Linking rare variants to cell-type function in profound autism with brain transcriptomics and foundation models

Genetic association studies have identified numerous genes harboring protein-disrupting variants in individuals with profound autism, but identifying convergent points of vulnerability remains challenging. We discuss how brain transcriptomic resources help decode the cellular consequences of these rare gene-disrupting variants. The functional interpretation of genetic associations has largely relied on gene ontologies and protein-interaction networks, with newer approaches leveraging single-cell expression to estimate cellular enrichment. However, the broad expression of many autism-associated genes confounds cell-type-specific effects. We therefore propose a framework quantifying the trade-off between a gene's cell-type specificity and sensitivity. The limited overlap between genetic associations and transcriptomic alterations in autistic brains prompts a discussion about causality. We examine whether foundation models linking genetic variation to cell-type transcriptomes could clarify the cellular functions affected by autism-associated variants. By combining experimental perturbations, artificial-intelligence-driven inference, and postmortem validation, we propose a unifying mechanistic framework for rare-variant liability in autism.

Authors

Institutions

Publication Details

Journal
Cell Genomics
Published
2026-04-01
DOI
https://doi.org/10.1016/j.xgen.2026.101192
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linking rare variants to cell-type function in profound autism with brain transcriptomics and foundation models

Alma Dubuc, Guillaume Huguet, Thomas Renne, Tomasz Nowakowski et al.
Cell Genomics
Autism Spectrum Disorder Research
article

Linking rare variants to cell-type function in profound autism with brain transcriptomics and foundation models

Alma Dubuc, Guillaume Huguet, Thomas Renne, Tomasz Nowakowski, Sébastien Jacquemont
article en

Abstract

Genetic association studies have identified numerous genes harboring protein-disrupting variants in individuals with profound autism, but identifying convergent points of vulnerability remains challenging. We discuss how brain transcriptomic resources help decode the cellular consequences of these rare gene-disrupting variants. The functional interpretation of genetic associations has largely relied on gene ontologies and protein-interaction networks, with newer approaches leveraging single-cell expression to estimate cellular enrichment. However, the broad expression of many autism-associated genes confounds cell-type-specific effects. We therefore propose a framework quantifying the trade-off between a gene's cell-type specificity and sensitivity. The limited overlap between genetic associations and transcriptomic alterations in autistic brains prompts a discussion about causality. We examine whether foundation models linking genetic variation to cell-type transcriptomes could clarify the cellular functions affected by autism-associated variants. By combining experimental perturbations, artificial-intelligence-driven inference, and postmortem validation, we propose a unifying mechanistic framework for rare-variant liability in autism.

Cell GenomicsVol. 6(4)
Université Claude Bernard Lyon 1 (FR), École Normale Supérieure de Lyon (FR), University of California, San Francisco (US), Centre Hospitalier Universitaire Sainte-Justine (CA)
Alliance de recherche numérique du Canada, Courtois Foundation, National Institutes of Health, Canadian Institutes of Health Research, National Institute of Mental Health
Openalex Percentile: Top 24%
Autism Spectrum Disorder Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.