Copy number variation profiling in pharmacogenes using panel-based exome resequencing and correlation to human liver...

Area of Science:

• Genomics
• Pharmacogenomics
• Bioinformatics

Background:

• Copy number variations (CNVs) are structural variants with significant implications in pharmacogenomics.
• Accurate detection of CNVs from next-generation sequencing (NGS) data remains a challenge.
• Understanding CNVs in drug-metabolizing genes is crucial for personalized medicine.

Purpose of the Study:

• To develop and validate a cohort-based workflow for detecting CNVs in a targeted panel of 340 absorption, distribution, metabolism, and excretion (ADME) genes.
• To identify and characterize CNVs in pharmacogenes using NGS data from human liver tissues.
• To correlate identified CNVs with mRNA expression levels to assess their functional impact.

Main Methods:

• Development of a cohort-based CNV detection workflow utilizing read counts from targeted NGS data.
• Application of the workflow to 150 human liver tissue samples.
• Correlation analysis between detected CNVs and corresponding mRNA expression levels.

Main Results:

• Identification of 445 deletions and 167 duplications across 36 pharmacogenes.
• Detection of known CNVs in CYPs, GSTs, SULTs, and UGTs, as well as rare CNVs in CYP2E1, SLC16A3, UGT2B15, and novel CNVs in SLC22A12, SLC22A17, and GPS2.
• Fine-mapping of complex CNVs in CYP2A6 and CYP2D6 at the exon level.
• Confirmation of known expression patterns for common CNVs and evidence of expression variability influence by rare CNVs.

Conclusions:

• The developed CNV detection workflow is effective for analyzing pharmacogenes in targeted NGS panels.
• The workflow facilitates the identification of both common and rare CNVs, enhancing genotype-phenotype correlations.
• This approach aids in understanding the functional relevance and clinical impact of CNVs in drug metabolism and response.