• Genomics & Informatics
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• v.10 no.4
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• pp.220-225
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• 2012

Recent rapid advances in genetic research are ushering us into the genome sequence era, where an individual's genome information is utilized for clinical practice. The most spectacular results of the human genome study have been provided by genome-wide association studies (GWASs). This is a review of the history of GWASs as related to my work. Further efforts are necessary to make full use of its potential power to medicine.

• Genes and Genomics
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• v.40 no.12
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• pp.1331-1338
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• 2018

Although there have been plenty of dominance deviation analysis, few studies have dealt with multiple phenotypes. Because researchers focused on multiple phenotypes (final weight and backfat thickness) of Landrace pigs, the classification of the genes was possible. With genome-wide association studies (GWASs), we analyzed the additive and dominance effects of the single nucleotide polymorphisms (SNPs). The classification of the pig genes into four categories (overdominance in final weight, overdominance in backfat thickness and overdominance in final weight, underdominance in backfat thickness, etc.) can enable us not only to analyze each phenotype's dominant effects, but also to illustrate the gene ontology (GO) analysis with different aspects. We aimed to determine the additive and dominant effect in backfat thickness and final weight and performed GO analysis. Using additive model and dominance deviation analysis in GWASs, Landrace pigs' overdominant and underdominant SNP effects in final weight and backfat thickness were surveyed. Then through GO analysis, we investigated the genes that were classified in the GWASs. The major GO terms of the underdominant effects in final weight and overdominant effects in backfat thickness were ion transport with the SLC8A3, KCNJ16, P2RX7 and TRPC3 genes. Interestingly, the major GO terms in the underdominant effects in the final weight and the underdominant effects in the backfat thickness were the regulation of ion transport with the STAC, GCK, TRPC6, UBASH3B, CAMK2D, CACNG4 and SCN4B genes. These results demonstrate that ion transport and ion transport regulation genes have distinct dominant effects. Through GWASs using the mode of linear additive model and dominance deviation, overdominant effects and underdominant effects in backfat thickness was contrary to each other in GO terms (ion transport and ion transport regulation, respectively). Additionally, because ion transport and ion transport regulation genes are associative with adipose tissue accumulation, we could infer that these two groups of genes had to do with unique fat accumulation mechanisms in Landrace pigs.

• Genomics & Informatics
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• v.12 no.3
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• pp.114-120
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• 2014

Bone mineral density (BMD) is one of the quantitative traits that are genetically inherited and affected by various factors. Over the past years, genome-wide association studies (GWASs) have searched for many genetic loci that influence BMD. A recent meta-analysis of 17 GWASs for BMD of the femoral neck and lumbar spine is the largest GWAS for BMD to date and offers 64 single-nucleotide polymorphisms (SNPs) in 56 associated loci. We investigated these BMD loci in a Korean population called Korea Association REsource (KARE) to identify their validity in an independent study. The KARE population contains genotypes from 8,842 individuals, and their BMD levels were measured at the distal radius (BMD-RT) and midshaft tibia (BMD-TT). Thirteen genomic loci among 56 loci were significantly associated with BMD variations, and 3 loci were involved in known biological pathways related to BMD. In order to find putative functional variants, nearby SNPs in relation to linkage equilibrium were annotated, and their possible functional effects were predicted. These findings reveal that tens of variants, not a single factor, may contribute to the genetic architecture of BMD; have an important role regardless of ethnic group; and may highlight the importance of a replication study in GWASs to validate genuine loci for BMD variation.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Genomics & Informatics
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• v.19 no.4
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• pp.37.1-37.7
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• 2021

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East Asian T2D summary statistics, and the linkage disequilibrium among the variants (r² < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.123-127
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• 2014

A recent study summarized several published genome-wide association studies (GWASs) of cancer and reported two pleiotropic loci at 6p21.1 and 7p15.3 contributing to multiple cancers including lung cancer, noncardia gastric cancer (NCGC), and esophageal squamous-cell carcinoma (ESCC) in Han Chinese. However, it is not known whether such genetic variants have similar effects on the risk of gynecologic cancers, such as ovarian cancer. Hence, we explored associations between genetic variants in 6p21.1 and 7p15.3 and ovarian cancer risk in Han Chinese women. We performed an independent case-control study by genotyping the two loci (rs2494938 A > G at 6p21.1 and rs2285947 A > G at 7p15.3) in a total of 377 ovarian cancer cases and 1,034 cancer-free controls using TaqMan allelic discrimination assay. We found that rs2285947 at 7p15.3 was significantly associated with risk of ovarian cancer with per allele odds ratio (OR) of 1.33 [95% confidence interval (CI): 1.08-1.64, P=0.008]. However, no significant association was observed between rs2494938 and ovarian cancer risk. Our results showed that rs2285947 at 7p15.3 may also contribute to the development of ovarian cancer in Han Chinese women, further suggesting pleiotropy of 7p15.3 in multiple cancers.

• Genomics & Informatics
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• v.10 no.4
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• pp.256-262
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• 2012

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.

• Genomics & Informatics
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• v.16 no.3
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• pp.59-64
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• 2018

Although pork quality traits are important commercially, genome-wide association studies (GWASs) have not well considered Landrace and Yorkshire pigs worldwide. Landrace and Yorkshire pigs are important pork-providing breeds. Although quantitative trait loci of pigs are well-developed, significant genes in GWASs of pigs in Korea must be studied. Through a GWAS using the PLINK program, study of the significant genes in Korean pigs was performed. We conducted a GWAS and surveyed the gene ontology (GO) terms associated with the backfat thickness (BF) trait of these pigs. We included the breed information (Yorkshire and Landrace pigs) as a covariate. The significant genes after false discovery rate (<0.01) correction were AFG1L, SCAI, RIMS1, and SPDEF. The major GO terms for the top 5% of genes were related to neuronal genes, cell morphogenesis and actin cytoskeleton organization. The neuronal genes were previously reported as being associated with backfat thickness. However, the genes in our results were novel, and they included ZNF280D, BAIAP2, LRTM2, GABRA5, PCDH15, HERC1, DTNBP1, SLIT2, TRAPPC9, NGFR, APBB2, RBPJ, and ABL2. These novel genes might have roles in important cellular and physiological functions related to BF accumulation. The genes related to cell morphogenesis were NOX4, MKLN1, ZNF280D, BAIAP2, DNAAF1, LRTM2, PCDH15, NGFR, RBPJ, MYH9, APBB2, DTNBP1, TRIM62, and SLIT2. The genes that belonged to actin cytoskeleton organization were MKLN1, BAIAP2, PCDH15, BCAS3, MYH9, DTNBP1, ABL2, ADD2, and SLIT2.

• Genomics & Informatics
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• v.12 no.4
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• pp.195-202
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• 2014

Metabolic syndrome (MetS) is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs), important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs), explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE) was used for analysis, which include 8,842 individuals (age, $52.2{\pm}8.9years$ ; body mass index, $24.6{\pm}3.2kg/m^2$). A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA) to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < $5{\times}10^{-6}$), and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < $1.38{\times}10^{-7}$, Bonferroni-adjusted p < 0.05). Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF), the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR) signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.