Evidence map›Paper›PMID 35043453›Full record

ArticleGenetic epidemiology2022

Genome-wide association studies of 27 accelerometry-derived physical activity measurements identified novel loci and genetic mechanisms.

Guanghao Qi, Diptavo Dutta, Andrew Leroux, Debashree Ray, John Muschelli, Ciprian Crainiceanu, Nilanjan Chatterjee

Open access · greenAbstract read
In one paragraph

Article in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
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

12 citing papers in PubMed, 28 citations in OpenAlex.

  1. Observational
  2. Article
  3. Contextualizing molecular and structural aging across human organs.medRxiv : the preprint server for health sciences · 2025
    Article
  4. Review
  5. Article
  6. Article
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  8. Article
  9. Review
  10. Article
  11. Article
  12. Can genetics guide exercise prescriptions in osteoarthritis?Frontiers in rehabilitation sciences · 2022
    Article
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

7 authors at 1 institution in 1 country.

Guanghao QiDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-8085-4748
Diptavo DuttaDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Andrew LerouxDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Debashree RayDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-0979-2935
John MuschelliDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Ciprian CrainiceanuDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Johns Hopkins University · US

Funding

Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk PredictionR01HG010480 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2019 to 2023
$2.8M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010480
6 · The paper itself

Abstract

Physical inactivity (PA) is an important risk factor for a wide range of diseases. Previous genome-wide association studies (GWAS), based on self-reported data or a small number of phenotypes derived from accelerometry, have identified a limited number of genetic loci associated with habitual PA and provided evidence for involvement of central nervous system in mediating genetic effects. In this study, we derived 27 PA phenotypes from wrist accelerometry data obtained from 88,411 UK Biobank study participants. Single-variant association analysis based on mixed-effects models and transcriptome-wide association studies (TWAS) together identified 5 novel loci that were not detected by previous studies of PA, sleep duration and self-reported chronotype. For both novel and previously known loci, we discovered associations with novel phenotypes including active-to-sedentary transition probability, light-intensity PA, activity during different times of the day and proxy phenotypes to sleep and circadian patterns. Follow-up studies including TWAS, colocalization, tissue-specific heritability enrichment, gene-set enrichment and genetic correlation analyses indicated the role of the blood and immune system in modulating the genetic effects and a secondary role of the digestive and endocrine systems. Our findings provided important insights into the genetic architecture of PA and its underlying mechanisms.

Indexed as

Genome-Wide Association StudyModels, GeneticAccelerometryExerciseGenetic LociGenetic Predisposition to DiseaseHumansblood and immune mechanismsgenome-wide association studiesnovel lociphysical activity

Identifiers

PMID35043453
PMCPMC8863635
OpenAlexW4205120811

What OpenQuestion holds

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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.