Evidence map›Paper›PMID 42105749›Full record

ArticleAmerican journal of human genetics2026

Principled measures and estimates of trait polygenicity.

Luke J O'Connor, Guy Sella

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Luke J O'ConnorDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: loconnor@hms.harvard.edu.
Guy SellaDepartment of Biological Sciences, Columbia University, New York, NY 10027, USA; Program for Mathematical Genomics, Columbia University, New York, NY 10032, USA. Electronic address: gs2747@columbia.edu.

Funding

The population genetics of disease risk and other quantitative traitsR01GM115889 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SELLA, GUY · 2015 to 2025
$2.9M
The functional and phenotypic effects of protein coding genetic variationR35GM155278 · NIGMS · HARVARD MEDICAL SCHOOL · PI Luke Jen O'Connor · 2024 to 2026
$1.3M
NIGMS NIH HHS R01 GM115889NIGMS NIH HHS R35 GM155278
6 · The paper itself

Abstract

While quantitative and statistical genetic studies often invoke the "polygenicity" of traits and sometimes quantify it, the meaning of the term is not always clear. We propose a principled definition of polygenicity that encompasses a range of measures. We show that these measures satisfy mathematical properties that are sensible and, conversely, that measures that satisfy these properties meet our definition. We consider four specific measures in greater detail, describe how they differ, and show that three of them can be estimated from genome-wide association studies summary statistics using Fourier mixture regression. We then estimate these measures for 36 human traits. This analysis reveals a lack of traits with polygenicity values that fall in the large gap between Mendelian and highly polygenic traits. We discuss the generality of this finding and the evolutionary and cellular processes that may explain it.

Indexed as

Models, GeneticMultifactorial InheritanceQuantitative Trait, HeritableGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait Locigenetic architecturegenome-wide association studiespolygenicity

Identifiers

PMID42105749
PMCPMC13231060

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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.