Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH

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Abstract

Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed HCR-FlowFISH, a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene, and can display activating and/or silencing effects. At the cholesterol-level associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominating causal variants and importantly, identifying their target genes.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2017)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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Minimap2: pairwise alignment for nucleotide sequences

Heng Li (2018)

Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat Genet

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 9 March 2022

Publication date (Print): August 2021

Publication date (Electronic): 29 July 2021

Publication date PMC-release: 16 March 2022

Volume: 53

Issue: 8

Pages: 1166-1176

Affiliations

[1 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA.

[2 ]Center for System Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.

[3 ]Harvard Graduate Program in Biological and Biomedical Science, Boston, MA, USA.

[4 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.

[5 ]Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.

[6 ]Howard Hughes Medical Institute, Chevy Chase, MD, USA.

[7 ]The Jackson Laboratory, Bar Harbor, ME, USA.

[8 ]The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

[9 ]Department of Genetics and Genome Sciences, University of Connecticut, Farmington, CT, USA.

[10 ]Institute of Systems Genomics, University of Connecticut, Farmington, CT, USA.

[11 ]Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

[12 ]Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA.

[13 ]Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA.

[14 ]These authors contributed equally to this work.

Author notes

Author Contributions

S.K.R., S.J.G., and R.T. designed experiments. S.K.R., A.G., A.M.-S., K.M., G.M.B., A.G.-Y., D.B., S.K., R.M.B., M.L.S., and R.T. performed experiments. S.K.R., S.J.G., A.M.-S., and R.T. designed and performed data analysis. M.K., J.C.U., and H.K.F. performed fine-mapping analyses. S.K.R., S.J.G., A.G., A.M.-S., H.K.F., P.C.S. and R.T. contributed to the writing of the manuscript and interpretation of data.

[* ] sreilly@ 123456broadinstitute.org , pardis@ 123456broadinstitute.org , ryan.tewhey@ 123456jax.org

Article

Manuscript ID: NIHMS1723995

DOI: 10.1038/s41588-021-00900-4

PMC ID: 8925018

PubMed ID: 34326544

SO-VID: 6c37d503-a499-4591-b20d-601234bbd810

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This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH

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Genome Integrity

Most cited references 66

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

A global reference for human genetic variation

Minimap2: pairwise alignment for nucleotide sequences

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