CNV-Finder: Streamlining Copy Number Variation Discovery

Description

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.

Training and Networking Working Group

The Training and Networking working group promotes training and networking throughout the GP2 project.
Learn more about Training and Networking Working Group

Complex Disease - Data Analysis Working Group

The Data Analysis working group manages the maintenance, democratization, and acceleration of analyses, and provides analytical support for investigators with approved projects.
Learn more about Complex Disease - Data Analysis Working Group

Meet the authors

M.S. Student

Nicole Kuznetsov

NIH | USA

Postdoctoral fellow

Kensuke Daida, MD, PhD

National Institute of Aging | Bethesda, MD, USA

Biomedical Data Scientist, Contractor

Mary B Makarious, PhD

National Institutes of Health | Washington D.C., USA

Senior Clinical Scientist participating as a Cohort PI

Bashayer Al Mubarak, PhD

King Faisal Specialist Hospital and Research Center | Saudi Arabia

Postdoctoral research fellow, Visiting research fellow

Kajsa Atterling Brolin, PhD

Lund University, Queen Mary University of London | Lund, Sweden

PhD student/Neurological resident, Honorary Research Assistant

Miriam Ostrožovičová, MD

University of Pavol Jozef Šafárik, University College London | Kosice, Slovakia

PhD student, PhD student

Pin-Jui Kung, MSc

Genome and systems biology degree program, National Taiwan University | Taiwan

Neurology Consultant

Yasser Mecheri, MD,MSc

Centre Hospitalo-Universitaire Dr Benbadis Constantine | Constantine, Algeria