Evidence map›Paper›PMID 34848700›Full record

ArticleNature communications2021

Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.

Marion Patxot, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Alexander Holloway, Julia Sidorenko, Zoltan Kutalik, Reedik Mägi and 3 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
6.4field-weighted citation impact, top 3% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats.Animals : an open access journal from MDPI · 2025
    Article
  8. Quantitative omnigenic model discovers interpretable genome-wide associations.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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  19. Improving GWAS discovery and genomic prediction accuracy in biobank data.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  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

13 authors at 6 institutions in 5 countries.

Marion Patxot *Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0003-0681-7786
Daniel Trejo Banos *Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-5268-5759
Athanasios Kousathanas *Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-6265-6521
Etienne J OrliacScientific Computing and Research Support Unit, University of Lausanne, Lausanne, Switzerland.
Sven E OjaveeDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-7433-7787
Gerhard MoserAustralian Agricultural Company Limited, Brisbane, QLD, Australia.ORCID 0000-0003-3104-5730
Alexander HollowayDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Julia SidorenkoInstitute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0003-1494-6772
Zoltan KutalikDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-8285-7523
Reedik MägiEstonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
Peter M VisscherInstitute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0002-2143-8760
Lars RönnegårdSchool of Technology and Business Studies, Dalarna University, Falun, Sweden.ORCID 0000-0002-1057-5401
Matthew R RobinsonInstitute of Science and Technology Austria, Klosterneuburg, Austria. matthew.robinson@ist.ac.at.ORCID 0000-0001-8982-8813
University of Lausanne · CHThe University of Queensland · AUDalarna University · SEInstitute of Science and Technology Austria · ATSIB Swiss Institute of Bioinformatics · CHUniversity of Tartu · EE

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

Indexed as

Genome-Wide Association StudyGenomicsBayes TheoremBody HeightBody Mass IndexCardiovascular DiseasesDiabetes Mellitus, Type 2Genetic TechniquesGenetic VariationGenotypeHumansIntronsModels, StatisticalMultifactorial InheritanceOpen Reading FramesPhenotype

Identifiers

PMID34848700
PMCPMC8633298
OpenAlexW3215565758

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.