Evidence map›Paper›PMID 29632380›Full record

ArticleNature genetics2018

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.

Hilary K Finucane, Yakir A Reshef, Verneri Anttila, Kamil Slowikowski, Alexander Gusev, Andrea Byrnes, Steven Gazal, Po-Ru Loh, Caleb Lareau, Noam Shoresh and 12 more

Abstract readValidation Study
In one paragraph

Article in Nature genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 732 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
732citing papers in PubMed, 18 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

732 citing papers in PubMed, 18 syntheses or guidelines pooled it.

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672 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors.

Hilary K FinucaneBroad Institute of MIT and Harvard, Cambridge, MA, USA. finucane@broadinstitute.org.ORCID http://orcid.org/0000-0003-3864-9828
Yakir A ReshefDepartment of Computer Science, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-6463-4203
Verneri AnttilaBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Kamil SlowikowskiBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-2843-6370
Alexander GusevDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7980-4620
Andrea ByrnesBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Steven GazalDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4510-5730
Po-Ru LohDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Caleb LareauBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Noam ShoreshBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Giulio GenoveseBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-3066-5575
Arpiar SaundersDepartment of Genetics, Harvard Medical School, Boston, MA, USA.
Evan MacoskoDepartment of Genetics, Harvard Medical School, Boston, MA, USA.
Samuela PollackDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Brainstorm Consortium
John R B PerryMedical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.
Jason D BuenrostroBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Bradley E BernsteinBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Soumya RaychaudhuriBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Steven McCarrollBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Benjamin M NealeBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-1513-6077
Alkes L PriceBroad Institute of MIT and Harvard, Cambridge, MA, USA. aprice@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-2971-7975

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Functional Genomics of Human Brain DevelopmentP50MH106934 · NIMH · YALE UNIVERSITY · PI SESTAN, NENAD · 2014 to 2019
$7.9M
Cis-Regulatory Epigenome Mappings in SchizophreniaU01MH103392 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AKBARIAN, SCHAHRAM, SKLAR, PAMELA · 2014 to 2017
$7.6M
Transcriptional and Epigenetic Signatures of Human Brain Development and AutismU01MH103339 · NIMH · YALE UNIVERSITY · PI SESTAN, NENAD, STATE, MATTHEW W. · 2014 to 2017
$6.5M
Discovery and Functional Impact of Common and Rare Variants in RAR01AR063759 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Soumya Raychaudhuri · 2013 to 2026
$5.4M
Genetic variants affect brain gene expression and risks of psychiatric disordersU01MH103340 · NIMH · UPSTATE MEDICAL UNIVERSITY · PI LIU, CHUNYU, RZHETSKY, ANDREY · 2014 to 2017
$4.3M
RA-SLE Molecular Deconstruction Leadership CenterUH2AR067677 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI BRENNER, MICHAEL B., RAYCHAUDHURI, SOUMYA · 2014 to 2020
$4.3M
RNA Sequencing of the Limbic System in Bipolar DisorderR01MH105898 · NIMH · JOHNS HOPKINS UNIVERSITY · PI HYDE, THOMAS MICHAEL, ZANDI, PETER P. · 2015 to 2018
$3.1M
Quantifying and Characterizing the shared genetic contribution to common cancersU01CA194393 · NCI · UNIVERSITY OF WASHINGTON · PI KRAFT, PETER, LINDSTROEM, SARA · 2015 to 2023
$3.0M
Statistical methods to localize disease heritability and identify biological mechanismsR01MH107649 · NIMH · BROAD INSTITUTE, INC. · PI NEALE, BENJAMIN MICHAEL · 2015 to 2018
$2.9M
Decoding schizophrenia-From GWAS to functional regulatory variantsR01MH105472 · NIMH · DUKE UNIVERSITY · PI CRAWFORD, GREGORY E, SULLIVAN, PATRICK F · 2014 to 2017
$2.9M
Medical Research Council MC_UU_12015/2NCI NIH HHS R01 CA194393NCI NIH HHS U01 CA194393NHGRI NIH HHS T32 HG002295NHGRI NIH HHS U01 HG009379NIAMS NIH HHS R01 AR063759NIAMS NIH HHS UH2 AR067677NIGMS NIH HHS T32 GM007753NIMH NIH HHS P50 MH106934NIMH NIH HHS R01 MH094714NIMH NIH HHS R01 MH105472NIMH NIH HHS R01 MH105898NIMH NIH HHS R01 MH107649NIMH NIH HHS R01 MH109978NIMH NIH HHS R21 MH102791NIMH NIH HHS R21 MH103877NIMH NIH HHS R21 MH105881NIMH NIH HHS U01 MH103339NIMH NIH HHS U01 MH103340NIMH NIH HHS U01 MH103346NIMH NIH HHS U01 MH103365NIMH NIH HHS U01 MH103392
6 · The paper itself

Abstract

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.

Indexed as

Gene ExpressionGenetic Predisposition to DiseaseBipolar DisorderBody Mass IndexBrainChromatinEpigenesis, GeneticGene Expression ProfilingGenome-Wide Association StudyHumansImmune System DiseasesLinkage DisequilibriumModels, GeneticMultifactorial InheritanceNeuronsSchizophreniaChromatin

Identifiers

PMID29632380
PMCPMC5896795

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.