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Knowledge on the genetic basis of Parkinson's disease has grown tremendously since the discovery of the first monogenic form, caused by a mutation in α-synuclein, and with the subsequent identification of multiple other causative genes and associated loci. Genetic studies provide insights into the phenotypic heterogeneity and global distribution of Parkinson's disease. By shedding light on the underlying biological mechanisms, genetics facilitates the identification of new biomarkers and therapeutic targets. Several clinical trials of genetics-informed therapies are ongoing or imminent. International programmes in populations who have been under-represented in Parkinson's disease genetics research are fostering collaboration and capacity-building, and have already generated novel findings. Many challenges remain for genetics research in these populations, but addressing them provides opportunities to obtain a more complete and equitable understanding of Parkinson's disease globally. These advances facilitate the integration of genetics into the clinic, to improve patient management and personalised medicine.

LRRK2 -related Parkinson’s disease ( LRRK2 -PD) is the most frequent form of monogenic PD worldwide, with important therapeutic opportunities, exemplified by the advancement in LRRK2 kinase inhibition studies/trials. However, many LRRK2 variants, especially those found in underrepresented populations, remain classified as variants of uncertain significance (VUS). Leveraging on Malaysian, Singaporean, and mainland Chinese PD datasets ( n  = 4901), we describe 12 Chinese-ancestry patients harboring the LRRK2 p.Arg1067Gln variant, more than doubling the number of previously reported cases (total n  = 23, 87% East Asian, mean age of onset: 53.9 years). We determine that this variant is enriched in East Asian PD patients compared to population controls (OR = 8.0, 95% CI: 3.0–20.9), and provide supportive data for its co-segregation with PD, albeit with incomplete penetrance. Utilizing established experimental workflows, this variant showed increased LRRK2 kinase activity, by ~2-fold compared to wildtype and higher than the p.Gly2019Ser variant. Taken together, p.Arg1067Gln should be reclassified from a VUS to pathogenic for causing LRRK2 -PD.

The frequency and clinical impact of LRRK2 p.G2385R and p.R1628P risk variants in Parkinson’s disease (PD) remain uncertain, particularly across different Asian populations. We genotyped 3058 multi-ethnic Malaysian PD patients, performed detailed phenotyping in 185, and analyzed disease progression in 635 using longitudinal Clinical Impression of Severity Index for PD scores. p.G2385R was largely confined to Chinese (8.2%), while p.R1628P occurred in mixed ancestry (11.0%), Chinese (8.3%), Malays (7.7%), and is reported for the first time in indigenous groups (3.9%). Double-variant carriers had younger onset and more frequently had positive family history. Compared with non-carriers, p.R1628P carriers had lower rates of dementia and orthostatic hypotension, and slower progression of global PD severity. Our findings highlight ethnic differences in the distribution of LRRK2 Asian variants, and suggest that these variants influence onset age, familial occurrence, non-motor features, and disease course, with implications for personalized approaches to PD in Asian populations.

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. Multi-ancestry genome-wide association analyses identify new risk loci for Parkinson’s disease, and fine-mapping and co-localization analyses implicate candidate genes whose expression is associated with disease susceptibility.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods-Parkin ( ), Leucine Rich Repeat And Ig Domain Containing 2 ( ), Microtubule Associated Protein Tau ( ), and alpha-Synuclein ( )-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia.

Objective Dystonia is one of the most prevalent movement disorders, characterized by significant clinical and etiological heterogeneity. Despite considerable heritability (~25%), the etiology in most patients remains elusive. Moreover, understanding correlations between clinical manifestations and genetic variants has become increasingly complex. Methods Exome sequencing was conducted on 1924 genetically unsolved, mainly late‐onset isolated dystonia patients, recruited primarily from two dystonia registries (DysTract and the Dystonia Coalition). Rare variants in genes previously linked to dystonia (n = 406) were examined, confirmed via Sanger sequencing, and analyzed for segregation when possible. Results We identified 137 distinct likely pathogenic/pathogenic variants (according to ACMG criteria) across 51 genes in 163/1924 patients, including 153/1895 index patients (diagnostic yield 8.1%). The strongest predictors of a genetic diagnosis were generalized dystonia (28.6% yield) and age at onset (20.4% yield in patients with onset < 30 years). Notably, 56.2% of these variants were novel, with recurrent variants in EIF2AK2, VPS16, KCNMA1, and SLC2A1. Additionally, 321 index patients (16.9%) harbored variants of uncertain significance in 102 genes. The most frequently implicated genes included VPS16, THAP1, GCH1, SGCE, GNAL, and KMT2B. Presumably pathogenic variants in less well‐established dystonia genes were also found, including KCNMA1, KIF1A, and ZMYND11. At least six variants (in ADCY5, GNB1, IR2BPL, KCNN2, KMT2B, and VPS16) occurred de novo, supporting pathogenicity. Interpretation This study provides valuable insights into the genetic landscape of dystonia, underscores the utility of exome sequencing for diagnosis, substantiates several candidate genes, and expands the phenotypic spectrum of some genes to include prominent, sometimes isolated dystonia.

BackgroundGenome-wide association studies (GWAS) have shown that variants in the 3-methylcrotonyl-CoA carboxylase (MCCC1)/lysosome-associated membrane protein 3 (LAMP3) loci (rs10513789, rs12637471, rs12493050) reduce the risk of Parkinson’s disease (PD) in Caucasians, Chinese and Ashkenazi-Jews while the rs11248060 variant in the diacylglycerol kinase theta (DGKQ) gene increases the risk of PD in Caucasian and Han Chinese cohorts. However, their roles in Malays are unknown. Therefore, this study aims to investigate the association of these variants with the risk of PD in individuals of Malay ancestry.MethodsA total of 1114 subjects comprising of 536 PD patients and 578 healthy controls of Malay ancestry were recruited and genotyped using Taqman® allelic discrimination assays.ResultsThe G allele of rs10513789 (OR = 0.83, p = 0.001) and A allele of rs12637471 (OR = 0.79, p = 0.007) in the MCCC1/LAMP3 locus were associated with a protective effect against developing PD in the Malay population. A recessive model of penetrance showed a protective effect of the GG genotype for rs10513789 and the AA genotype for rs12637471. No association with PD was found with the other MCCC1/LAMP3 rs12493050 variant or with the DGKQ (rs11248060) variant. No significant associations were found between the four variants with the age at PD diagnosis.ConclusionMCCC1/LAMP3 variants rs10513789 and rs12637471 protect against PD in the Malay population.

Risk prediction models play a crucial role in advancing healthcare by enabling early detection and supporting personalized medicine. Nonetheless, polygenic risk scores (PRS) for Parkinson’s disease (PD) have not been extensively studied across diverse populations, contributing to health disparities. In this study, we constructed 105 PRS using individual-level data from seven ancestries and compared two different models. Model 1 was based on the cumulative effect of 90 known European PD risk variants, weighted by summary statistics from four independent ancestries (European, East Asian, Latino/Admixed American, and African/Admixed). Model 2 leveraged multi-ancestry summary statistics using a p -value thresholding approach to improve prediction across diverse populations. Our findings provide a comprehensive assessment of PRS performance across ancestries and highlight the limitations of a “one-size-fits-all” approach to genetic risk prediction. We observed variability in predictive performance between models, underscoring the need for larger sample sizes and ancestry-specific approaches to enhance accuracy. These results establish a foundation for future research aimed at improving generalizability in genetic risk prediction for PD.