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Fulltext CNV-Finder: Streamlining Copy Number Variation Discovery

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.
Fulltext Understanding monogenic Parkinson's disease at a global scale

Junker J, Lange LM, Vollstedt EJ, Roopnarain K, Doquenia MLM, Annuar AA, et al.

medRxiv, 2024 Apr 09.
PMID: 38529492 DOI: 10.1101/2024.03.12.24304154

Until recently, about three-quarters of all monogenic Parkinson's disease (PD) studies were performed in European/White ancestry, thereby severely limiting our insights into genotype-phenotype relationships at global scale. The first systematic approach to embrace monogenic PD worldwide, The Michael J. Fox Foundation Global Monogenic PD (MJFF GMPD) Project, contacted authors of publications reporting individuals carrying pathogenic variants in known PD-causing genes. In contrast, the Global Parkinson's Genetics Program's (GP2) Monogenic Network took a different approach by targeting PD centers not yet represented in the medical literature. Here, we describe combining both efforts in a "merger project" resulting in a global monogenic PD cohort with build-up of a sustainable infrastructure to identify the multi-ancestry spectrum of monogenic PD and enable studies of factors modifying penetrance and expression of monogenic PD. This effort demonstrates the value of future research based on team science approaches to generate comprehensive and globally relevant results.
Fulltext GCH1 p.Ser80Asn Confers Risk for Parkinson's Disease in East Asian Populations

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.
Fulltext The Global Landscape of Genetic Variation in Parkinson's disease: Multi-Ancestry Insights into Established Disease Genes and their Translational Relevance

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.