Imaging genetics approach to Parkinson’s disease and its correlation with clinical score

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with both underlying genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson’s disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone.

Predicting progression in patients with Parkinson's disease

“…Such findings are in contrast with those of previous studies in which imaging was a predictor of long-term motor outcomes and, when based on the same PPMI data, imaging in combination with genetics was used to predict MDS-UPDRS scores. 6,7 …”

Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson’s disease

“…Our study considered two types of information (i.e., neuroimaging and genetic information) and many procedures are necessary to process them. The schematic of the overall processing steps is given in Fig 1 [24]. Details regarding the procedures are provided later in this study.…”

“…The ENIGMA protocol included the following processes: 1) call rate check per subject, 2) sex check, 3) sibling pair identification and 4) population stratification using multi-dimensional scaling. In addition, the SNPs that did not meet the quality control criteria (minor allele frequency < 0.01; genotype call rate < 95%; Hardy-Weinberg equilibrium < 10 −6 ) were filtered out of the dataset [24,28,29]. SNPs were only kept if they did not belong to the Caucasian population according to the HapMap3 reference population.…”

Multi view based imaging genetics analysis on Parkinson disease

“…Despite the success in characterizing complex neurodegenerative diseases, such as AD, imaging genetics analyses have been scarcely applied on PD. However, studies combining neuroimaging and genomic data have been shown effective in discovering potential genetic drivers of PD [10,11]. Kim and coworkers investigated the factors underlying the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) scores in PD patients [10].…”

“…However, studies combining neuroimaging and genomic data have been shown effective in discovering potential genetic drivers of PD [10,11]. Kim and coworkers investigated the factors underlying the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) scores in PD patients [10]. The authors showed that linear models trained on genetic and neuroimaging data improve predictions on MDS-UPDRS scores, suggesting that genomic and brain imaging data provide complementary information in PD.…”