Evidence map›Paper›PMID 41875896›Full record

ArticleAmerican journal of human genetics2026

Focus on single-gene effects limits discovery and interpretation of complex-trait-associated variants.

Kathryn A Lawrence, Tamara Gjorgjieva, Daniel Nachun, Stephen B Montgomery

Abstract read
In one paragraph

Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Kathryn A LawrenceDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: klawren@stanford.edu.
Tamara GjorgjievaDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Daniel NachunDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Stephen B MontgomeryDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: smontgom@stanford.edu.

Funding

INSTITUTIONAL TRAINING GRANT IN GENOME SCIENCET32HG000044 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL P. SNYDER · 1995 to 2026
$32.2M
Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologiesU01AG072573 · NIA · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2021 to 2025
$8.3M
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory codeU01HG012069 · NHGRI · STANFORD UNIVERSITY · PI Anshul Kundaje · 2021 to 2026
$3.9M
Identifying causal genetic variants and molecular mechanisms impacting mental healthR01MH125244 · NIMH · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2021 to 2025
$3.0M
NHGRI NIH HHS T32 HG000044NHGRI NIH HHS U01 HG012069NIA NIH HHS U01 AG072573NIMH NIH HHS R01 MH125244
6 · The paper itself

Abstract

Standard quantitative trait locus (QTL) mapping approaches consider variant effects on a single gene at a time, despite abundant evidence of allelic pleiotropy, where a single variant can affect multiple genes simultaneously. While allelic pleiotropy describes variant effects on both local and distal genes or a mixture of molecular effects on a single gene, here, we specifically investigate allelic expression "proxitropy," where a single variant influences the expression of multiple, neighboring genes. We introduce a multi-gene expression QTL (eQTL) mapping framework-cis-principal-component eQTL (cis-pc eQTL or pcQTL)-to identify variants associated with shared axes of expression variation across a cluster of neighboring genes. We perform pcQTL mapping in 13 GTEx human tissues and discover novel loci undetected by single-gene approaches. In total, we identify an average of 1,396 pcQTLs/tissue, 27% of which were not discovered by single-gene methods. These novel pcQTLs colocalized with an additional 176 genome-wide association study (GWAS) trait-associated variants and increased the number of colocalizations by 33% over single-gene QTL mapping. These findings highlight the idea that moving beyond single-gene-at-a-time approaches toward multi-gene methods can offer a more comprehensive view of gene regulation and complex-trait-associated variation.

Indexed as

Genetic VariationQuantitative Trait LociAllelesChromosome MappingGenome-Wide Association StudyHumansPolymorphism, Single Nucleotideallelic pleiotropyallelic proxitropycis-eQTLco-expressioncomplex-trait-associated variationgene clustersGTExGWAS colocalizationmulti-gene analysisprincipal-component QTL

Identifiers

PMID41875896
PMCPMC13087463

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Registered trials

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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.