Evidence map›Paper›PMID 31492854›Full record

ArticleNature communications2019

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.

Yosuke Tanigawa, Jiehan Li, Johanne M Justesen, Heiko Horn, Matthew Aguirre, Christopher DeBoever, Chris Chang, Balasubramanian Narasimhan, Kasper Lage, Trevor Hastie and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

60 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Colocalization and functional analyses identifyAmerican journal of physiology. Endocrinology and metabolism · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. 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

14 authors.

Yosuke TanigawaDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9759-157X
Jiehan LiDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
Johanne M JustesenDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Heiko HornDepartment of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4898-0557
Matthew AguirreDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9069-6475
Christopher DeBoeverDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Chris ChangGrail, Inc., Menlo Park, CA, USA.
Balasubramanian NarasimhanDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Kasper LageDepartment of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Trevor HastieDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Chong Y ParkDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
Gill BejeranoDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5179-3635
Erik IngelssonDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA. eriking@stanford.edu.ORCID http://orcid.org/0000-0003-2256-6972
Manuel A RivasDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA. mrivas@stanford.edu.ORCID http://orcid.org/0000-0003-1457-9925

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
Characterization of novel insulin resistance genes by gene editing, high-throughput phenotyping and in vivo studiesR01DK120565 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2019 to 2023
$3.2M
Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to functionR01DK106236 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2016 to 2020
$2.4M
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
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010140NHGRI NIH HHS U01 HG009080NIDDK NIH HHS R01 DK106236NIDDK NIH HHS R01 DK120565NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

Indexed as

Biological Specimen Banks3T3-L1 CellsAdipocytesAnimalsCells, CulturedCyclic Nucleotide Phosphodiesterases, Type 3Genetic Association StudiesGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMiceObesityPhenotypePolymorphism, Single NucleotideUnited KingdomCyclic Nucleotide Phosphodiesterases, Type 3PDE3B protein, human

Identifiers

PMID31492854
PMCPMC6731283

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.