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Additional file 1: Fig. S1. Wavelet amplitudes of neurogenic and endothelial NO-independent activities at phase 2 (prior to CIVD) in groups of subjects differentiated by their CIVD response and genotypes of (a) rs73619297 of COL4A2 or (b) rs931740 of PRLR. Data are means ± standard error of the mean. a.u., arbitrary units. Fig. S2. Principal component analysis of imputed genotype data from the JPQ cohort. Fig. S3. Wavelet amplitudes of endothelial NO-independent, endothelial NO-dependent, and neurogenic activities in four phases, including the baseline phase, in 10 study subjects with no CIVD response. Phases 1, 2, and 3 represent “vasoconstriction,” “prior to CIVD,” and “CIVD,” respectively [1]. a.u., arbitrary units. Fig. S4. Physical position and linkage disequilibrium of candidate SNPs around COL4A2 or at 14q23.3 in 94 Japanese subjects. The diagram was created using Haploview version 4.2. The numbers in diamonds represent r2 values (×100). The haplotype block was defined using the method of Gabriel et al. [2]. SNPs shown in bold are CIVD-associated SNPs. Table S1. Characteristics of all study subjects including one female. Table S2. SNP with p < 1×10-5 for CIVD-related categorical traits (CIVD response and eNO-independent activity, neurogenic activity) in the logistic regression analysis with the additive model. Table S3. SNP with p < 1×10-5 for CIVD-related categorical traits (CIVD response and eNO-independent activity, neurogenic activity) in the Fisher's exact test with the additive model. Table S4. Allele frequency of candidate SNPs detected in GWASs of CIVD response with neurogenic and endothelial NO-independent activities. Table S5. SNP with p < 1×10-5 for eNO-independent and neurogenic activities in the additive model using two-way ANOVA. Table S6. SNP with p < 1×10-5 for eNO-independent and neurogenic activities in the dominant model using two-way ANOVA. Table S7. Relationships among the genotypes of five candidate SNPs defined in the dominant model and network of three CIVD-related factors. Table S8. Relationships among the genotypes of five candidate SNPs defined in the additive model and network of three CIVD-related factors. Table S9. Correlation estimates of four candidate SNPs with their adjacent SNPs using LDproxy.

Additional file 4: Fig. S3. Hierarchical tree of GO terms in the biological process category. Significantly overrepresented GO terms (FDR < 0.05) are shown in red characters.

Additional file 3: Fig. S2. Relative quantification levels of CTSL measured using real-time RT-qPCR and the comparative Ct method. The bold black bars represent median values. The vertical axis represents the mean relative quantification of CTSL transcripts.

Additional file 2: Fig. S1. Gene expression levels of 13 differentially expressed genes with |LFC| < 1, identified using the DESeq2 program, in each of the study subjects. Normalized counts at the vertical axis represent the estimated expression abundance at the gene level.

Additional file 1: Table S1. The quality of total RNA used in RNA-seq and real-time RT-qPCR analyses.

DNA methylation is an important epigenetic modification that has been implicated in the pathogenesis of atherosclerosis. Although previous studies have identified various CpG sites and genes whose methylation is associated with atherosclerosis in populations with European or Mexican ancestry, the genome‑wide pattern of DNA methylation in the atherosclerotic human aorta is yet to be elucidated in Japanese individuals. In the present study, a genome‑wide analysis of DNA methylation at ~853,000 CpG sites was performed using 128 postmortem aortic intima specimens obtained from 64 Japanese patients. To avoid the effects of interindividual variation, intraindividual paired comparisons were performed between atheromatous plaque lesions and corresponding plaque‑free tissue for each patient. Bisulfite‑modified genomic DNA was analyzed using a specific microarray for DNA methylation. DNA methylation at each CpG site was calculated as the β value, where β = (intensity of the methylated allele)/(intensity of the methylated allele + intensity of the unmethylated allele + 100). Bonferroni's correction for statistical significance of association was applied to compensate for multiple comparisons. The methylation of 2,679 CpG sites differed significantly (P0.15 (plaque lesion‑plaque‑free intima) and 11 had a β ratio of >1.50 (plaque/plaque‑free intima). A further 15 and 17 hypomethylated CpG sites in atheromatous plaques were observed to have a difference in β value of

We have performed exome-wide association studies to identify genetic variants that influence renal function-related traits or confer susceptibility to chronic kidney disease or hyperuricemia in Japanese. Exome-wide association studies for estimated glomerular filtration rate and the serum concentration of creatinine were performed with 12,565 individuals, that for the serum concentration of uric acid with 9934 individuals, and those for chronic kidney disease or hyperuricemia with 5161 individuals (3270 cases, 1891 controls) or 11,686 individuals (2045 cases, 9641 controls), respectively. The relation of genotypes of single nucleotide polymorphisms to estimated glomerular filtration rate or the serum concentrations of creatinine or uric acid was examined by linear regression analysis, and that of allele frequencies of single nucleotide polymorphisms to chronic kidney disease or hyperuricemia was examined with Fisher’s exact test. The exome-wide association studies revealed that 25, seven, and six single nucleotide polymorphisms were significantly (P

Figure S2. Relationship between BMI and SpO2 at sea level in 46 Japanese male students. (PDF 101Â kb)

Figure S4. Relationship between SpO2 and perfusion index at 60 min (equivalent to 4000 m) for rs12097901 (a) and rs2790859 (b). Red characters represent highlander alleles. Blue line and gray band represent a regression line and its 95% confidence interval, respectively. Circle colors indicate genotypes of each SNP. The mean slope of the regression line was 0.011. Linear regression analysis showed no significant correlation (r2 = 0.011, P = 0.481). ANCOVA also showed no significant differences in the regression coefficient between genotypes (F(2, 40) = 2.20, P = 0.124 for rs12097901; F(2, 40) = 0.69, P = 0.509 for rs2790859) and in the adjusted mean values (F(2, 42) = 1.29, P = 0.285 for rs12097901; F(2, 42) = 1.08, P = 0.348 for rs2790859). (PDF 227 kb)

Figure S1. Linkage disequilibrium plot of 1504 biallelic sites across a ~ 399-kb genomic region at 1q42.2, based on datasets from JPT (Japanese in Tokyo, Japan) in the 1000 Genomes Project database. The position of SNPs examined is shown by the green arrow. The haplotype block was defined by the method of Gabriel et al. [36]. The genetic variants with the minor allele frequency of

Figure S5. Relationship between SpO2 and respiratory exchange ratio at 60 min (equivalent to 4000 m) for rs12097901 (a) and rs2790859 (b). Red characters represent highlander alleles. Blue line and gray band represent a regression line and its 95% confidence interval, respectively. Circle colors indicate genotypes of each SNP. The mean slope of the regression line was 0.002. Linear regression analysis showed no significant correlation (r2 = 0.012, P = 0.474). ANCOVA also showed no significant differences in the regression coefficient between genotypes (F(2, 40) = 0.19, P = 0.825 for rs12097901; F(2, 40) = 0.98, P = 0.384 for rs2790859) and in the adjusted mean values (F(2, 42) = 0.59, P = 0.557 for rs12097901; F(2, 42) = 0.17, P = 0.840 for rs2790859). (PDF 215 kb)

Figure S3. Relationship between SpO2 and minute ventilation at 60 min (equivalent to 4000 m) for rs12097901 (a) and rs2790859 (b). Red characters represent highlander alleles. Blue line and gray band represent a regression line and its 95% confidence interval, respectively. Circle colors indicate genotypes of each SNP. The mean slope of the regression line was − 0.018. Linear regression analysis showed no significant correlation (r2 = 0.001, P = 0.818). ANCOVA also showed no significant differences in the regression coefficient between genotypes (F(2, 40) = 1.21, P = 0.309 for rs12097901; F(2, 40) = 0.04, P = 0.961 for rs2790859) and in the adjusted mean values (F(2, 42) = 2.97, P = 0.062 for rs12097901; F(2, 42) = 0.11, P = 0.894 for rs2790859). (PDF 230 kb)

Table S2. Haplotype and diplotype frequencies of five SNPs around EGLN1 in 46 Japanese subjects. (PDF 71Â kb)