Molecular Genetics of Parkinsonism

Abstract

Over the past decade, genetic causes of parkinsonism have been elucidated, but they account in most populations, for less than 10% of the cases. Since the discovery of the first gene responsible for Parkinson disease (PD), SCNA encoding α‐synuclein, linkage mapping and positional cloning have identified autosomal dominantly or recessively inherited PD‐causing mutations in the genes encoding parkin , PINK1 , DJ‐1 , LRRK2 and ATP13A2 , indicating that PD has a highly heterogeneous aetiology. With the introduction of next‐generation sequencing, rare mutations in DNAJC6 , SYNJ1 , VPS13C , VPS35 , DNAJC13 , TMEM230 and CHCHD2 were then discovered to cause inherited parkinsonism. In addition, genetic studies from candidate genes, to unbiased genome‐wide approaches including association and next‐generation sequencing have nominated a number of disease determinants. In this article, we will highlight the current progress and future prospects in the field of PD genetics, since 2010. Key Concepts The introduction of next‐generation sequencing technologies associated with exome/genome sequencing has accelerated the discovery of novel causative genes and susceptibility factors in numerous Mendelian disorders. Integrative approaches combining DNA sequencing with gene expression and methylome data will support and accelerate the identification and functional characterisation of the biological variants and disease genes. Deep and accurate patient phenotyping is of major importance for the success of gene identification in heterogeneous diseases. The use of larger and more homogeneous cohorts (endophenotype) will increase the chances of identifying high/intermediate risk variants in whole exome/whole genome sequencing. Identification of additional PD and parkinsonism‐associated genes will enable further elucidation of the disease mechanisms and the development of disease‐modifying therapeutic strategies. Meta‐analysis of many genome‐wide association studies improves the power to detect more associations and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations.