Uncovering new genes and disease modifiers for ALS and related disorders

PROJECT SUMMARY (Project 2: Genes and Modifiers) Missing heritability is a major unsolved problem in amyotrophic lateral sclerosis (ALS) and related motor neuron diseases (MNDs), such as primary lateral sclerosis, progressive muscular atrophy, and hereditary spastic paraplegia. Additionally, much remains unknown about the considerable phenotypic heterogeneity observed in the course of these devastating diseases, even in people harboring the same mutation (e.g., C9orf72 repeat expansion). As such, in our present proposal, we seek to discover novel genes or genomic loci as well as disease modifiers for ALS-spectrum disorders. To this end, we will expand our recently developed rare variant analysis framework to include coding, non-coding, and structural variants, in addition to integrating public control data (Aim 1). Our streamlined cloud-based workflows and publicly accessible data portal will handle genomic data in an efficient, reliable, and cost-effective manner, while promoting collaborations among rare disease investigators, thereby accelerating discoveries. Moreover, we will leverage innovative statistical modeling methods to identify latent variables underlying disease progression and intra-patient clinical variability (Aim 2). Our method has been designed to deal with irregularly spaced time points, measurement noise, and non-linear trends, enabling us to reconstruct full disease progression trajectories. Those trajectories will then be used to uncover genetic disease modifiers, refine disease subtypes, and predict disease-related outcomes (e.g., survival time). Furthermore, we will capitalize on our long-read sequencing experience to comprehensively characterize the expanded C9orf72 repeat (Aim 3). By measuring the length, purity, and methylation profile of the expansion at an unprecedented resolution, we can determine whether they may account for different aspects of phenotypes associated with the expanded repeat. Taken together, our cutting-edge approaches that exploit cloud-based computational methods, statical learning models, and long-read sequencing technologies, will not only improve upon our ability to find new genes, genomic loci, and disease modifiers for ALS-spectrum disorders, but also lay the foundation for personalized prognostic and therapeutic strategies.