# Identifying associations of de novo noncoding variants with autism through integration of gene expression, sequence, and sex information

## TL;DR

This paper investigates how noncoding genetic variants contribute to autism by integrating gene expression, sequence data, and sex information.

## Contribution

The study introduces ENSAS, a new framework for identifying phenotype-associated variant sets using gene expression and sequence data.

## Key findings

- Local GC content can capture similar autism association signals as deep learning methods.
- ASD association signals are driven by variants upstream of genes in male proband-female sibling pairs.
- ENSAS identifies gene expression-based neighborhoods with significant ASD associations enriched for synapse-related terms.

## Abstract

Whole-genome sequencing (WGS) data has facilitated genome-wide identification of rare noncoding variants. However, elucidating these variants’ associations with complex diseases remains challenging. A previous study utilized a deep-learning-based framework and reported a significant brain-related association signal of autism spectrum disorder (ASD) detected from de novo noncoding variants in the Simons Simplex Collection (SSC) WGS cohort.

We revisit the reported significant brain-related ASD association signal attributed to deep-learning and show that local GC content can capture similar association signals. We further show that the association signal appears driven by variants from male proband-female sibling pairs that are upstream of assigned genes. We then develop Expression Neighborhood Sequence Association Study (ENSAS), which utilizes gene expression correlations and sequence information, to more systematically identify phenotype-associated variant sets. Applying ENSAS to the same set of de novo variants, we identify gene expression-based neighborhoods showing significant ASD association signal, enriched for synapse-related gene ontology terms. For these top neighborhoods, we also identify chromatin state annotations of variants that are predictive of the proband-sibling local GC content differences.

Overall, our work simplifies a previously reported ASD signal and provides new insights into associations of noncoding de novo mutations in ASD. We also present a new analytical framework for understanding disease impact of de novo mutations, applicable to other phenotypes.

The online version contains supplementary material available at 10.1186/s13059-025-03619-1.