Evidence map›Paper›PMID 32424349›Full record

ArticleNature genetics2020

Quantifying genetic effects on disease mediated by assayed gene expression levels.

Douglas W Yao, Luke J O'Connor, Alkes L Price, Alexander Gusev

Abstract read
In one paragraph

Article in Nature genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
214citing papers in PubMed, 5 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

214 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Where risk resides.Nature immunology · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Douglas W YaoProgram in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA. douglasyao@g.harvard.edu.ORCID http://orcid.org/0000-0002-0939-4670
Luke J O'ConnorProgram in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2730-9668
Alkes L PriceDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2971-7975
Alexander GusevProgram in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. alexander_gusev@dfci.harvard.edu.ORCID http://orcid.org/0000-0002-7980-4620

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Statistical methods for studies of rare variantsR01MH101244 · NIMH · HARVARD MEDICAL SCHOOL · PI Benjamin Michael Neale, ALKES L PRICE · 2013 to 2026
$9.4M
Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
Joint genomic and statistical analyses of schizophrenia and bipolar to decipher genetic susceptibilityR01MH115676 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Roel A Ophoff, Bogdan Pasaniuc · 2018 to 2026
$5.9M
(PQ3) A functional genomic approach to identification and interpretation of germline-tumor genetic interactionsR01CA227237 · NCI · DANA-FARBER CANCER INST · PI GUSEV, ALEXANDER, ZAITLEN, NOAH A · 2018 to 2022
$3.5M
Statistical methods to localize disease heritability and identify biological mechanismsR01MH107649 · NIMH · BROAD INSTITUTE, INC. · PI NEALE, BENJAMIN MICHAEL · 2015 to 2018
$2.9M
Functionally specialized components of disease heritability in ENCODE dataU01HG009379 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L, RAYCHAUDHURI, SOUMYA · 2017 to 2021
$2.5M
NCI NIH HHS R01 CA227237NHGRI NIH HHS R01 HG006399NHGRI NIH HHS T32 HG002295NHGRI NIH HHS U01 HG009379NIMH NIH HHS R01 MH101244NIMH NIH HHS R01 MH107649NIMH NIH HHS R01 MH115676
6 · The paper itself

Abstract

Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels 'mediating' genetic effects on disease. Here, we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis genetic component of gene expression levels. We applied MESC to GWAS summary statistics for 42 traits (average N = 323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11 ± 2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk tissue eQTLs, although disease relevant, cannot explain the majority of disease heritability.

Indexed as

Gene ExpressionGenetic Predisposition to DiseaseQuantitative Trait LociCalibrationGenome-Wide Association StudyHumansLinkage DisequilibriumPolymorphism, Single NucleotideRegression Analysis

Identifiers

PMID32424349
PMCPMC7276299

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.