Evidence mapPaperPMID 30951530Full record

ArticlePLoS genetics2019

Reverse GWAS: Using genetics to identify and model phenotypic subtypes.

Andy Dahl, Na Cai, Arthur Ko, Markku Laakso, Päivi Pajukanta, Jonathan Flint, Noah Zaitlen

Open access · goldFull text readValidation Study
In one paragraph

Article in PLoS genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
6.1field-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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026
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  10. Review
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  14. Review
  15. Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021
    Article
  16. Article
  17. Polygenic risk modeling with latent trait-related genetic components.European journal of human genetics : EJHG · 2021
    Article
  18. Article
  19. A model and test for coordinated polygenic epistasis in complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2021
    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

7 authors at 4 institutions in 3 countries.

Andy DahlDepartment of Medicine, UCSF, San Francisco, California, United States of America.ORCID 0000-0001-6520-4766
Na CaiWellcome Sanger Institute, Cambridge, United Kingdom.ORCID 0000-0001-7496-2075
Arthur KoDepartment of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-1523-7225
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.
Päivi PajukantaDepartment of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America.
Jonathan FlintCenter for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, United States of America.
Noah ZaitlenDepartment of Medicine, UCSF, San Francisco, California, United States of America.
University of California, Los Angeles · USUniversity of California, San Francisco · USEuropean Bioinformatics Institute · GBUniversity of Eastern Finland · FI

Funding

MUTATIONS AFFECTING LIPOPROTEIN METABOLISMP01HL028481 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 1985 to 2005
$13.6M
NHGRI NIH HHS R01 HG006399NHGRI NIH HHS U01 HG009080NHLBI NIH HHS F31 HL127921NHLBI NIH HHS K25 HL121295NHLBI NIH HHS P01 HL028481NHLBI NIH HHS R01 HL095056NIDCR NIH HHS R03 DE025665NIDDK NIH HHS U01 DK105561
6 · The paper itself

Abstract

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.

Indexed as

Models, GeneticMultifactorial InheritancePhenotypeAlgorithmsBlood GlucoseCluster AnalysisComputer SimulationCoronary DiseaseDiabetes Mellitus, Type 2Genome-Wide Association StudyHumansHydroxymethylglutaryl-CoA Reductase InhibitorsLipidsMajor Depressive DisorderPolymorphism, Single NucleotidePrediabetic StateBlood GlucoseHydroxymethylglutaryl-CoA Reductase InhibitorsLipids

Identifiers

PMID30951530
PMCPMC6469799
OpenAlexW2897912114

What Socratic holds

Textfull text, public
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 Socratic graph.