Evidence map›Paper›PMID 36535946›Full record

ArticleNature communications2022

A comparison of the genes and genesets identified by GWAS and EWAS of fifteen complex traits.

Thomas Battram, Tom R Gaunt, Caroline L Relton, Nicholas J Timpson, Gibran Hemani

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Article
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  3. Review
  4. Article
  5. Review
  6. Interaction of identity and beliefs with genetic literacy.American journal of human genetics · 2026
    Article
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  8. Article
  9. Article
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  11. Review
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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

5 authors.

Thomas BattramMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. thomas.battram@bristol.ac.uk.ORCID 0000-0001-5769-6158
Tom R GauntMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID 0000-0003-0924-3247
Caroline L ReltonMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID 0000-0003-2052-4840
Nicholas J TimpsonMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID 0000-0002-7141-9189
Gibran HemaniMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID 0000-0003-0920-1055

Funding

Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_00011/4Wellcome Trust 203746Wellcome Trust 208806/Z/17/Z
6 · The paper itself

Abstract

Identifying genomic regions pertinent to complex traits is a common goal of genome-wide and epigenome-wide association studies (GWAS and EWAS). GWAS identify causal genetic variants, directly or via linkage disequilibrium, and EWAS identify variation in DNA methylation associated with a trait. While GWAS in principle will only detect variants due to causal genes, EWAS can also identify genes via confounding, or reverse causation. We systematically compare GWAS (N > 50,000) and EWAS (N > 4500) results of 15 complex traits. We evaluate if the genes or gene ontology terms flagged by GWAS and EWAS overlap, and find substantial overlap for diastolic blood pressure, (gene overlap P = 5.2 × 10

Indexed as

EpigenomeGenome-Wide Association StudyDNA MethylationEpigenesis, GeneticMultifactorial Inheritance

Identifiers

PMID36535946
PMCPMC9763500

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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.