Back

Gene-Burden Meta-Analysis of 748,879 Individuals Identifies LGI1-ADAM23 Protein Complex Association with Epilepsy

Lal, J. C.; Leu, C.; Bosselmann, C.; Ivaniuk, A.; Perez-Palma, E.; Lal, D.

2024-12-31 genetic and genomic medicine
10.1101/2024.12.30.24319794 medRxiv
Show abstract

Epilepsy affects over 50 million individuals globally and has a substantial genetic component that remains to be completely understood. Traditional studies have focused on severe, early-onset cases enrolled through clinical or research settings. Recent biobank-based approaches, leveraging large-scale population datasets, offer opportunities to explore genetic associations in broader epilepsy phenotypes, including milder, later-onset forms. We analyzed data from over 750,000 individuals across the UK Biobank, All of Us, and Massachusetts General Brigham Biobank, including 20,026 individuals with epilepsy. Rare coding variant burden testing revealed a significant association with LGI1, a known epilepsy gene. Other top-ranked genes, including GABRG2, ATP1A3, and ADAM23, demonstrated strong enrichment for epilepsy-associated variants. Notably, ADAM23, previously linked to epilepsy in dogs and strong brain expression in humans, emerged as a novel candidate, potentially contributing to human epilepsy through its direct interaction with LGI1. Phenome-wide analyses highlighted the pleiotropic effects of epilepsy genes, with LGI1 and ADAM23 predominantly associated with epilepsy, while other genes such as KRIT1, TSC1, and TSC2 exhibited broader systemic involvement. Our study shows the potential of population-scale genomic data and suggests that integrating these datasets with deep phenotyping will uncover more novel insights into epilepsy genetics in the future.

Matching journals

The top 6 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.