Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data...

Area of Science:

• Genomics
• Bioinformatics
• Human Genetics

Background:

• Rare copy number variants (CNVs) are significant genomic alterations impacting human health and disease susceptibility.
• Analyzing rare CNVs from SNP array data traditionally requires complex bioinformatic pipelines.
• There is a need for efficient and flexible tools to investigate rare CNVs.

Purpose of the Study:

• To develop and present a flexible bioinformatic pipeline for the automated detection and analysis of rare CNVs from human SNP array data.
• To provide a robust framework for researchers investigating the role of rare CNVs in human diseases.

Main Methods:

• The pipeline is implemented using Snakemake, a rule-based workflow management system.
• It comprises two main sub-pipelines: one for variant calling and quality control (QC), and another for rare CNV analysis.
• The framework is designed for automation, scalability, and flexibility.

Main Results:

• The pipeline automates the calling and quality control of CNVs.
• It enables the assessment of rare CNV frequencies in patient versus control cohorts.
• The system facilitates the evaluation of CNV impact on genes and biological pathways.

Conclusions:

• The developed pipeline offers an efficient and flexible bioinformatic solution for rare CNV investigation.
• It incorporates rigorous quality control and comparative frequency analysis for reliable results.
• This framework aims to advance biomedical research by simplifying rare CNV analysis.