Neurodegenerative diseases (NDDs) are disorders caused by degeneration of neurons leading to damage to the nervous system, especially the brain. It affects about 15% of the people worldwide and is expected to increase with the aging population. The underlying causes of complex NDDs are not well understood due to complicated gene-environmental interactions which makes it difficult to accurately diagnose patients. Identifying genetic factors in these diseases is essential for early and accurate diagnosis, enabling patients access to timely interventions. In this dissertation, we developed a strategy to identify genetic factors associated with a complex NDD using environmental modifiers. We utilized genetic information from a cohort of well-documented patients that included data on disease progression and environmental exposures to identify rare variants associated with disease risk. We found rare variants that were highly associated between disease and exposure patterns. However, validation of these variants using functional studies is cumbersome and expensive in both time and cost. Therefore, we proposed to use a novel approach, transcriptional profiling, to validate variants in patients directly. To test our method, we started with a simple use case to identify disease-specific transcriptomic signatures from known patients and mouse models in a rare neuromuscular disorder. We demonstrated that disease-specific transcriptomic signatures could be derived and subsequently applied to validate a variant of unknown clinical significance in a test patient. We also demonstrated that for genes that are ubiquitously expressed, we can leverage accessible tissues such as whole blood to derive disease specific expression profiles for NDDs. We then applied our method to a relatively more prevalent heterogenous ataxic disease. We developed disease-specific biomarkers to validate patients suspected of having this disorder. We expect our transcriptional profiling method to provide a foundation for investigating more complex neurodegenerative diseases and to help identify potential therapeutic targets that could improve patient prognosis.