Evidence map›Paper›PMID 42102804›Full record

ArticleCell genomics2026

Buffering of gene dosage response curves for human complex traits.

Nikhil Milind, Courtney J Smith, Huisheng Zhu, Tamara Gjorgjieva, Jeffrey P Spence, Jonathan K Pritchard

Abstract read
In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Genetic architectures of brain-related traits are shaped by strong selective constraints.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  11. Human-specific gene expansions contribute to brain evolution.bioRxiv : the preprint server for biology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Nikhil MilindDepartment of Genetics, Stanford University, Stanford, CA 94305, USA. Electronic address: nmilind@stanford.edu.
Courtney J SmithDepartment of Genetics, Stanford University, Stanford, CA 94305, USA.
Huisheng ZhuDepartment of Biology, Stanford University, Stanford, CA 94305, USA.
Tamara GjorgjievaDepartment of Genetics, Stanford University, Stanford, CA 94305, USA.
Jeffrey P SpenceDepartment of Genetics, Stanford University, Stanford, CA 94305, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: jeff.spence@ucsf.edu.
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA. Electronic address: pritch@stanford.edu.

Funding

Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
New methods for constructing and evaluating polygenic scoresR01HG011432 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2020 to 2023
$3.3M
Bayesian estimation of gene effects on traits from coding variantsR01HG014005 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2025 to 2026
$1.3M
NHGRI NIH HHS R01 HG008140NHGRI NIH HHS R01 HG011432NHGRI NIH HHS R01 HG014005
6 · The paper itself

Abstract

The genome-wide burdens of deletions, loss-of-function mutations, and duplications correlate with many traits. Curiously, for most of these traits, variants that decrease expression have the same genome-wide average direction of effect as variants that increase expression. This seemingly contradicts the intuition that for individual genes, reducing expression should have the opposite effect on a phenotype as increasing expression. To understand this paradox, we use the gene dosage response curve (GDRC), which relates changes in gene expression to expected changes in phenotype. We show that, for many traits, GDRCs are systematically biased in one trait direction relative to the other, a phenomenon we call trait buffering. Because of trait buffering, traits are more easily modified in one direction than the other by genetic variation. We develop a simple theoretical model that explains this bias in trait direction. Our results have broad implications for complex traits, drug discovery, and statistical genetics.

Indexed as

Gene DosageGenetic VariationGenome-Wide Association StudyHumansModels, GeneticPhenotypeburden testscomplex traitsduplicationsgene expressiongenome-wide association studieshuman geneticsloss-of-function variants

Identifiers

PMID42102804
PMCPMC13347943

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.