Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Machine learning nominates the inositol pathway and novel genes in Parkinson's disease

There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.

Redefining the hypotheses driving Parkinson's diseases research

Parkinson’s disease (PD) research has largely focused on the disease as a single entity centred on the development of neuronal pathology within the central nervous system. However, there is growing recognition that PD is not a single entity but instead reflects multiple diseases, in which different combinations of environmental, genetic and potential comorbid factors interact to direct individual disease trajectories. Moreover, an increasing body of recent research implicates peripheral tissues and non-neuronal cell types in the development of PD. These observations are consistent with the hypothesis that the initial causative changes for PD development need not occur in the central nervous system. Here, we discuss how the use of neuronal pathology as a shared, qualitative phenotype minimises insights into the possibility of multiple origins and aetiologies of PD. Furthermore, we discuss how considering PD as a single entity potentially impairs our understanding of the causative molecular mechanisms, approaches for patient stratification, identification of biomarkers, and the development of therapeutic approaches to PD. The clear consequence of there being distinct diseases that collectively form PD, is that there is no single biomarker or treatment for PD development or progression. We propose that diagnosis should shift away from the clinical definitions, towards biologically defined diseases that collectively form PD, to enable informative patient stratification. N-of-one type, clinical designs offer an unbiased, and agnostic approach to re-defining PD in terms of a group of many individual diseases.