Evidence map›Paper›PMID 40634707›Full record

ArticleNature genetics2025

Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs.

Aviya Litman, Natalie Sauerwald, LeeAnne Green Snyder, Jennifer Foss-Feig, Christopher Y Park, Yun Hao, Ilan Dinstein, Chandra L Theesfeld, Olga G Troyanskaya

Abstract read
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

60 citing papers in PubMed.

  1. Article
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  12. Challenging the Developmental Substructure Model for Autism: A Perspective From Causal Statistics.Autism research : official journal of the International Society for Autism Research · 2026
    Article
  13. Article
  14. Article
  15. Sterol biosynthesis, brain development, and disease.The Journal of clinical investigation · 2026
    Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors.Autism research : official journal of the International Society for Autism Research · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Aviya Litman *Quantitative and Computational Biology Program, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0002-0044-7617
Natalie Sauerwald *Center for Computational Biology, Flatiron Institute, New York, NY, USA. nsauerwald@flatironinstitute.org.
LeeAnne Green SnyderSimons Foundation, New York, NY, USA.
Jennifer Foss-FeigSimons Foundation, New York, NY, USA.
Christopher Y ParkCenter for Computational Biology, Flatiron Institute, New York, NY, USA.
Yun HaoCenter for Computational Biology, Flatiron Institute, New York, NY, USA.
Ilan DinsteinCognitive and Brain Sciences Department, Ben Gurion University of the Negev, Be'er Sheva, Israel.
Chandra L TheesfeldLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0002-8379-6600
Olga G TroyanskayaLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. ogt@genomics.princeton.edu.ORCID http://orcid.org/0000-0002-5676-5737

Funding

Quantitavie and Computational Biology Graduate ProgramT32HG003284 · NHGRI · PRINCETON UNIVERSITY · PI Joshua Michael Akey, Stanislav Y. Shvartsman · 2004 to 2026
$9.9M
lntegration and Visualization of Diverse Biological DataR01GM071966 · NIGMS · PRINCETON UNIVERSITY · PI TROYANSKAYA, OLGA G · 2005 to 2022
$6.4M
NHGRI NIH HHS T32 HG003284NIGMS NIH HHS R01 GM071966Simons Foundation 395506U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) T32HG003284U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R01GM071966
6 · The paper itself

Abstract

Unraveling the phenotypic and genetic complexity of autism is extremely challenging yet critical for understanding the biology, inheritance, trajectory and clinical manifestations of the many forms of the condition. Using a generative mixture modeling approach, we leverage broad phenotypic data from a large cohort with matched genetics to identify robust, clinically relevant classes of autism and their patterns of core, associated and co-occurring traits, which we further validate and replicate in an independent cohort. We demonstrate that phenotypic and clinical outcomes correspond to genetic and molecular programs of common, de novo and inherited variation and further characterize distinct pathways disrupted by the sets of mutations in each class. Remarkably, we discover that class-specific differences in the developmental timing of affected genes align with clinical outcome differences. These analyses demonstrate the phenotypic complexity of children with autism, identify genetic programs underlying their heterogeneity, and suggest specific biological dysregulation patterns and mechanistic hypotheses.

Indexed as

Autistic DisorderChildChild, PreschoolCohort StudiesFemaleGenetic HeterogeneityGenetic Predisposition to DiseaseHumansMaleMutationPhenotype

Identifiers

PMID40634707
PMCPMC12283356

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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.