Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson's Disease Risk

Parkinson's Disease, Parkinsonisms, and Mitochondria: the Role of Nuclear and Mitochondrial DNA

PURPOSE OF REVIEW

Overwhelming evidence indicates that mitochondrial dysfunction is a central factor in Parkinson's disease (PD) pathophysiology. This paper aims to review the latest literature published, focusing on genetic defects and expression alterations affecting mitochondria-associated genes, in support of their key role in PD pathogenesis.

RECENT FINDINGS

Thanks to the use of new omics approaches, a growing number of studies are discovering alterations affecting genes with mitochondrial functions in patients with PD and parkinsonisms. These genetic alterations include pathogenic single-nucleotide variants, polymorphisms acting as risk factors, and transcriptome modifications, affecting both nuclear and mitochondrial genes. We will focus on alterations of mitochondria-associated genes described by studies conducted on patients or on animal/cellular models of PD or parkinsonisms. We will comment how these findings can be taken into consideration for improving the diagnostic procedures or for deepening our knowledge on the role of mitochondrial dysfunctions in PD.

Genetic Architecture of Parkinson's Disease

Year 2022 marks 25 years since the first mutation in familial autosomal dominant Parkinson's disease was identified. Over the years, our understanding of the role of genetic factors in the pathogenesis of familial and idiopathic forms of Parkinson's disease has expanded significantly - a number of genes for the familial form of the disease have been identified, and DNA markers for an increased risk of developing its sporadic form have been found. But, despite all the success achieved, we are far from an accurate assessment of the contribution of genetic and, even more so, epigenetic factors to the disease development. The review summarizes the information accumulated to date on the genetic architecture of Parkinson's disease and formulates issues that need to be addressed, which are primarily related to the assessment of epigenetic factors in the disease pathogenesis.

Gene-wide significant association analyses of DNMT1 genetic variants with Parkinson's disease

Background: DNA methylation plays an important role in Parkinson's disease (PD) pathogenesis. DNA methyltransferase 1 (DNMT1) is critical for maintaining DNA methylation in mammals. The link between DNMT1 polymorphisms and PD remains elusive. Methods: The DNMT1 gene contained a total of 28 single nucleotide polymorphisms (SNPs). Four representing tag-SNPs (rs16999593, rs2162560, rs11880553, and rs9305012) were identified and genotyped in a Han Chinese population comprising 712 PD patients and 696 controls. Association analyses were performed at gene-wide significance (p < 1.8 × 10^-3). Results: Rs9305012, but not the other 3 tag-SNPs, was gene-wide significantly associated with PD risk (p = 0.8 × 10^-3). The rs9305012/C was a protective allele against PD (p = 1.5 × 10^-3, OR 0.786, 95% CI 0.677-0.912). No significant association was observed in individual genders or PD subtypes. Haplotypes of the 4 tag-SNPs showed a significant overall distribution difference between PD patients and controls (p < 1 × 10^-4). The 3-allele ACC module in the order of rs2162560, rs11880553, and rs9305012 was the highest-risk haplotype associated with PD (p < 1 × 10^-4, OR 2.439, 95% CI 1.563-3.704). Rs9305012 displayed certain probability to affect transcription factor binding and target gene expression based on functional annotation analyses. Conclusion: The DNMT1 variant rs9305012 together with its haplotypes may gene-wide significantly modulate PD susceptibility. Our results support a role of DNMT1 in PD pathogenesis and provide novel insights into the genetic connection in between.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.