Investigating ALS genetic epidemiology complexity through a multi-analytical approach and introducing GenUInE, a tool for genomic signals prioritization

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disease characterized by motor neuron degeneration in both familial (fALS) and sporadic (sALS) forms. Despite the great advancements in identifying genetic factors, understanding the full genetic landscape of complex diseases such as ALS remains challenging, due to the limited power of the studies or intrinsic constraints of single analytical methods. To address this point, we developed GenUInE, a multi-analysis aggregator tool designed to integrate results from various genomic analyses into a final unified matrix, enabling the identification of genetic hotspots. GenUInE uses a probability-based model to prioritize genomic windows associated with disease traits by analyzing diverse input sources such as homozygosity mapping, IBD segments, epivariations, and rare variants. The tool computes combined probabilities and summation values for each window, additionally providing a score for prioritizing and weighting genomic regions. Applied to ALS, GenUInE successfully highlighted previously ALS-associated known genes (NIPA1) and identified novel genetic signatures linked to neurodegenerative pathways. Our work provides a novel framework for exploring genetics in complex diseases, providing a different method aimed at identifying new therapeutic targets.