Abstract
For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.
Highlights
It is well established that human genetic diversity is important for our understanding population histology [1], variability in disease susceptibility, and treatment response or adverse reactions to medications [2]
Genome-wide association (GWA) studies have led to the identification of multiple genetic variants correlated with traits, such as body mass index, skin color [3], fat distribution [4], and glomerular filtration rate [5], and with diseases, such as autoimmune disease [6] and non-alcoholic fatty liver disease [7]
For the copy number variations (CNVs) data, 945 subjects remained after excluding patients with cancer
Summary
It is well established that human genetic diversity is important for our understanding population histology [1], variability in disease susceptibility, and treatment response or adverse reactions to medications [2]. Genome-wide association (GWA) studies have led to the identification of multiple genetic variants correlated with traits, such as body mass index, skin color [3], fat distribution [4], and glomerular filtration rate [5], and with diseases, such as autoimmune disease [6] and non-alcoholic fatty liver disease [7]. These SNV markers from GWA studies can be used in pharmacogenomic research as a means of directly predicting interindividual responses to medicines [8]. These genetic variations alter the structure and function of proteins such as drug-metabolizing enzymes, drug transporters, receptors, and response targets, collectively referred to as pharmacogenes [15]
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