Rare Copy Number Variant Analysis in Case-Control Studies Using SNP Array Data: a Scalable and Automated Data Analysis Pipeline

Haydee Artaza,Ksenia Lavrichenko,Anette S. B. Wolff,Ellen C. Røyrvik,Marc Vaudel,Stefan Johansson

BMC Bioinform（2024）

University of Bergen | Norwegian Institute of Public Health

Cited 0|Views21

Abstract

BackgroundRare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data.ResultsThe pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility.ConclusionsOur pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

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Key words

Copy number variant (CNV),Calls detection,Quality control,Burden analysis,Enrichment analysis,Rare variants analysis,Snakemake

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