The genetic effect of copy number variations on the risk of type 2 diabetes in a Korean population

Abstract

Background: Unlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes-associated CNV in a Korean cohort.

Methodology/principal findings: Using the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758-45999227 (P = 8.6E-04, P(corr) = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation.

Conclusion/significance: We expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations.

Figure 1. Copy number variation validation by qPCR around rs682567 within chr15:45994758–45999227.

(A) Genoplot image of the identified deletions (marker name: rs682567). Genoplot image represents allelic intensity (X-axis) and signal intensity (Y-axis) of all samples. Two types of copy numbers are depicted as 2× and 1×. Individuals having hemizygous deletions (copy number: 1×) are clustered as two distinct groups (color: yellow). Samples having null copy numbers are displayed with a black dot at the bottom. (B) Validation by qPCR around the rs682567 within chr15: 45994758–45999227. The value of the X-axis (expected copy number) is estimated by the Illumina Genoplot image analysis. The Y-axis indicates the determined copy number by qPCR. The copy number value estimated through visual examination is matched with the quantitative measurement value by qPCR.

Figure 2. Map of identified common copy number variation and T2DM-associated regions.

This figure shows common CNVRs (freq.>1%, red-colored rectangle) and highly common CNVRs (freq.>5%, blue colored triangle) from CNVs identified in this study. Putative diseases associated CNVRs are marked by a green-colored triangle. The location of each CNVR was displayed by the Karyoview of Ensembl (http://apr2006.archive.ensembl.org/Homo_sapiens/karyoview) according to our previous method .