Displaying all 2 publications

Abstract:
Sort:
  1. 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.
  2. 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.

Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links