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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
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Analysis of population-specific pharmacogenomic variants using next-generation sequencing data
Eunyong Ahn1,2, Taesung Park3,4
1Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, 151-747, Korea.
Scientific Reports
|September 6, 2017
Summary
This study introduces a new method, Population Differentiation of Rare and Common variants (PDRC), to analyze genetic variations in drug response genes. PDRC effectively identifies highly differentiated pharmacogenes across diverse populations.
Area of Science:
- Population genetics
- Pharmacogenomics
- Statistical genetics
Background:
- Rare variants in drug-related genes show significant ethnic differentiation.
- Limited understanding of population differentiation (PD) for rare single-nucleotide variants (SNVs).
- Existing methods inadequately leverage fixation indices (Fst) for gene-level PD analysis.
Purpose of the Study:
- To develop a novel gene-based PD method for analyzing rare variants.
- To identify highly population-differentiated drug response genes (pharmacogenes).
- To assess the method's performance using simulations and real-world data.
Main Methods:
- Proposed a new gene-based PD method: Population Differentiation of Rare and Common variants (PDRC).
- PDRC is inspired by Generalized Cochran-Mantel-Haenszel (GCMH) statistics.
- Applied PDRC to whole-exome sequencing data from T2D-GENES and Go-T2D initiatives.
Main Results:
- Simulation studies confirmed PDRC's ability to summarize PD of rare and common variants within genes.
- Real-world data analysis identified highly population-differentiated pharmacogenes.
- Identified key genes including ACE, CYP2B6, DPYD, F5, MTHFR, and SCN5A among VIPgenes.
Conclusions:
- PDRC is an effective method for identifying population-differentiated pharmacogenes.
- The study highlights significant PD in important drug response genes.
- Findings contribute to understanding genetic diversity in pharmacogenetics and precision medicine.
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