Meta-analysis of whole-exome sequencing data from two independent cohorts finds no evidence for rare variant enrichment in Parkinson disease associated loci

Joint-tissue integrative analysis identifies high-risk genes for Parkinson's disease

The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.

Large-scale rare variant burden testing in Parkinson's disease

Parkinson's disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson's disease. To address this gap, we investigated the rare genetic component of Parkinson's disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson's disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2, have variants that have been previously implicated as risk factors for Parkinson's disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson's disease. To date, this is the largest analysis of rare genetic variants in Parkinson's disease.

Expanding causal genes for Parkinson's disease via multi-omics analysis

Genome‑wide association studies (GWASs) have revealed numerous loci associated with Parkinson's disease (PD). However, some potential causal/risk genes were still not revealed and no etiological therapies are available. To find potential causal genes and explore genetically supported drug targets for PD is urgent. By integrating the expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood, cerebrospinal fluid (CSF) and brain) and PD GWAS summary statistics, a pipeline combing Mendelian randomization (MR), Steiger filtering analysis, Bayesian colocalization, fine mapping, Protein-protein network and enrichment analysis were applied to identify potential causal genes for PD. As a result, GPNMB displayed a robust causal role for PD at the protein level in the blood, CSF and brain, and transcriptional level in the brain, while the protective role of CD38 (in brain pQTL and eQTL) was also identified. We also found inconsistent roles of DGKQ on PD between protein and mRNA levels. Another 9 proteins (CTSB, ARSA, SEC23IP, CD84, ENTPD1, FCGR2B, BAG3, SNCA, FCGR2A) were associated with the risk for PD based on only a single pQTL after multiple corrections. We also identified some proteins' interactions with known PD causative genes and therapeutic targets. In conclusion, this study suggested GPNMB, CD38, and DGKQ may act in the pathogenesis of PD, but whether the other proteins involved in PD needs more evidence. These findings would help to uncover the genes underlying PD and prioritize targets for future therapeutic interventions.

A multicenter study of genetic testing for Parkinson’s disease in the clinical setting

Parkinson’s disease (PD) guidelines lack clear criteria for genetic evaluation. We assessed the yield and rationale of genetic testing for PD in a routine clinical setting on a multicenter cohort of 149 early-onset and familial patients by exome sequencing and semi-quantitative multiplex ligation-dependent probe amplification of evidence-based PD-associated gene panel. We show that genetic testing for PD should be considered for both early-onset and familial patients alike, and a clinical yield of about 10% in the Caucasian population can be expected.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

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

Given that improved imputation software and high-coverage whole genome sequence (WGS)-based haplotype reference panels now enable inexpensive approximation of WGS genotype data, we hypothesised that WGS-based imputation and analysis of existing ExomeChip-based genome-wide association (GWA) data will identify novel intronic and intergenic single nucleotide polymorphism (SNP) effects associated with complex disease risk. In this study, we reanalysed a Parkinson’s disease (PD) dataset comprising 5540 cases and 5862 controls genotyped using the ExomeChip-based NeuroX array. After genotype imputation and extensive quality control, GWA analysis was performed using PLINK and a recently developed machine learning approach (GenEpi), to identify novel, conditional and joint genetic effects associated with PD. In addition to improved validation of previously reported loci, we identified five novel genome-wide significant loci associated with PD: three (rs137887044, rs78837976 and rs117672332) with 0.01 < MAF < 0.05, and two (rs187989831 and rs12100172) with MAF < 0.01. Conditional analysis within genome-wide significant loci revealed four loci (p < 1 × 10⁻⁵) with multiple independent risk variants, while GenEpi analysis identified SNP–SNP interactions in seven genes. In addition to identifying novel risk loci for PD, these results demonstrate that WGS-based imputation and analysis of existing exome genotype data can identify novel intronic and intergenic SNP effects associated with complex disease risk.

Crosstalk between Different DNA Repair Pathways Contributes to Neurodegenerative Diseases

Simple Summary

Constant exposure to endogenous and environmental factors induces oxidative stress and DNA damage. Rare brain disorders caused by defects in DNA repair and DNA damage response (DDR) signaling establish that failure to process DNA damage may lead to neurodegeneration. In this review, we present mechanisms that link DDR with neurodegeneration in these disorders and discuss their relevance for common age-related neurodegenerative diseases (NDDs). Moreover, we highlight recent insight into the crosstalk between the DDR and other cellular processes known to be disturbed during NDDs.

Abstract

Genomic integrity is maintained by DNA repair and the DNA damage response (DDR). Defects in certain DNA repair genes give rise to many rare progressive neurodegenerative diseases (NDDs), such as ocular motor ataxia, Huntington disease (HD), and spinocerebellar ataxias (SCA). Dysregulation or dysfunction of DDR is also proposed to contribute to more common NDDs, such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and Amyotrophic Lateral Sclerosis (ALS). Here, we present mechanisms that link DDR with neurodegeneration in rare NDDs caused by defects in the DDR and discuss the relevance for more common age-related neurodegenerative diseases. Moreover, we highlight recent insight into the crosstalk between the DDR and other cellular processes known to be disturbed during NDDs. We compare the strengths and limitations of established model systems to model human NDDs, ranging from C. elegans and mouse models towards advanced stem cell-based 3D models.