Poster #73 - Lionel Sequeira

Evaluating Low-Pass Whole Genome Sequencing as a Cost-Effective Alternative to SNP Genotyping Arrays for Copy Number Variant Detection

Lionel Sequeira, BSc (1,2); Yue Xing, PhD (2); Jay A Tischfield, PhD (1,2); Gary A Heiman, PhD (1,2); Jinchuan Xing, PhD (1,2) 1 Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA 2 Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

Copy number variants (CNVs) are a significant source of genomic variation and are being increasingly implicated in a wide range of diseases. CNVs have been traditionally detected via SNP genotyping arrays; however, with plummeting sequencing costs, low-pass whole genome sequencing (LP-WGS) has emerged as a promising alternative for CNV detection. Notably, LP-WGS can be efficiently integrated into existing whole exome sequencing (WES) workflows - a "blended" genome-exome approach - adding genome-wide CNV detection with minimal extra cost. We present this study to evaluate whether LP-WGS, in conjunction with various CNV calling tools, can effectively replace SNP genotyping arrays for CNV identification. High-coverage genomes (~30x) from benchmark datasets (1000 Genomes) were subsampled to low coverage (2x-4x) and processed with multiple CNV callers - including read-depth based (CNVpytor, GATK-gCNV) and paired-end/split-read based (DELLY) methods. Each tool's CNV calls were compared to reference CNV sets to assess sensitivity and precision across CNV size ranges. Preliminary results indicate that each algorithm recovers a subset of true CNVs with good overlap, yet their call sets significantly differ from one another. Ongoing work aims to optimize parameters for each tool and employ consensus and machine learning approaches to enhance detection accuracy and sensitivity. Establishing a workflow for CNV detection by leveraging blended WES/LPWGS sequencing along with optimized tool combinations could enable a viable alternative to SNP genotyping arrays, expanding the accessibility of cytogenetic testing in research and clinical settings.