ~1,600 large multi-exonic RNAs across four mouse cell types. In sharp contrast to previous collections, these large intervening non-coding RNAs (lincRNAs) show strong purifying selection in their genomic loci, exonic sequences and promoter regions, with greater than 95% showing clear evolutionary conservation. We also developed a functional genomics approach that assigns putative functions to each lincRNA, demonstrating a diverse range of roles for lincRNAs in processes from embryonic stem cell pluripotency to cell proliferation. We obtained independent functional validation for the predictions for over 100 lincRNAs, using cell-based assays. In particular, we demonstrate that specific lincRNAs are transcriptionally regulated by key transcription factors in these processes such as p53, NFκB, Sox2, Oct4 (also known as Pou5f1) and Nanog. Together, these results define a unique collection of functional lincRNAs that are highly conserved and implicated in diverse biological processes.

Improved vectors and genome-wide libraries for CRISPR screening.

Abstract: Genome-wide, targeted loss-of-function pooled screens using the CRISPR (clustered regularly interspaced short palindrome repeats)–associated nuclease Cas9 in human and mouse cells provide an alternative screening system to RNA interference (RNAi) and have been used to reveal new mechanisms in diverse biological models1-4. Previously, we used a Genome-scale CRISPR Knock-Out (GeCKO) library to identify loss-of-function mutations conferring vemurafenib resistance in a melanoma model1. However, initial lentiviral delivery systems for CRISPR screening had low viral titer or required a cell line already expressing Cas9, limiting the range of biological systems amenable to screening. Here, we sought to improve both the lentiviral packaging and choice of guide sequences in our original GeCKO library1, where a pooled library of synthesized oligonucleotides was cloned into a lentiviral backbone containing both the Streptococcus pyogenes Cas9 nuclease and the single guide RNA (sgRNA) scaffold. To create a new vector capable of producing higher-titer virus (lentiCRISPRv2), we made several modifications, including removal of one of the nuclear localization signals (NLS), human codon-optimization of the remaining NLS and P2A bicistronic linker sequences, and repositioning of the U6-driven sgRNA cassette (Fig. 1a). These changes resulted in a ~10-fold increase in functional viral titer over lentiCRISPRv11 (Fig. 1b). Figure 1 New lentiviral CRISPR designs produce viruses with higher functional titer. To further increase viral titer, we also cloned a two-vector system, in which Cas9 (lentiCas9-Blast) and sgRNA (lentiGuide-Puro) are delivered using separate viral vectors with distinct antibiotic selection markers (Fig. 1a). LentiGuide-Puro has a ~100-fold increase in functional viral titer over the original lentiCRISPRv1 (Fig. 1b). Both single and dual-vector systems mediate efficient knock-out of a genomically-integrated copy of EGFP in human cells (Supplementary Fig. 1). Whereas the dual vector system enables generation of Cas9-expressing cell lines which can be subsequently used for screens using lentiGuide-Puro, the single vector lentiCRISPRv2 may be better suited for in vivo or primary cell screening applications. In addition to the vector improvements, we designed and synthesized new human and mouse GeCKOv2 sgRNA libraries (Supplementary Methods) with several improvements (Table 1): First, for both human and mouse libraries, to target all genes with a uniform number of sgRNAs, we selected 6 sgRNAs per gene distributed over 3-4 constitutively expressed exons. Second, to further minimize off-target genome modification, we improved the calculation of off-target scores based on specificity analysis5. Third, to inactivate microRNAs (miRNAs) which play a key role in transcriptional regulation, we added sgRNAs to direct mutations to the pre-miRNA hairpin structure6. Finally, we targeted ~1000 additional genes not included in the original GeCKO library. Table 1 Comparison of new GeCKO v2 human and mouse sgRNA libraries with existing CRISPR libraries. Both libraries, mouse and human, are divided into 2 sub-libraries — containing 3 sgRNAs targeting each gene in the genome, as well as 1000 non-targeting control sgRNAs. Screens can be performed by combining both sub-libraries, yielding 6 sgRNAs per gene, for higher coverage. Alternatively, individual sub-libraries can be used in situations where cell numbers are limiting (eg. primary cells, in vivo screens). The human and mouse libraries have been cloned into lentiCRISPRv2 and into lentiGuide-Puro and deep sequenced to ensure uniform representation (Supplementary Fig. 2, 3). These new lentiCRISPR vectors and human and mouse libraries further improve the GeCKO reagents for diverse screening applications. Reagents are available to the academic community through Addgene and associated protocols, support forums, and computational tools are available via the Zhang lab website (www.genome-engineering.org).

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

Abstract: The MuTect algorithm for calling somatic point mutations enables subclonal analysis of the whole-genome or whole-exome sequencing data being generated in large-scale cancer genomics projects.

Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease

Abstract: Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.

LD score regression distinguishes confounding from polygenicity in genome-wide association studies :

Abstract: Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.