PSMF1 variants cause a phenotypic spectrum from early-onset Parkinson's disease to perinatal lethality by disrupting mitochondrial pathways.

Dissecting biological pathways highlighted by Mendelian gene discovery has provided critical insights into the pathogenesis of Parkinson's disease (PD) and neurodegeneration. This approach ultimately catalyzes the identification of potential biomarkers and therapeutic targets. Here, we identify PSMF1 as a new gene implicated in PD and childhood neurodegeneration. We find that biallelic PSMF1 missense and loss-of-function variants co-segregate with phenotypes from early-onset PD and parkinsonism to perinatal lethality with neurological manifestations across 15 unrelated pedigrees with 22 affected subjects, showing clear genotype-phenotype correlation. PSMF1 encodes the proteasome regulator PSMF1/PI31, a highly conserved, ubiquitously expressed partner of the 20S proteasome and neurodegeneration-associated F-box-O 7 and valosin-containing proteins. We demonstrate that PSMF1 variants impair mitochondrial membrane potential, dynamics and mitophagy in patient-derived fibroblasts. Additionally, we develop models of psmf1 knockdown Drosophila and Psmf1 conditional knockout mouse exhibiting age-dependent motor impairment, with diffuse gliosis in mice. These findings unequivocally link defective PSMF1 to early-onset PD and neurodegeneration and suggest mitochondrial dysfunction as a mechanistic contributor.

Characterizing a complex CT-rich haplotype in intron 4 of SNCA using large-scale targeted amplicon long-read sequencing.

Parkinson's disease (PD) is a common neurodegenerative disorder with a significant risk proportion driven by genetics. While much progress has been made, most of the heritability remains unknown. This is in-part because previous genetic studies have focused on the contribution of single nucleotide variants. More complex forms of variation, such as structural variants and tandem repeats, are already associated with several synucleinopathies. However, because more sophisticated sequencing methods are usually required to detect these regions, little is understood regarding their contribution to PD. One example is a polymorphic CT-rich region in intron 4 of the SNCA gene. This haplotype has been suggested to be associated with risk of Lewy Body (LB) pathology in Alzheimer's Disease and SNCA gene expression, but is yet to be investigated in PD. Here, we attempt to resolve this CT-rich haplotype and investigate its role in PD. We performed targeted PacBio HiFi sequencing of the region in 1375 PD cases and 959 controls. We replicate the previously reported associations and a novel association between two PD risk SNVs (rs356182 and rs5019538) and haplotype 4, the largest haplotype. Through quantitative trait locus analyzes we identify a significant haplotype 4 association with alternative CAGE transcriptional start site usage, not leading to significant differential SNCA gene expression in post-mortem frontal cortex brain tissue. Therefore, disease association in this locus might not be biologically driven by this CT-rich repeat region. Our data demonstrates the complexity of this SNCA region and highlights that further follow up functional studies are warranted.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources.

Although large-scale genetic association studies have proven opportunistic for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

Profiling complex repeat expansions in RFC1 in Parkinson's disease.

A biallelic (AAGGG) expansion in the poly(A) tail of an AluSx3 transposable element within the gene RFC1 is a frequent cause of cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS), and more recently, has been reported as a rare cause of Parkinson's disease (PD) in the Finnish population. Here, we investigate the prevalence of RFC1 (AAGGG) expansions in PD patients of non-Finnish European ancestry in 1609 individuals from the Parkinson's Progression Markers Initiative study. We identified four PD patients carrying the biallelic RFC1 (AAGGG) expansion and did not identify any carriers in controls.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

Federated learning for multi-omics: A performance evaluation in Parkinson's disease.

While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.

GBA1 rs3115534 Is Associated with REM Sleep Behavior Disorder in Parkinson's Disease in Nigerians.

BACKGROUND: Rapid eye movement (REM) sleep behavior disorder (RBD) is an early feature of Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Damaging coding variants in Glucocerebrosidase (GBA1) are a genetic risk factor for RBD. Recently, a population-specific non-coding risk variant (rs3115534) was found to be associated with PD risk and earlier onset in individuals of African ancestry. OBJECTIVES: We aimed to investigate whether the GBA1 rs3115534 PD risk variant is associated with RBD in persons with PD. METHODS: We studied 709 persons with PD and 776 neurologically healthy controls from Nigeria. All DNA samples were genotyped and imputed, and the GBA1 rs3115534 risk variant was extracted. The RBD screening questionnaire (RBDSQ) was used to assess symptoms of possible RBD. RESULTS: RBD was present in 200 PD (28.2%) and 51 (6.6%) controls. We identified that the non-coding GBA1 rs3115534 risk variant is associated with possible RBD in individuals of Nigerian origin (ß, 0.3640; standard error [SE], 0.103, P = 4.093e-04), as well as in all samples after adjusting for PD status (ß, 0.2542; SE, 0.108; P = 0.019) suggesting that although non-coding, this variant may have the same downstream consequences as GBA1 coding variants. CONCLUSIONS: Our results indicate that the non-coding GBA1 rs3115534 risk variant is associated with an increasing number of RBD symptoms in persons with PD of Nigerian origin. Further research is needed to assess if this variant is also associated with polysomnography-defined RBD and with RBD symptoms in DLB. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Analysis of rare Parkinson's disease variants in millions of people.

Although many rare variants have been reportedly associated with Parkinson's disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. We assessed a total of 27,590 PD cases, 6701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. The meta-analysis resulted in five significant variants after Bonferroni correction, including variants in GBA1 and LRRK2. Another eight variants are strong candidate variants for their association with PD. Here, we provide the largest rare variant meta-analysis to date, providing information on confirmed and newly identified variants for their association with PD using several large databases. Additionally we also show the complexities of studying rare variants in large-scale cohorts.

omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases.

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.

Multi-ancestry genome-wide association meta-analysis of Parkinson's disease.

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.