MOESM1 of Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population

Additional file 1: Figure S1. The direction of the allelic effect for each of three pleiotropic SNPs in the 12q24.12 region. Figure S2. Protein-protein interactions among three genes in the 12q24.12 region. Table S1. Frequency distribution of minor allele frequencies in Exome chip data. Table S2. Descriptive information of study subjects. Table S3. Replication results of known lipid-associated loci in Korean individuals. Table S4. Population diversity of three pleiotropic SNPs in the 12q24.12 region. Table S5. Evaluation of pleiotropic effect of 12q24.12 (ALDH2, rs671) genotypes. Table S6. Protein-protein interactions among three genes in 12q24.12 region.

Presentation1_Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population.pptx

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.</p

Correlations of the degree of DNA methylation of <i>MSI2</i> (chr17:55484635) with six related traits.

<p>Correlations between DNA methylation at cg23586172 and Glu0 (A), HbA1c (B), BMI (C), Ins0 (D), HOMA-IR (E) and <i>QUICKI</i> (F) were present in the T2D group (n = 440). Glu0, fasting blood glucose level; HbA1, glycosylated hemoglobin level; BMI, body mass index; and Ins0, fasting blood insulin level. HOMA-IR, Ins0(μU/mL) × Glu0 (mg/mL)/405): <i>QUICKI</i>, 1 / (log(Ins0 μU/mL) + log(Glu0 mg/dL).</p

Canonical pathways enriched in the T2D and ΔhiGlu60 subgroups by Ingenuity Pathway Analysis (IPA).

<p>The bar graphs showed 39 pathways in the T2D and 26 pathways in the ΔhiGlu60 subgroups with the enriched percentage and p values. The percentages indicate the number of differentially methylated genes that map to each pathway divided by the total number of genes that map to the canonical pathway. The total number of gene was indicated at the top of bar. The p value (–log10 <i>P</i>) is the probability that each biological function assigned to that data set was assigned by chance.</p

DNA methylation mapping of <i>MSI2</i> in human pancreatic islets.

<p>A. Estimate of the average methylation of chr17:55484635 (chromosome 17:55484635) and the complimentary CpG site (chromosome 17:55484636) in controls (n = 16) and T2D subjects (n = 2). The p values are given. B. Schematic <i>MSI2</i> gene structure was described with 13 exons. DNA methylation map of the 2 kb region (chromosome 17:55483600 to 55485600) centered on the target site (chr17:55484635). C. The average normal methylation values (n = 16) from chr17:55483600 to chr17:55485600 are graphed in blue and the average T2D methylation values (n = 2) are in red. The—log10 (<i>P</i>) of 86 DMPs is indicated by the green bar on the bottom of the methylation curve. The line at -log10 (p value = 0.05) shows the cutoff for statistical significance. The arrow indicates the position at chromosome 17:55484635. D. Differential methylation map drawn after classifying 18 islets into cases and controls by age (“young” < 40 yrs and “old” > 40 yrs). The average methylation values of young islets (n = 5) are graphed as controls in blue, and those of old islets (n = 13) are shown as cases in red. E. Differential methylation map drawn after classifying 18 islets into cases and controls by gender (“male” and “female”). The average methylation values of male islets (n = 8) are graphed in blue, and those of female islets (n = 10) are shown in red. F. Differential methylation map drawn after classifying 8 male islets into T2D cases and controls after sex matching. The male T2D cases (n = 2) are shown in red, and the male controls (n = 6) are in blue.</p

Summary of the DMP criteria for the discovery study.

<p>Summary of the DMP criteria for the discovery study.</p

Association of Metabolites with Obesity and Type 2 Diabetes Based on <i>FTO</i> Genotype

<div><p>The single nucleotide polymorphism rs9939609 of the gene <i>FTO</i>, 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 <i>FTO</i> 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 <i>FTO</i> 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 <i>FTO</i> risk allele (adjusted <i>p</i> < 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 <i>FTO</i> genotype in complex disorders. This study may contribute to a better understanding of the biological mechanisms linking obesity and T2D.</p></div

Differential DNA methylation of <i>MSI2</i> and its correlation with diabetic traits

<div><p>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 <i>MSI2</i> and <i>CXXC4</i> genes. In a DNA methylation replication study done by pyrosequencing (n = 440), the CpG site of <i>MSI2</i> were shown to have strong associations with the T2D group (p value = 2.20E-16). The differential methylation of <i>MSI2</i> at chr17:55484635 was associated with diabetes-related traits, in particular with insulin sensitivity (<i>QUICKI</i>, 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 <i>MSI2</i> gene (chr17:55484635) in blood and islet cells is strongly related to hyperglycemia. Our findings suggest that epigenetic perturbation on the target site of <i>MSI2</i> gene in circulating blood and pancreatic islets should represent or affect hyperglycemia.</p></div

Summary of the subjects in the discovery study based on OGTT.

<p>Summary of the subjects in the discovery study based on OGTT.</p

Linear regression of target CpG methylations with six diabetes-related traits.

<p>Linear regression of target CpG methylations with six diabetes-related traits.</p