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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.
Team Science Approaches to Unravel Monogenic Parkinson's Disease on a Global Scale

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

Mov Disord, 2024 Jul 30.
PMID: 39076159 DOI: 10.1002/mds.29925

BACKGROUND: 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 a global scale.
OBJECTIVE: To identify the multi-ancestry spectrum of monogenic PD.
METHODS: The first systematic approach to embrace monogenic PD worldwide, The Michael J. Fox Foundation Global Monogenic PD Project, contacted authors of publications reporting individuals carrying pathogenic variants in known PD-causing genes. In contrast, the Global Parkinson's Genetics Program's Monogenic Network took a different approach by targeting PD centers underrepresented or not yet represented in the medical literature.
RESULTS: In this article, we describe combining both efforts in a merger project resulting in a global monogenic PD cohort with the buildup of a sustainable infrastructure to identify the multi-ancestry spectrum of monogenic PD and enable studies of factors modifying penetrance and expressivity of monogenic PD.
CONCLUSIONS: This effort demonstrates the value of future research based on team science approaches to generate comprehensive and globally relevant results. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Fulltext Identification of Risk Loci for Parkinson Disease in Asians and Comparison of Risk Between Asians and Europeans: A Genome-Wide Association Study

Foo JN, Chew EGY, Chung SJ, Peng R, Blauwendraat C, Nalls MA, et al.

JAMA Neurol, 2020 06 01;77(6):746-754.
PMID: 32310270 DOI: 10.1001/jamaneurol.2020.0428

Importance: Large-scale genome-wide association studies in the European population have identified 90 risk variants associated with Parkinson disease (PD); however, there are limited studies in the largest population worldwide (ie, Asian).
Objectives: To identify novel genome-wide significant loci for PD in Asian individuals and to compare genetic risk between Asian and European cohorts.
Design Setting, and Participants: Genome-wide association data generated from PD cases and controls in an Asian population (ie, Singapore/Malaysia, Hong Kong, Taiwan, mainland China, and South Korea) were collected from January 1, 2016, to December 31, 2018, as part of an ongoing study. Results were combined with inverse variance meta-analysis, and replication of top loci in European and Japanese samples was performed. Discovery samples of 31 575 individuals passing quality control of 35 994 recruited were used, with a greater than 90% participation rate. A replication cohort of 1 926 361 European-ancestry and 3509 Japanese samples was analyzed. Parkinson disease was diagnosed using UK Parkinson's Disease Society Brain Bank Criteria.
Main Outcomes and Measures: Genotypes of common variants, association with disease status, and polygenic risk scores.
Results: Of 31 575 samples identified, 6724 PD cases (mean [SD] age, 64.3 [10] years; age at onset, 58.8 [10.6] years; 3472 [53.2%] men) and 24 851 controls (age, 59.4 [11.4] years; 11 030 [45.0%] men) were analyzed in the discovery study. Eleven genome-wide significant loci were identified; 2 of these loci were novel (SV2C and WBSCR17) and 9 were previously found in Europeans. Replication in European-ancestry and Japanese samples showed robust association for SV2C (rs246814; odds ratio, 1.16; 95% CI, 1.11-1.21; P = 1.17 × 10-10 in meta-analysis of discovery and replication samples) but showed potential genetic heterogeneity at WBSCR17 (rs9638616; I2=67.1%; P = 3.40 × 10-3 for hetereogeneity). Polygenic risk score models including variants at these 11 loci were associated with a significant improvement in area under the curve over the model based on 78 European loci alone (63.1% vs 60.2%; P = 6.81 × 10-12).
Conclusions and Relevance: This study identified 2 apparently novel gene loci and found 9 previously identified European loci to be associated with PD in this large, meta-genome-wide association study in a worldwide population of Asian individuals and reports similarities and differences in genetic risk factors between Asian and European individuals in the risk for PD. These findings may lead to improved stratification of Asian patients and controls based on polygenic risk scores. Our findings have potential academic and clinical importance for risk stratification and precision medicine in Asia.
Exome sequencing in Asian populations identifies low-frequency and rare coding variation influencing Parkinson's disease risk

Chew EG, Liu Z, Li Z, Chung SJ, Lian MM, Tandiono M, et al.

Nat Aging, 2024 Nov 21.
PMID: 39572736 DOI: 10.1038/s43587-024-00760-7

Parkinson's disease (PD) is an incurable, progressive and common movement disorder that is increasing in incidence globally because of population aging. We hypothesized that the landscape of rare, protein-altering variants could provide further insights into disease pathogenesis. Here we performed whole-exome sequencing followed by gene-based tests on 4,298 PD cases and 5,512 controls of Asian ancestry. We showed that GBA1 and SMPD1 were significantly associated with PD risk, with replication in a further 5,585 PD cases and 5,642 controls. We further refined variant classification using in vitro assays and showed that SMPD1 variants with reduced enzymatic activity display the strongest association (<44% activity, odds ratio (OR) = 2.24, P = 1.25 × 10-15) with PD risk. Moreover, 80.5% of SMPD1 carriers harbored the Asian-specific p.Pro332Arg variant (OR = 2.16; P = 4.47 × 10-8). Our findings highlight the utility of performing exome sequencing in diverse ancestry groups to identify rare protein-altering variants in genes previously unassociated with disease.