Identifying associations of <i>de novo</i> noncoding variants with autism through integration of gene expression, sequence and sex information.

ABSTRACT: Whole-genome sequencing (WGS) data is facilitating genome-wide identification of rare noncoding variants, while elucidating their roles in disease remains challenging. Towards this end, we first revisit a reported significant brain-related association signal of autism spectrum disorder (ASD) detected from de novo noncoding variants 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.

SUBMITTER: Li R 

PROVIDER: S-EPMC10983996 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature