Abstract
Progressive and late-onset neurological disorders such as Parkinson's disease and Alzheimer's disease affect up to 50 million people globally-a number postulated to double every 20 years in a continually aging population. While predisposing allelic variants in several genes clearly confer risk, individual age and specific environmental influences are equally important discriminators of disease onset age and progression. However, none of these factors can independently predict disease with significant precision. Therefore, we must actively develop models that accommodate contributions from all factors, potentially resulting in an A × G × E (age-gene-environment) metric that reflects individual cumulative risk and reliably forecasts disease outcomes. This effort can only be enabled by a deep quantitative understanding of the contribution of these factors to neurodegenerative disease, both individually and in combination. This is also an important consideration because neuronal loss typically precedes clinical presentation and disease-modifying therapies are contingent on early diagnosis that is likely to be informed by an accurate estimation of individual risk. Although epidemiological studies continue to make strong advances in these areas with the advent of powerful "omics"-based approaches, systematic phenotypic modeling of AxGxE interactions is currently more feasible in model organisms such as Drosophila melanogaster where all three parameters can be manipulated with manageable experimental burden. Here, we outline the advantages of using fruit flies for investigating these complex interactions and highlight potential approaches that might help synthesize existing information from diverse fields into a cogent description of age-dependent, environmental, and genetic risk factors in the pathophysiology of neurological disorders.
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