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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. 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.
  3. Tay YW, Lee AL, Schee JP, Lin CH, Tan EK, Shin JH, et al.
    Mov Disord, 2026 Oct 14.
    PMID: 42876073 DOI: 10.1002/mds.70530
    INTRODUCTION: GCH1 has been implicated in Parkinson's disease (PD), but its risks variants and associations are not well defined.

    OBJECTIVES: Our goal was to investigate the clinical relevance and PD risk associated with the GCH1 p.Ser80Asn variant.

    METHODS: We first identified a segregating GCH1 p.Ser80Asn variant in a Malaysian Chinese PD family via whole genome sequencing (WGS). We assessed its risk association using multi-ancestry WGS data from the Global Parkinson's Genetics Program (GP2) (n = 22,372PD vs. n = 8,826Controls) and meta-analysis of East Asian (EAS) cohorts (n = 4,712PD vs. 38,733Controls). Haplotype analyses were performed and clinico-demographic details of affected variant carriers were collated.

    RESULTS: The GCH1 p.Ser80Asn variant was enriched in GP2 EAS PD populations (n = 9/2,757; 0.33%), but not detected in other ancestries. Meta-analysis revealed increased PD risk in EAS populations (odds ratio = 5.1; 95% confidence intervals [CI] = 2.3-10.7; P = 2.89 × 10-5). A shared haplotype (~330 kbp) was identified among variant carriers. Affected carriers (mean age at onset: 56.3 ± 12.5 years) had additional occurrence of dystonia, whereas dementia was rare.

    CONCLUSIONS: The GCH1 p.Ser80Asn variant is a rare, EAS-enriched risk variant for PD. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

  4. Tay YW, Lee AL, Schee JP, Lin CH, Tan EK, Shin JH, et al.
    medRxiv, 2026 Jun 22.
    PMID: 42396327 DOI: 10.64898/2026.06.11.26354827
    INTRODUCTION: GCH1 has been implicated in Parkinson's disease (PD), but its risks variants and associations are not well defined.

    OBJECTIVES: To investigate the clinical relevance and PD risk associated with the GCH1 p.Ser80Asn variant.

    METHODS: We first identified a segregating GCH1 p.Ser80Asn variant in a Malaysian Chinese PD family via whole genome sequencing (WGS). We assessed its risk association using multi-ancestry WGS data from the Global Parkinson's Genetics Program (GP2) (n=22,372PD vs n=8,826Controls) and meta-analysis of East Asian (EAS) cohorts (n=4,712PD vs 38,733Controls). Clinico-demographic details of affected variant carriers were collated.

    RESULTS: The GCH1 p.Ser80Asn variant was enriched in GP2 EAS PD populations (n=9/2,757; 0.33%) but not detected in other ancestries. Meta-analysis revealed increased PD risk in EAS populations (odds ratio:5.1; 95%CI:2.3-10.7; p=2.89×10-5). Affected carriers (mean age at onset:56.3±12.5 years) had additional occurrence of dystonia, while dementia was rare.

    CONCLUSIONS: The GCH1 p.Ser80Asn variant is a rare, EAS-enriched risk variant for PD.

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