Evidence map›Paper›PMID 41530440›Full record

ReviewNature genetics2026

The predicament of heritable confounders.

Na Cai, Andy Dahl, Richard Border, Aditya Gorla, Jolien Rietkerk, Joel Mefford, Noah Zaitlen, Morten Dybdahl Krebs, Andrew J Schork, Kenneth Kendler and 1 more

Abstract readReview
In one paragraph

Review in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Na Cai *Department of Biosystems and Engineering, ETH Zürich, Basel, Switzerland.
Andy Dahl *Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
Richard BorderDepartment of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID 0000-0002-6293-2968
Aditya GorlaDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0003-0849-7894
Jolien RietkerkInstitute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA.ORCID 0000-0002-7539-787X
Joel MeffordInstitute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark.
Noah ZaitlenDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Morten Dybdahl KrebsInstitute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark.ORCID 0000-0002-4452-0732
Andrew J SchorkInstitute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark.ORCID 0000-0003-4164-9335
Kenneth KendlerVirginia Institute of Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.ORCID 0000-0001-8689-6570
Jonathan FlintDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. jflint@mednet.ucla.edu.ORCID 0000-0002-9427-4429

Funding

Improving the interpretability of genetic studies of major depressive disorder to identify risk genesR01MH130581 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JONATHAN FLINT, KENNETH SEEDMAN KENDLER · 2022 to 2026
$2.8M
NIMH NIH HHS R01 MH130581U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH130581
6 · The paper itself

Abstract

Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaires and data from electronic health records, incorporate heritable variation unrelated to the disease of interest into the diagnosis. Consequently, genetic mapping will identify loci unrelated to the target disease while missing some that are related, and genetic correlations cannot be used to infer the genetic relationships between diseases and between cohorts. Furthermore, shared biases between different disorders appear as shared etiology. As sample sizes grow, such confounders propagate, and findings based on their presence are replicated and extended. Here, we draw attention to the problem, make suggestions for flagging affected cohorts, and discuss future data collection and machine learning approaches to mitigate the effects of heritable confounders in psychiatric disorders.

Indexed as

Genetic Predisposition to DiseaseMental DisordersGenome-Wide Association StudyHumans

Identifiers

PMID41530440
PMCPMC13000753

What Socratic holds

Textmetadata
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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.