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Abstract Background Pathogenic variants of MYH7, which encodes the beta-myosin heavy chain protein, are major causes of dilated and hypertrophic cardiomyopathy. Methods In this study, we used whole-genome sequencing data to identify MYH7 variants in 397 patients with various cardiomyopathy subtypes who were participating in the National Project of Bio Big Data pilot study in Korea. We also performed in silico analyses to predict the pathogenicity of the novel variants, comparing them to known pathogenic missense variants. Results We identified 27 MYH7 variants in 41 unrelated patients with cardiomyopathy, consisting of 20 previously known pathogenic/likely pathogenic variants, 2 variants of uncertain significance, and 5 novel variants. Notably, the pathogenic variants predominantly clustered within the myosin motor domain of MYH7. We confirmed that the novel identified variants could be pathogenic, as indicated by high prediction scores in the in silico analyses, including SIFT, Mutation Assessor, PROVEAN, PolyPhen-2, CADD, REVEL, MetaLR, MetaRNN, and MetaSVM. Furthermore, we assessed their damaging effects on protein dynamics and stability using DynaMut2 and Missense3D tools. Conclusions Overall, our study identified the distribution of MYH7 variants among patients with cardiomyopathy in Korea, offering new insights for improved diagnosis by enriching the data on the pathogenicity of novel variants using in silico tools and evaluating the function and structural stability of the MYH7 protein.

Abstract Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.

Conflicting findings have been reported regarding the association between Agent Orange (AO) exposure and type 2 diabetes. This study aimed to examine whether AO exposure is associated with the development of type 2 diabetes and to verify the causal relationship between AO exposure and type 2 diabetes by combining DNA methylation with DNA genotype analyses. An epigenome-wide association study and DNA genotype analyses of the blood of AO-exposed and AO-unexposed individuals with type 2 diabetes and that of healthy controls were performed. Methylation quantitative trait locus and Mendelian randomisation analyses were performed to evaluate the causal effect of AO-exposure-identified CpGs on type 2 diabetes. AO-exposed individuals with type 2 diabetes were associated with six hypermethylated CpG sites (cg20075319, cg21757266, cg05203217, cg20102280, cg26081717, and cg21878650) and one hypo-methylated CpG site (cg07553761). Methylation quantitative trait locus analysis showed the methylation levels of some CpG sites (cg20075319, cg20102280, and cg26081717) to be significantly different. Mendelian randomisation analysis showed that CpG sites that were differentially methylated in AO-exposed individuals were causally associated with type 2 diabetes; the reverse causal effect was not significant. These findings reflect the need for further epigenetic studies on the causal relationship between AO exposure and type 2 diabetes.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, caused by a complex interplay of genetic and environmental factors. This study aimed to evaluate the combined efficacy of multi-polygenic risk scores and pooled cohort equations (PCE) for predicting future CVD risks in the Korean population. In this longitudinal study, 7,612 individuals from the Ansan and Ansung cohorts were analyzed over a 17-year follow-up period. The participants were genotyped using the Korea Biobank Array, and quality-controlled genetic data were subjected to imputation analysis. The weighted sum of the PRSs (wPRSsum) was calculated using PRS-CS with summary statistics from myocardial infarction, ischemic stroke, coronary artery disease, and hypertension genome-wide association studies. The recalibrated PCE was used to assess clinical risk, and the participants were stratified into risk groups based on the wPRSsum and PCE. Associations between these risk scores and incident CVD were evaluated using Cox proportional hazards models and Kaplan–Meier analysis. The wPRSsum approach showed a significant association with incident CVD (HR = 1.15, p = 7.49 × 10−5), and the top 20% high-risk genetic group had an HR of 1.50 (p = 5.04 × 10−4). The recalibrated PCE effectively differentiated between the low and high 10-year CVD risk groups, with a marked difference in survival rates. The predictive models constructed using the wPRSsum and PCE demonstrated a slight improvement in prediction accuracy, particularly among males aged

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Metabolic traits are heritable intermediate phenotypes widely used in assessing the risk of various diseases. By conducting a genome-wide meta-analysis for metabolic traits in 288,137 East Asians, the authors highlight the interplay of common and rare variants on inherited risk of metabolic traits.

Genotype imputation is essential for enhancing the power of association-mapping and discovering rare and indels that are missed by most genotyping arrays. Imputation analysis can be more accurate with a population-specific reference panel or a multi-ethnic reference panel with numerous samples. The National Institute of Health, Republic of Korea, initiated the Korean Reference Genome (KRG) project to identify variants in whole-genome sequences of ∼20,000 Korean participants. In the pilot phase, we analyzed the data from 1,490 participants. The genetic characteristics and imputation performance of the KRG were compared with those of the 1,000 Genomes Project Phase 3, GenomeAsia 100K Project, ChinaMAP, NARD, and TOPMed reference panels. For comparison analysis, genotype panels were artificially generated using whole-genome sequencing data from combinations of four different ancestries (Korean, Japanese, Chinese, and European) and two population-specific optimized microarrays (Korea Biobank Array and UK Biobank Array). The KRG reference panel performed best for the Korean population (R2 = 0.78–0.84, percentage of well-imputed is 91.9% for allele frequency >5%), although the other reference panels comprised a larger number of samples with genetically different background. By comparing multiple reference panels and multi-ethnic genotype panels, optimal imputation was obtained using reference panels from genetically related populations and a population-optimized microarray. Indeed, the reference panels of KRG and TOPMed showed the best performance when applied to the genotype panels of KBA (R2 = 0.84) and UKB (R2 = 0.87), respectively. Using a meta-imputation approach to merge imputation results from different reference panels increased the imputation accuracy for rare variants (∼7%) and provided additional well-imputed variants (∼20%) with comparable imputation accuracy to that of the KRG. Our results demonstrate the importance of using a population-specific reference panel and meta-imputation to assess a substantial number of accurately imputed rare variants.

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.

Abstract Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m2, 0.79 vs. 0.58, P

Abstract Background Obesity is a chronic low-grade inflammatory disease that is generally characterized by enhanced inflammation in obese adipose tissue (AT). Here, we investigated alterations in gene expression between lean and obese conditions using mRNA-Seq data derived from human purified adipocytes (ACs) and preadipocytes (preACs). Results Total mRNA-seq data were generated with 27 AC and 21 preAC samples purified from human visceral AT collected during resection surgery in cancer patients, where the samples were classified into lean and obese categories by BMI > 25 kg/m2. We defined four classes of differentially expressed genes (DEGs) by comparing gene expression between (1) lean and obese ACs, (2) lean and obese preACs, (3) lean ACs and lean preACs, and 4) obese ACs and obese preACs. Based on an analysis of comparison 1, numerous canonical obesity-related genes, particularly inflammatory genes including IL-6, TNF-α and IL-1β, i.e., the genes that are expected to be upregulated in obesity conditions, were found to be expressed at significantly lower levels in obese ACs than in lean ACs. In contrast, some inflammatory genes were found to be expressed at higher levels in obese preACs than lean preACs in the analysis of comparison 2. The analysis of comparisons 3 and 4 showed that inflammatory gene classes were expressed at higher levels in differentiated ACs than undifferentiated preACs under both lean and obese conditions; however, the degree of upregulation was significantly greater for lean than for obese conditions. We validated our observations using previously published microarray transcriptome data deposited in the GEO database (GSE80654). Conclusions Taken together, our analyses suggest that inflammatory genes are expressed at lower levels in obese ACs than in lean ACs because lean adipogenesis involves even greater enhancement of inflammatory responses than does obese adipogenesis.

Here, we found two genomic safe harbor (GSH) candidates from chromosomes 3 and 8, based on large-scale population-based cohort data from 4,694 Koreans by CNV analysis. Furthermore, estimated genotype of these CNVRs was validated by quantitative real-time PCR, and epidemiological data examined no significant genetic association between diseases or traits and two CNVRs. After screening the GSH candidates by in silico approaches, we designed TALEN pairs to integrate EGFP expression cassette into human cell lines in order to confirm the functionality of GSH candidates in an in vitro setting. As a result, transgene insertion into one of the two loci using TALEN showed robust transgene expression comparable to that with an AAVS1 site without significantly perturbing neighboring genes. Changing the promoter or cell type did not noticeably disturb this trend. Thus, we could validate two CNVRs as a site for effective and safe transgene insertion in human cells. Keywords: genome editing, TALEN, safe harbor, copy number variation region

Rheumatoid arthritis: Understanding the regulation of disease-relevant genes Whole genome analysis of synoviocytes, specialized cells in the joint-lubricating synovial fluid, sheds light on the pathogenic mechanisms of rheumatoid arthritis (RA). Around 350 million people worldwide suffer joint pain and stiffness due to RA, but the inheritance pattern of the disease remains unclear. A study led by Tae-Young Roh at Pohang University of Science and Technology, South Korea, reveals a distinct pattern of chemical tags on the DNA of synoviocytes from RA patients. Differences in methyl group tags in over 500 regions of the genome influenced the expression of RA-associated genes and of microRNAs, small RNA molecules that are also involved in the regulation of gene expression. These differentially methylated sites may not only represent potential disease biomarkers, but also offer new insights into the regulation of RA-relevant genes.

Blood pressure (BP) is a major risk factor for cardiovascular disease and more than 200 genetic loci associated with BP are known. Here, the authors perform discovery GWAS for BP in East Asians and meta-analysis in East Asians and Europeans and report ancestry-specific BP SNPs and selection signals.

Diabetic retinopathy (DR) is a common complication of diabetes, and it is the consequence of microvascular retinal changes due to high glucose levels over a long time. Metabolomics profiling is a rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. In this study, we used a targeted metabolomics approach to quantify the serum metabolites in type 2 diabetes (T2D) patients. Diabetes patients were divided into three groups based on the status of their complications: non-DR (NDR, n = 143), non-proliferative DR (NPDR, n = 123), and proliferative DR (PDR, n = 51) groups. Multiple logistic regression analysis and multiple testing corrections were performed to identify the significant differences in the metabolomics profiles of the different analysis groups. The concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Finally, sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total DMA, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a marker potential. The metabolite signatures identified in this study will provide insight into the mechanisms underlying DR development and progression in T2D patients in future studies.

Over the last decade, genome-wide association studies (GWASs) have provided an unprecedented amount of genetic variations that are associated with various phenotypes. However, previous GWAS were mostly conducted in European populations, and these biased results for non-Europeans may result in a significant reduction in risk prediction for non-Europeans. An issue with the early GWAS was the winner’s curse problem, which led to misleading results when constructing the polygenic risk scores (PRS). Therefore, more non-European population-based studies are needed to validate reported variants and improve genetic risk assessment across diverse populations. In this study, we validated 422 variants independently associated with glycemic indexes, liver enzymes, and type 2 diabetes in 125,872 samples from a Korean population, and further validated the results by assessing publicly available summary statistics from European GWAS (n = 898,130). Among the 422 independently associated variants, 284, 320, and 361 variants were replicated in Koreans, Europeans, and either one of the two populations. In addition, the effect sizes for Koreans and Europeans were moderately correlated (r = 0.33–0.68). However, 61 variants were not replicated in both Koreans and Europeans. Our findings provide valuable information on effect sizes and statistical significance, which is essential to improve the assessment of disease risk using PRS analysis.

Abstract Background Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest. Results For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples. Conclusions We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers.

Osteoporotic fractures (OFs) are critical hard outcomes of osteoporosis and are characterized by decreased bone strength induced by low bone density and microarchitectural deterioration in bone tissue. Most OFs cause acute pain, hospitalization, immobilization, and slow recovery in patients and are associated with increased mortality. A variety of genetic studies have suggested associations of genetic variants with the risk of OF. Genome-wide association studies have reported various single-nucleotide polymorphisms and copy number variations (CNVs) in European and Asian populations. To identify CNV regions associated with OF risk, we conducted a genome-wide CNV study in a Korean population. We performed logistic regression analyses in 1,537 Korean subjects (299 OF cases and 1,238 healthy controls) and identified a total of 8 CNV regions significantly associated with OF (p < 0.05). Then, one CNV region located on chromosome 20q13.12 was selected for experimental validation. The selected CNV region was experimentally validated by quantitative polymerase chain reaction. The CNV region of chromosome 20q13.12 is positioned upstream of a family of long non-coding RNAs, LINC01260. Our findings could provide new information on the genetic factors associated with the risk of OF.

Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.

Here, Imamuraet al. conduct meta-analysis of genome-wide association studies to identify novel susceptibility loci for type 2 diabetes (T2D) in the Japanese population. By doing so, this study shows that both ethnicity-specific and ethnically-shared genetic loci can contribute to T2D risk.

This study investigated whether the promoter region of DNA methylation positively or negatively regulates tissue-specific genes (TSGs) and if it correlates with disease pathophysiology. We assessed tissue specificity metrics in five human tissues, using sequencing-based approaches, including 52 whole genome bisulfite sequencing (WGBS), 52 RNA-seq, and 144 chromatin immunoprecipitation sequencing (ChIP-seq) data. A correlation analysis was performed between the gene expression and DNA methylation levels of the TSG promoter region. The TSG enrichment analyses were conducted in the gene–disease association network (DisGeNET). The epigenomic association analyses of CpGs in enriched TSG promoters were performed using 1986 Infinium MethylationEPIC array data. A correlation analysis showed significant associations between the promoter methylation and 449 TSGs’ expression. A disease enrichment analysis showed that diabetes- and obesity-related diseases were high-ranked. In an epigenomic association analysis based on obesity, 62 CpGs showed statistical significance. Among them, three obesity-related CpGs were newly identified and replicated with statistical significance in independent data. In particular, a CpG (cg17075888 of PDK4), considered as potential therapeutic targets, were associated with complex diseases, including obesity and type 2 diabetes. The methylation changes in a substantial number of the TSG promoters showed a significant association with metabolic diseases. Collectively, our findings provided strong evidence of the relationship between tissue-specific patterns of epigenetic changes and metabolic diseases.

Platelets are derived from the fragments that are formed from the cytoplasm of bone marrow megakaryocytes-small irregularly shaped anuclear cells. Platelets respond to vascular damage, contracts blood vessels, and attaches to the damaged region, thereby stopping bleeding, together with the action of blood coagulation factors. Platelet activation is known to affect genes associated with vascular risk factors, as well as with arteriosclerosis and myocardial infarction. Here, we performed a genome-wide association study with 352,228 single-nucleotide polymorphisms typed in 8,842 subjects of the Korea Association Resource (KARE) project and replicated the results in 7,861 subjects from an independent population. We identified genetic associations between platelet count and common variants nearby chromosome 4p16.1 (p = 1.46 × 10-10, in the KIAA0232 gene), 6p21 (p = 1.36 × 10-7, in the BAK1 gene), and 12q24.12 (p = 1.11 × 10-15, in the SH2B3 gene). Our results illustrate the value of large-scale discovery and a focus for several novel research avenues.

BackgroundUntil recently, genome-wide association study (GWAS)-based findings have provided a substantial genetic contribution to type 2 diabetes mellitus (T2DM) or related glycemic traits. However, identification of allelic heterogeneity and population-specific genetic variants under consideration of potential confounding factors will be very valuable for clinical applicability. To identify novel susceptibility loci for T2DM and glycemic traits, we performed a two-stage genetic association study in a Korean population.MethodsWe performed a logistic analysis for T2DM, and the first discovery GWAS was analyzed for 1,042 cases and 2,943 controls recruited from a population-based cohort (KARE, n=8,842). The second stage, de novo replication analysis, was performed in 1,216 cases and 1,352 controls selected from an independent population-based cohort (Health 2, n=8,500). A multiple linear regression analysis for glycemic traits was further performed in a total of 14,232 nondiabetic individuals consisting of 7,696 GWAS and 6,536 replication study participants. A meta-analysis was performed on the combined results using effect size and standard errors estimated for stage 1 and 2, respectively.ResultsA combined meta-analysis for T2DM identified two new (rs11065756 and rs2074356) loci reaching genome-wide significance in CCDC63 and C12orf51 on the 12q24 region. In addition, these variants were significantly associated with fasting plasma glucose and homeostasis model assessment of β-cell function. Interestingly, two independent single nucleotide polymorphisms were associated with sex-specific stratification in this study.ConclusionOur study showed a strong association between T2DM and glycemic traits. We further observed that two novel loci with multiple diverse effects were highly specific to males. Taken together, these findings may provide additional insights into the clinical assessment or subclassification of disease risk in a Korean population.

BackgroundGlycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes.Methods & findingsUsing genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants.ConclusionsAs G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.

Differential DNA methylation with hyperglycemia is significantly associated with Type 2 Diabetes (T2D). Longtime extended exposure to high blood glucose levels can affect the epigenetic signatures in all organs. However, the relevance of the differential DNA methylation changes with hyperglycemia in blood with pancreatic islets remains unclear. We investigated differential DNA methylation in relation to glucose homeostasis based on the Oral Glucose Tolerance Test (OGTT) in a population-based cohort. We found a total of 382 differential methylation sites from blood DNA in hyperglycemia and type 2 diabetes subgroups using a longitudinal and cross-sectional approach. Among them, three CpG sites were overlapped; they were mapped to the MSI2 and CXXC4 genes. In a DNA methylation replication study done by pyrosequencing (n = 440), the CpG site of MSI2 were shown to have strong associations with the T2D group (p value = 2.20E-16). The differential methylation of MSI2 at chr17:55484635 was associated with diabetes-related traits, in particular with insulin sensitivity (QUICKI, p value = 2.20E-16) and resistance (HOMA-IR, p value = 1.177E-07). In human pancreatic islets, at the single-base resolution (using whole-genome bisulfite sequencing), the 292 CpG sites in the ±5kb at chr17:55484635 were found to be significantly hypo-methylated in donors with T2D (average decrease = 13.91%, 95% confidence interval (CI) = 4.18~ 17.06) as compared to controls, and methylation patterns differed by sex (-9.57%, CI = -16.76~ -6.89) and age (0.12%, CI = -11.17~ 3.77). Differential methylation of the MSI2 gene (chr17:55484635) in blood and islet cells is strongly related to hyperglycemia. Our findings suggest that epigenetic perturbation on the target site of MSI2 gene in circulating blood and pancreatic islets should represent or affect hyperglycemia.

Liver enzyme elevations, as an indicator of liver function, are widely associated with metabolic diseases. Genome-wide population-based association studies have identified a genetic susceptibility to liver enzyme elevations and their related traits; however, the genetic architecture in childhood remains largely unknown. We performed a genome-wide association study to identify new genetic loci for liver enzyme levels in a Korean childhood cohort (n = 484). We observed three novel loci (rs4949718, rs80311637, and rs596406) that were multiply associated with elevated levels of alanine transaminase and aspartate transaminase. Although there are some limitations, including genetic power, additional replication and functional characterization will support the clarity on the genetic contribution that the ST6GALNAC3, ADAMTS9, and CELF2 genes have in childhood liver function.

The single nucleotide polymorphism rs9939609 of the gene FTO, which encodes fat mass and obesity-associated protein, is strongly associated with obesity and type 2 diabetes (T2D) in multiple populations; however, the underlying mechanism of this association is unclear. The present study aimed to investigate FTO genotype-dependent metabolic changes in obesity and T2D. To elucidate metabolic dysregulation associated with disease risk genotype, genomic and metabolomic datasets were recruited from 2,577 participants of the Korean Association REsource (KARE) cohort, including 40 homozygous carriers of the FTO risk allele (AA), 570 heterozygous carriers (AT), and 1,967 participants carrying no risk allele (TT). A total of 134 serum metabolites were quantified using a targeted metabolomics approach. Through comparison of various statistical methods, seven metabolites were identified that are significantly altered in obesity and T2D based on the FTO risk allele (adjusted p < 0.05). These identified metabolites are relevant to phosphatidylcholine metabolic pathway, and previously reported to be metabolic markers of obesity and T2D. In conclusion, using metabolomics with the information from genome-wide association studies revealed significantly altered metabolites depending on the FTO genotype in complex disorders. This study may contribute to a better understanding of the biological mechanisms linking obesity and T2D.

Exome-based genotyping arrays are cost-effective and have recently been used as alternative platforms to whole-exome sequencing. However, the automated clustering algorithm in an exome array has a genotype calling problem in accuracy for identifying rare and low-frequency variants. To address these shortcomings, we present a practical approach for accurate genotype calling using the Illumina Infinium HumanExome BeadChip. We present comparison results and a statistical summary of our genotype data sets. Our data set comprises 14,647 Korean samples. To solve the limitation of automated clustering, we performed manual genotype clustering for the targeted identification of 46,076 variants that were identified using GenomeStudio software. To evaluate the effects of applying custom cluster files, we tested cluster files using 804 independent Korean samples and the same platform. Our study firstly suggests practical guidelines for exome chip quality control in Asian populations and provides valuable insight into an association study using exome chip.

Abstract Background This study investigated the impact of physical frailty on the development of disabilities in mobility, activities of daily living (ADL), and instrumental activities of daily living (IADL) according to sex among community-dwelling Korean older adults. Methods We used data of 2,905 older adults aged 70-84 years from the Korean Frailty and Aging Cohort Study (KFACS) at baseline (2016-2017) and Wave 2 (2018-2019). Fried’s physical frailty phenotype was used to identify frailty. Results After adjustment, frailty showed a higher impact for women than men on developing mobility disability (odds ratio [OR]=14.00, 95% confidence interval [CI]=4.8–40.78 vs. OR=9.89, 95% CI=4.28–22.86) and IADL disability after two years (OR=7.22, 95% CI=2.67–19.56 vs. OR=3.19, 95% CI=1.17–8.70). Pre-frailty led to mobility disability for women and men (OR=2.77, 95% CI=1.93–3.98 vs. OR=2.49, 95% CI=1.66–3.72, respectively), and IADL disability only for women (OR=3.01, 95% CI=1.28–7.09). Among the IADL components, both men and women who were prefrail or frail showed increased disability in ‘using transportation’. Among men, pre-frailty was significantly associated with disability in “going out” and “shopping”. In women, frailty was significantly associated with disability in “doing laundry,” “performing household chores,” “shopping,” and “managing money”. Conclusions Physical frailty increased disability over 2 years for women more than men. Physical frailty increased disability in outdoor activity-related IADL components in men and household work-related IADL components in women. This study highlights the need for gender-specific policies and preventative programs for frailty, particularly restorative interventions that focus on women who are physically frail.

Objective: To investigate the relationship between reduced glutathione (GSH), a key molecule of the antioxidant defense system in the blood, and glutathione reductase (GR), which reduces oxidized glutathione (glutathione disulfide [GSSG]) to GSH and maintains the redox balance, with the prevalence of Alzheimer's dementia and cognitive decline. Methods: In all, 20 participants with Alzheimer's dementia who completed the third follow-up clinical evaluation over 6 years were selected, and 20 participants with normal cognition were selected after age and sex matching. The GSH and GR concentrations were the independent variables. Clinical diagnosis and neurocognitive test scores were the dependent variables indicating cognitive status. Results: The higher the level of GR, the greater the possibility of having normal cognition than of developing Alzheimer's dementia. Additionally, the higher the level of GR, the higher the neurocognitive test scores. However, this association was not significant for GSH. After 6 years, the conversion rate from normal cognition to cognitive impairment was significantly higher in the lower 50th percentile of the GR group than in the upper 50th percentile. Conclusion: The higher the GR, the lower the prevalence of Alzheimer's dementia and incidence of cognitive impairment and the higher the cognitive test scores. Therefore, GR is a potential protective biomarker against Alzheimer's dementia and cognitive decline. [ABSTRACT FROM AUTHOR]