Genetics of Parkinson's Disease: state-of-the-art and role in clinical settings

Advances in sequencing technologies have enabled extensive genetic testing on an individual basis. Genome-wide association studies (GWAS) have provided insight into the pathophysiology of PD. Additionally, direct-to-consumer genetic testing has enabled the identification of genetic diseases and risk factors without genetic counselling. As genetics increasingly permeates clinical practice, this paper aims to summarise the most important information on genetics in PD forclinical practitioners.

LRRK2 mutations may be found in c.1% of all PD patients with an indistinguishable phenotype from sporadic PD. LRRK2-PD is more prevalent in patients with a positive family history (5-6%) and among certain populations (e.g. up to 41% in North Africans and Ashkenazi Jews). Other familial forms include PRKN (patients with early onset, EOPD), VPS35 (Western European ancestry), PINK1 (EOPD), DJ-1 (EOPD), and SNCA. GBA mutations are found in a large number of PD patients and are associated with faster progression and a poorer prognosis. GWAS have identified 90 genetic risk variants for developing PD and several genetic modifiers for the age at onset, disease progression, and response to treatment.

Multigene panels using next-generation sequencing (NGS) are the first choice for genetic testing in clinical settings. Whole exome sequencing is increasingly being used, particularly as the second-tier testing in patients with negative results of multigene panels. NGS may not detect accurately copy number variants (CNV), meaning that additional analysis is warranted. In a case of a variant of unknown significance (VUS), we suggest firstly searching the up-to-date literature. Segregation studies and in silico predictions may shed more light on the character of the VUS; however, functional studies remain the gold standard. Several interventional clinical trials are active for carriers of LRRK2 and/or GBA mutations.

Application of artificial intelligence and machine learning will enable high-throughput analysis of large sets of multimodal data. We speculate that, in the future, the treatment landscape for PD will be similar to that in oncological conditions, in which the presence of certain gene mutations or gene overexpression determines the prognosis and treatment decision-making.