Evidence map›Paper›PMID 33587031›Full record

ArticleeLife2021

GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background.

Nasa Sinnott-Armstrong, Sahin Naqvi, Manuel Rivas, Jonathan K Pritchard

Abstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed, 4 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

104 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Principled measures and estimates of trait polygenicity.American journal of human genetics · 2026
    Article
  8. Article
  9. Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026
    Article
  18. Review
  19. Article
  20. Article

44 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.

Nasa Sinnott-Armstrong *Department of Genetics, Stanford University, Stanford, United States.ORCID 0000-0003-4490-0601
Sahin Naqvi *Department of Genetics, Stanford University, Stanford, United States.ORCID 0000-0003-2635-7967
Manuel RivasDepartment of Biomedical Data Sciences, Stanford University, Stanford, United States.
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, United States.ORCID 0000-0002-8828-5236

Funding

Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
Decoding the regulatory architecture of the human genome across cell types, individuals and diseaseU01HG009431 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2017 to 2021
$3.5M
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASESR01HG010140 · NHGRI · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2018 to 2022
$2.0M
NHGRI NIH HHS R01 HG008140NHGRI NIH HHS R01 HG010140NHGRI NIH HHS U01 HG009080NHGRI NIH HHS U01 HG009431
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

Indexed as

Genome-Wide Association StudyMultifactorial InheritanceDatabases, GeneticFemaleHumansInsulin-Like Growth Factor IMalePolymorphism, Single NucleotideQuantitative Trait LociSex FactorsTestosteroneUnited KingdomUric AcidWhite PeopleIGF1 protein, humanInsulin-Like Growth Factor ITestosteroneUric Acidbiomarkerscomplex traitsgeneticsgenome-wide association studygenomicshumanigf-1testosteroneurate

Identifiers

PMID33587031
PMCPMC7884075

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.