Rare Copy Number Variant analysis in case-control studies using SNP Array Data: a scalable and automated data analysis pipeline
crossref(2024)
Abstract
Background Rare 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.
Results The pipeline performs two major tasks: (1) CNV detection and quality control, and (2) rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility.
Conclusions Our 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.
### Competing Interest Statement
The authors have declared no competing interest.
* CNV
: Copy number variation
SNP
: Single nucleotide polymorphism
GWAS
: Genome wide association study
LRR
: Log R Ratio
BAF
: B allele frequency
WF
: Waviness Factor
NumCNVs
: Number of called CNVs
PFB
: Population frequency of B allele
PCA
: Principal component analysis
MDS
: Multidimensional scaling
IBD
: Identity by descent
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