Back

Regulatory de novo mutations underlying intellectual disability

De Vas, M.; Boulet, F.; Joshi, S. S.; Garstang, M. G.; Khan, T. N.; Atla, G.; Parry, D.; Moore, D.; Cebola, I.; Zhang, S.; Cui, W.; Lampe, A. K.; Lam, W. W.; Ferrer, J.; Madapura, P.; Atanur, S. S.

2022-11-17 genetic and genomic medicine
10.1101/2022.11.14.22279410 medRxiv
Show abstract

The genetic aetiology of a major fraction of patients with intellectual disability (ID) remains unknown. De novo mutations (DNMs) in protein-coding genes explain up to 40% of cases, but the potential role of regulatory DNMs is still poorly understood. We sequenced 63 whole genomes from 21 ID probands and their unaffected parents (trio). Additionally, we analysed 30 previously sequenced genomes from exome-negative ID probands. We found that regulatory DNMs were selectively enriched in fetal brain-specific and human-gained enhancers. DNM-containing enhancers were associated with genes that show preferential expression in the pre-frontal cortex, have been previously implicated in ID or related disorders, and exhibit intolerance to loss of function mutations. Moreover, we found that highly interacting regulatory regions from intermediate progenitor cells of the developing human cortex were strongly enriched for ID DNMs. Furthermore, we identified recurrently mutated enhancer clusters that regulate genes involved in nervous system development (CSMD1, OLFM1, and POU3F3). The majority of the DNMs from ID probands showed allele-specific enhancer activity when tested using luciferase assay. Using CRISPR-mediated mutation and editing of epigenomic marks, we show that regulatory elements harbouring DNMs indeed function as enhancers and DNMs at regulatory elements affect the expression of putative target genes. Our results, therefore, provide new evidence to indicate that DNMs in fetal brain-specific enhancers play an essential role in the aetiology of ID.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.