The Causal Pivot: A structural approach to genetic heterogeneity and variant discovery in complex diseases.

Chad A Shaw, C J Williams, Taotao Tan, Daniel Illera, Nicholas Di, Joshua M Shulman, John W Belmont

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Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Chad A ShawDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Department of Statistics, Rice University, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA. Electronic address: cashaw@bcm.edu.

C J WilliamsGenetics & Genomics Services, Inc., Houston, TX, USA.

Taotao TanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA.

Daniel IlleraDepartment of Statistics, Rice University, Houston, TX, USA.

Nicholas DiDepartment of Statistics, Rice University, Houston, TX, USA.

Joshua M ShulmanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA.

John W BelmontDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Genetics & Genomics Services, Inc., Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages an established causal factor or factors to detect the contribution of additional suspected causes. Specifically, polygenic risk scores (PRSs) serve as known causes, while rare variants (RVs) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum-likelihood procedure for binary and quantitative traits and develop the Causal Pivot likelihood ratio test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRT's robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) data, analyzing three exemplar diseases: hypercholesterolemia (HC, low-density lipoprotein cholesterol ≥4.9 mmol/L; n

Indexed as

Breast NeoplasmsGenetic HeterogeneityGenetic Predisposition to DiseaseGenetic VariationModels, GeneticParkinson DiseaseFemaleGenome-Wide Association StudyHumansLikelihood FunctionsMultifactorial Inheritancecausal inferencecollidercomplex diseasegenetic heterogeneitypolygenic risk scorerare variation

Identifiers

PMID40829599

DOI10.1016/j.ajhg.2025.07.012

PMCPMC12461002

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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.