![]() ![]() Modelling interactions may yield insights into PD pathobiology, further improve prediction algorithms and suggest potential ways to modify risk through intervention in geneticallystratified groups. ![]() The modest overall liability explained by genetic factors and small individual effect sizes of environmental risk factors for PD suggest that interactions between them may explain some of the missing risk. 4 5 There have been efforts to incorporate these non-genetic risk factors into predictive algorithms to identify individuals at higher risk of PD. 3 Separately, epidemiological studies have identified potentially modifiable exposures, various comorbidities and prodromal features. From the latest PD GWAS, 90 independent signals were identified, which collectively explain ~16% of overall PD liability. Over the past decade, large genome-wide association studies (GWAS) of PD have built on linkage studies of rare, familial forms of PD. 2 Identification of at-risk individuals and earlier detection likely represent the best opportunities for the development of effective treatments to prevent or reverse progression of PD. 1 By the time an individual is diagnosed with PD, a substantial proportion of nigrostriatal neurons has already been lost. Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide.
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