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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 Expanding the phenome and variome of skeletal dysplasia

Maddirevula S, Alsahli S, Alhabeeb L, Patel N, Alzahrani F, Shamseldin HE, et al.

Genet Med, 2018 12;20(12):1609-1616.
PMID: 29620724 DOI: 10.1038/gim.2018.50

PURPOSE: To describe our experience with a large cohort (411 patients from 288 families) of various forms of skeletal dysplasia who were molecularly characterized.
METHODS: Detailed phenotyping and next-generation sequencing (panel and exome).
RESULTS: Our analysis revealed 224 pathogenic/likely pathogenic variants (54 (24%) of which are novel) in 123 genes with established or tentative links to skeletal dysplasia. In addition, we propose 5 genes as candidate disease genes with suggestive biological links (WNT3A, SUCO, RIN1, DIP2C, and PAN2). Phenotypically, we note that our cohort spans 36 established phenotypic categories by the International Skeletal Dysplasia Nosology, as well as 18 novel skeletal dysplasia phenotypes that could not be classified under these categories, e.g., the novel C3orf17-related skeletal dysplasia. We also describe novel phenotypic aspects of well-known disease genes, e.g., PGAP3-related Toriello-Carey syndrome-like phenotype. We note a strong founder effect for many genes in our cohort, which allowed us to calculate a minimum disease burden for the autosomal recessive forms of skeletal dysplasia in our population (7.16E-04), which is much higher than the global average.
CONCLUSION: By expanding the phenotypic, allelic, and locus heterogeneity of skeletal dysplasia in humans, we hope our study will improve the diagnostic rate of patients with these conditions.

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