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  1. Step K, Eltaraifee E, Elsayed I, Rasaholiarison N, Okubadejo N, Walker R, et al.
    Mov Disord, 2025 Jan;40(1):51-56.
    PMID: 39482233 DOI: 10.1002/mds.30051
  2. Mohamed W, Eltantawi MA, Agarwal V, Bandres-Ciga S, Makarious MB, Mecheri Y, et al.
    J Integr Neurosci, 2024 Aug 19;23(8):152.
    PMID: 39207075 DOI: 10.31083/j.jin2308152
    Over 80% of genetic studies in the Parkinson's disease (PD) field have been conducted on individuals of European descent. There is a social and scientific imperative to understand the genetic basis of PD across global populations for therapeutic development and deployment. PD etiology is impacted by genetic and environmental factors that are variable by ancestry and region, emphasising the need for worldwide programs to gather large numbers of patients to identify novel candidate genes and risk loci involved in disease. Only a handful of documented genetic assessments have investigated families with PD in AfrAbia, which comprises the member nations of the Arab League and the African Union, with very limited cohort and case-control studies reported. This review article summarises prior research on PD genetics in AfrAbia, highlighting gaps and challenges. We discuss the etiological risk spectrum in the context of historical interactions, highlighting allele frequencies, penetrance, and the clinical manifestations of known genetic variants in the AfrAbian PD patient community.
  3. Kuznetsov N, Daida K, Makarious MB, Al-Mubarak B, Brolin KA, Malik L, et al.
    bioRxiv, 2024 Nov 23.
    PMID: 39605431 DOI: 10.1101/2024.11.22.624040
    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 of significant importance 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 the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), alpha-Synuclein (SNCA), and Amyloid Beta Precursor Protein (APP)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), 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 both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. 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.
  4. Lim SY, Tan AH, Ahmad-Annuar A, Okubadejo NU, Lohmann K, Morris HR, et al.
    Lancet Neurol, 2024 Dec;23(12):1267-1280.
    PMID: 39447588 DOI: 10.1016/S1474-4422(24)00378-8
    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.
  5. Lange LM, Fang ZH, Makarious MB, Kuznetsov N, Atterling Brolin K, Ballard S, et al.
    Lancet Neurol, 2026 Aug;25(8):741-754.
    PMID: 42456684 DOI: 10.1016/S1474-4422(26)00198-5
    BACKGROUND: The genetic architecture of Parkinson's disease varies considerably across ancestries, yet most previous genetic studies have focused on individuals of European ancestry. We aimed to characterise the distribution of established Parkinson's disease causal variants, as well as risk-associated variants with clinical implications (ie, variants in genes involved in pathways targeted by ongoing clinical trials), across ancestrally diverse populations.

    METHODS: We conducted a multi-ancestry, observational, cross-sectional genetic study using retrospective data from the Global Parkinson's Genetics Program (GP2) release 11 (released in December, 2025). The study investigated causal and risk variants, including copy number variants, in established Parkinson's disease and parkinsonism-associated genes, following the recommendations of the Movement Disorder Society (MDS) Task Force on the Nomenclature of Genetic Movement Disorders, including GBA1, LRRK2, SNCA, VPS35, RAB32, PINK1, PRKN, PARK7, ATP13A2, DCTN1, DNAJC6, FBXO7, JAM2, RAB39B, SLC20A2, SYNJ1, VPS13C, and WDR45. Individuals with Parkinson's disease were diagnosed based on established clinical criteria, including the Parkinson's UK Brain Bank or MDS diagnostic criteria (or both), and healthy control participants were defined as individuals without evidence of neurodegenerative disease and unrelated to participants with Parkinson's disease. We analysed genome and exome sequencing and array genotyping data of 99 783 individuals, including 58 559 individuals with Parkinson's disease and 41 224 controls, from 11 genetically inferred ancestries (African, African admixed, Ashkenazi Jewish, Latino and Indigenous people of the Americas, central Asian, complex admixture, east Asian, European, Finnish, Middle Eastern, and south Asian), defined using reference population-based ancestry inference methods. We calculated allele frequencies for all investigated variants in individuals with Parkinson's disease and controls, both overall and stratified by ancestry.

    FINDINGS: Approximately 29% of individuals (29 001 of 99 783; 15 443 [26·4%] of 58 559 individuals with Parkinson's disease and 13 558 [32·9%] of 41 224 controls) were from under-represented populations (ie, non-European and non-Ashkenazi Jewish). Our findings indicated both shared genetic contributors across ancestries as well as ancestry-specific differences in variant frequencies and the spectrum of variants within Parkinson's disease-associated genes. Overall, 1217 (2·1%) of 58 559 individuals with Parkinson's disease carried a causal variant, with substantial variations across ancestries ranging from ten (0·4%) of 2844 African individuals to 251 (10·7%) of 2343 individuals of Ashkenazi Jewish ancestry. Risk variants in GBA1 and LRRK2 were identified in 6893 (11·8%) of 58 559 individuals with Parkinson's disease and 3578 (8·7%) of 41 224 controls. GBA1 risk variants were most frequent overall and identified across all ancestries, but variant frequency and spectra differed substantially between ancestries, from 195 (4·1%) of 4773 in the east Asian ancestry group to 1505 (52·9%) of 2844 in the African ancestry group. Similarly, LRRK2 causal and risk variants showed ancestry-specific enrichment, with the highest frequencies of causal variants in the Ashkenazi Jewish (250 [10·7%] of 2343) and Middle Eastern (59 [4·4%] of 1347) ancestry groups, whereas risk variants were predominantly identified in the east Asian ancestry group (601 [12·6%] of 4773). Carriers of biallelic causal variants in PRKN, commonly including deletions and duplications, were also identified across all ancestries except Ashkenazi Jewish; the highest frequency was in the Middle Eastern ancestry group (17 [1·3%] of 1347), and frequencies in all other ancestries were less than 1%.

    INTERPRETATION: This large-scale, multi-ancestry genetic study offers crucial insights into the population-specific genetic architecture of Parkinson's disease. Whereas clinical trials targeting GBA1 and LRRK2 variant carriers are primarily performed in Europe and the USA, increased ancestral diversity in Parkinson's disease research will be crucial to improve diagnostic accuracy, enhance our understanding of disease mechanisms across populations, and ensure equitable application of and access to emerging genetically informed therapies.

    FUNDING: Aligning Science Across Parkinson's (ASAP) through the Global Parkinson's Genetics Program (GP2).

  6. Lange LM, Fang ZH, Makarious MB, Kuznetsov N, Brolin KA, Ballard S, et al.
    medRxiv, 2025 Jul 11.
    PMID: 40672482 DOI: 10.1101/2025.07.08.25330815
    BACKGROUND: The genetic architecture of Parkinson's disease (PD) varies considerably across ancestries, yet most genetic studies have focused on individuals of European descent, limiting our insights into the genetic architecture of PD at a global scale.

    METHODS: We conducted a large-scale, multi-ancestry investigation of causal and risk variants in PD-related genes. Using genetic datasets from the Global Parkinson's Genetics Program, we analyzed sequencing and genotyping data from 69,881 individuals, including 41,139 affected and 28,742 unaffected, from eleven different ancestries, including ~30% of individuals from non-European ancestries.

    FINDINGS: Our findings revealed shared and ancestry-specific patterns in the prevalence and spectrum of PD-associated variants. Overall, ~2% of affected individuals carried a causative variant, with substantial variations across ancestries ranging from <0·5% in African, African-admixed, and Central Asian to >10% in Middle Eastern and Ashkenazi Jewish ancestries. Including disease-associated GBA1 and LRRK2 risk variants raised the yield to ~12.5%, largely driven by GBA1, except in East Asians, where LRRK2 risk variants dominated. GBA1 variants were most frequent globally, albeit with substantial differences in frequencies and variant spectra. While GBA1 variants were identified across all ancestries, frequencies ranged from 3·4% in Middle Eastern to 51·7% in African ancestry. Similarly, LRRK2 variants showed ancestry-specific enrichment, with G2019S most frequently seen in Middle Eastern and Ashkenazi Jewish, and risk variants predominating in East Asians. However, clinical trials targeting proteins encoded by these genes are primarily based in Europe and North America.

    INTERPRETATION: This large-scale, multi-ancestry assessment offers crucial insights into the population-specific genetic architecture of PD. It underscores the critical need for increased diversity in PD genetic research to improve diagnostic accuracy, enhance our understanding of disease mechanisms across populations, and ensure the equitable development and application of emerging precision therapies.

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