BackgroundRecently, the discovery of copy number variation (CNV) led researchers to think that there are more variations of genomic DNA than initially believed. Moreover, a certain CNV region has been found to be associated with the onset of diseases. Therefore, CNV is now known as an important genomic variation in biological mechanisms. However, most CNV studies have only involved the human genome. The study of CNV involving other animals, including cattle, is severely lacking.ResultsIn our study of cattle, we used Illumina BovineSNP50 BeadChip (54,001 markers) to obtain each marker's signal intensity (Log R ratio) and allelic intensity (B allele frequency), which led to our discovery of 855 bovine CNVs from 265 cows. For these animals, the average number of CNVs was 3.2, average size was 149.8 kb, and median size was 171.5 kb. Taking into consideration some overlapping regions among the identified bovine CNVs, 368 unique CNV regions were detected. Among them, there were 76 common CNVRs with > 1% CNV frequency. Together, these CNVRs contained 538 genes. Heritability errors of 156 bovine pedigrees and comparative pairwise analyses were analyzed to detect 448 common deletion polymorphisms. Identified variations in this study were successfully validated using visual examination of the genoplot image, Mendelian inconsistency, another CNV identification program, and quantitative PCR.ConclusionsIn this study, we describe a map of bovine CNVs and provide important resources for future bovine genome research. This result will contribute to animal breeding and protection from diseases with the aid of genomic information.

PurposeUGT1A1, UGT2B7, and UGT2B15 are well-known pharmacogenes that belong to the uridine diphosphate glucuronyltransferase gene family. For personalized drug treatment, it is important to study differences in the frequency of core markers across various ethnic groups. Accordingly, we screened single nucleotide polymorphisms (SNPs) of these three genes and analyzed differences in their frequency among five ethnic groups, as well as attempted to predict the function of novel SNPs.Materials and MethodsWe directly sequenced 288 subjects consisting of 96 Korean, 48 Japanese, 48 Han Chinese, 48 African American, and 48 European American subjects. Subsequently, we analyzed genetic variability, linkage disequilibrium (LD) structures and ethnic differences for each gene. We also conducted in silico analysis to predict the function of novel SNPs.ResultsA total of 87 SNPs were detected, with seven pharmacogenetic core SNPs and 31 novel SNPs. We observed that the frequencies of UGT1A1 *6 (rs4148323), UGT1A1 *60 (rs4124874), UGT1A1 *93 (rs10929302), UGT2B7 *2 (rs7439366), a part of UGT2B7 *3 (rs12233719), and UGT2B15 *2 (rs1902023) were different between Asian and other ethnic groups. Additional in silico analysis results showed that two novel promoter SNPs of UGT1A1 -690G>A and -689A>C were found to potentially change transcription factor binding sites. Moreover, 673G>A (UGT2B7), 2552T>C, and 23269C>T (both SNPs from UGT2B15) changed amino acid properties, which could cause structural deformation.ConclusionFindings from the present study would be valuable for further studies on pharmacogenetic studies of personalized medicine and drug response.

KCNQ1 polymorphisms shown to be associated with increased risk for T2DM in the recent GWA study might also represent genetic factors contributing to the development of GDM in Koreans.

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BackgroundUnlike 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 FindingsUsing 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, Pcorr = 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/SignificanceWe expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations.