jMorp: Japanese Multi-Omics Reference Panel update report 2023.

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp.

Dietary habits and plasma lipid concentrations in a general Japanese population.

INTRODUCTION: Accumulating data on the associations between food consumption and lipid composition in the body is essential for understanding the effects of dietary habits on health. OBJECTIVES: As part of omics research in the Tohoku Medical Megabank Community-Based Cohort Study, this study sought to reveal the dietary impact on plasma lipid concentration in a Japanese population. METHODS: We conducted a correlation analysis of food consumption and plasma lipid concentrations measured using mass spectrometry, for 4032 participants in Miyagi Prefecture, Japan. RESULTS: Our analysis revealed 83 marked correlations between six food categories and the concentrations of plasma lipids in nine subclasses. Previously reported associations, including those between seafood consumption and omega-3 fatty acids, were validated, while those between dairy product consumption and odd-carbon-number fatty acids (odd-FAs) were validated for the first time in an Asian population. Further analysis suggested that dairy product consumption is associated with odd-FAs via sphingomyelin (SM), which suggests that SM is a carrier of odd-FAs. These results are important for understanding odd-FA metabolism with regards to dairy product consumption. CONCLUSION: This study provides insight into the dietary impact on plasma lipid concentration in a Japanese population.

Decreased ß-hydroxybutyrate and ketogenic amino acid levels in depressed human adults.

ß-hydroxybutyrate (BHB) is a major ketone body synthesized mainly in the liver mitochondria and is associated with stress and severity of depression in humans. It is known to alleviate depressive-like behaviors in mouse models of depression. In this study, plasma BHB, ketogenic and glucogenic amino acids selected from the Tohoku Medical Megabank Project Community-Based Cohort Study were analysed and measured using nuclear magnetic resonance spectroscopy. The Center for Epidemiologic Studies Depression Scale (CES-D) was utilized to select adult participants with depressive symptoms (CES-D ≥ 16; n = 5722) and control participants (CES-D < 16; n = 18,150). We observed significantly reduced plasma BHB, leucine, and tryptophan levels in participants with depressive symptoms. Using social defeat stress (SDS) mice models, we found that BHB levels in mice sera increased after acute SDS, but showed no change after chronic SDS, which differed from human plasma results. Furthermore, acute SDS increased mitochondrial BHB levels in the prefrontal cortex at 6 h. In contrast, chronic SDS significantly increased the amount of food intake but reduced hepatic mitochondrial BHB levels in mice. Moreover, gene transcriptions of voltage-dependent anion-selective channel 1 (Vdac1) and monocarboxylic acid transporter 1 (Mct1), major molecules relevant to mitochondrial biogenesis and BHB transporter, significantly decreased in the liver and PFC after chronic SDS exposure. These results provide evidence that hepatic and prefrontal mitochondrial biogenesis plays an important role in BHB synthesis under chronic stress and in humans with depressive symptoms.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population.

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

Identification of predictive biomarkers for diagnosis and radiation sensitivity of uterine cervical cancer using wide-targeted metabolomics.

AIM: Uterine cervical cancer (UCC) is the fourth most common cancer in women, responsible for more than 300 000 deaths worldwide. Its early detection, by cervical cytology, and prevention, by vaccinating against human papilloma virus, greatly contribute to reducing cervical cancer mortality in women. However, penetration of the effective prevention of UCC in Japan remains low. Plasma metabolome analysis is widely used for biomarker discovery and the identification of cancer-specific metabolic pathways. Here, we aimed to identify predictive biomarkers for the diagnosis and radiation sensitivity of UCC using wide-targeted plasma metabolomics. METHODS: We analyzed 628 metabolites in plasma samples obtained from 45 patients with UCC using ultra-high-performance liquid chromatography with tandem mass spectrometry. RESULTS: The levels of 47 metabolites were significantly increased and those of 75 metabolites were significantly decreased in patients with UCC relative to healthy controls. Increased levels of arginine and ceramides, and decreased levels of tryptophan, ornithine, glycosylceramides, lysophosphatidylcholine, and phosphatidylcholine were characteristic of patients with UCC. Comparison of metabolite profiles in groups susceptible and non-susceptible to radiation therapy, a treatment for UCC, revealed marked variations in polyunsaturated fatty acid, nucleic acid, and arginine metabolism in the group not susceptible to treatment. CONCLUSIONS: Our findings suggest that the metabolite profile of patients with UCC may be an important indicator for distinguishing these patients from healthy cohorts, and may also be useful for predicting sensitivity to radiotherapy.

Impacts of NRF2 activation in non-small-cell lung cancer cell lines on extracellular metabolites.

Aberrant activation of NRF2 is as a critical prognostic factor that drives the malignant progression of various cancers. Cancer cells with persistent NRF2 activation heavily rely on NRF2 activity for therapeutic resistance and aggressive tumorigenic capacity. To clarify the metabolic features of NRF2-activated lung cancers, we conducted targeted metabolomic (T-Met) and global metabolomic (G-Met) analyses of non-small-cell lung cancer (NSCLC) cell lines in combination with exome and transcriptome analyses. Exome analysis of 88 cell lines (49 adenocarcinoma, 14 large cell carcinoma, 15 squamous cell carcinoma and 10 others) identified non-synonymous mutations in the KEAP1, NRF2 and CUL3 genes. Judging from the elevated expression of NRF2 target genes, these mutations are expected to result in the constitutive stabilization of NRF2. Out of the 88 cell lines, 52 NSCLC cell lines (29 adenocarcinoma, 10 large cell carcinoma, 9 squamous cell carcinoma and 4 others) were subjected to T-Met analysis. Classification of the 52 cell lines into three groups according to the NRF2 target gene expression enabled us to draw typical metabolomic signatures induced by NRF2 activation. From the 52 cell lines, 18 NSCLC cell lines (14 adenocarcinoma, 2 large cell carcinoma, 1 squamous cell carcinoma and 1 others) were further chosen for G-Met and detailed transcriptome analyses. G-Met analysis of their culture supernatants revealed novel metabolites associated with NRF2 activity, which may be potential diagnostic biomarkers of NRF2 activation. This study also provides useful information for the exploration of new metabolic nodes for selective toxicity towards NRF2-activated NSCLC.

jMorp: Japanese Multi Omics Reference Panel.

We developed jMorp, a new database containing metabolome and proteome data for plasma obtained from >5000 healthy Japanese volunteers from the Tohoku Medical Megabank Cohort Study, which is available at https://jmorp.megabank.tohoku.ac.jp. Metabolome data were measured by proton nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS), while proteome data were obtained by nanoLC-MS. We released the concentration distributions of 37 metabolites identified by NMR, distributions of peak intensities of 257 characterized metabolites by LC-MS, and observed frequencies of 256 abundant proteins. Additionally, correlation networks for the metabolites can be observed using an interactive network viewer. Compared with some existing databases, jMorp has some unique features: (i) Metabolome data were obtained using a single protocol in a single institute, ensuring that measurement biases were significantly minimized; (ii) The database contains large-scale data for healthy volunteers with various health records and genome data and (iii) Correlations between metabolites can be easily observed using the graphical viewer. Metabolites data are becoming important intermediate markers for evaluating the health states of humans, and thus jMorp is an outstanding resource for a wide range of researchers, particularly those in the fields of medical science, applied molecular biology, and biochemistry.

Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project.

BACKGROUND: Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels. RESULTS: We found that the 1KJPN population was more similar to other Japanese populations, Nagahama (south-central Japan) and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%. CONCLUSIONS: 1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population's genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago.

Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals.

Clarifying allele frequencies of disease-related genetic variants in a population is important in genomic medicine; however, such data is not yet available for the Japanese population. To estimate frequencies of actionable pathogenic variants in the Japanese population, we examined the reported pathological variants in genes recommended by the American College of Medical Genetics and Genomics (ACMG) in our reference panel of genomic variations, 2KJPN, which was created by whole-genome sequencing of 2049 individuals of the resident cohort of the Tohoku Medical Megabank Project. We searched for pathogenic variants in 2KJPN for 57 autosomal ACMG-recommended genes responsible for 26 diseases and then examined their frequencies. By referring to public databases of pathogenic variations, we identified 143 reported pathogenic variants in 2KJPN for the 57 ACMG recommended genes based on a classification system. At the individual level, 21% of the individuals were found to have at least one reported pathogenic allele. We then conducted a literature survey to review the variants and to check for evidence of pathogenicity. Our results suggest that a substantial number of people have reported pathogenic alleles for the ACMG genes, and reviewing variants is indispensable for constructing the information infrastructure of genomic medicine for the Japanese population.

COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals.

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

Validation of multiple single nucleotide variation calls by additional exome analysis with a semiconductor sequencer to supplement data of whole-genome sequencing of a human population.

BACKGROUND: Validation of single nucleotide variations in whole-genome sequencing is critical for studying disease-related variations in large populations. A combination of different types of next-generation sequencers for analyzing individual genomes may be an efficient means of validating multiple single nucleotide variations calls simultaneously. RESULTS: Here, we analyzed 12 independent Japanese genomes using two next-generation sequencing platforms: the Illumina HiSeq 2500 platform for whole-genome sequencing (average depth 32.4×), and the Ion Proton semiconductor sequencer for whole exome sequencing (average depth 109×). Single nucleotide polymorphism (SNP) calls based on the Illumina Human Omni 2.5-8 SNP chip data were used as the reference. We compared the variant calls for the 12 samples, and found that the concordance between the two next-generation sequencing platforms varied between 83% and 97%. CONCLUSIONS: Our results show the versatility and usefulness of the combination of exome sequencing with whole-genome sequencing in studies of human population genetics and demonstrate that combining data from multiple sequencing platforms is an efficient approach to validate and supplement SNP calls.

Whole blood transcriptome analysis for age- and gender-specific gene expression profiling in Japanese individuals.

Whole blood transcriptome analysis is a valuable approachin medical research, primarily due to the ease of sample collection and the richness of the information obtained. Since the expression profile of individual genes in the analysis is influenced by medical traits and demographic attributes such as age and gender, there has been a growing demand for a comprehensive database for blood transcriptome analysis. Here, we performed whole blood RNA sequencing (RNA-seq) analysis on 576 participants stratified by age (20-30s and 60-70s) and gender from cohorts of the Tohoku Medical Megabank (TMM). A part of female segment included pregnant women. We did not exclude the globin gene family in our RNA-seq study, which enabled us to identify instances of hereditary persistence of fetal hemoglobin based on the HBG1 and HBG2 expression information. Comparing stratified populations allowed us to identify groups of genes associated with age-related changes and gender differences. We also found that the immune response status, particularly measured by neutrophil-to-lymphocyte ratio (NLR), strongly influences the diversity of individual gene expression profiles in whole blood transcriptome analysis. This stratification has resulted in a data set that will be highly beneficial for future whole blood transcriptome analysis in the Japanese population.

Selective Elimination of NRF2-Activated Cells by Competition With Neighboring Cells in the Esophageal Epithelium.

BACKGROUND & AIMS: NF-E2-related factor 2 (NRF2) is a transcription factor that regulates cytoprotective gene expression in response to oxidative and electrophilic stresses. NRF2 activity is mainly controlled by Kelch-like ECH-associated protein 1 (KEAP1). Constitutive NRF2 activation by NRF2 mutations or KEAP1 dysfunction results in a poor prognosis for esophageal squamous cell carcinoma (ESCC) through the activation of cytoprotective functions. However, the detailed contributions of NRF2 to ESCC initiation or promotion have not been clarified. Here, we investigated the fate of NRF2-activated cells in the esophageal epithelium. METHODS: We generated tamoxifen-inducible, squamous epithelium-specific Keap1 conditional knockout (Keap1-cKO) mice in which NRF2 was inducibly activated in a subset of cells at the adult stage. Histologic, quantitative reverse-transcription polymerase chain reaction, single-cell RNA-sequencing, and carcinogen experiments were conducted to analyze the Keap1-cKO esophagus. RESULTS: KEAP1-deleted/NRF2-activated cells and cells with normal NRF2 expression (KEAP1-normal cells) coexisted in the Keap1-cKO esophageal epithelium in approximately equal numbers, and NRF2-activated cells formed dysplastic lesions. NRF2-activated cells exhibited weaker attachment to the basement membrane and gradually disappeared from the epithelium. In contrast, neighboring KEAP1-normal cells exhibited accelerated proliferation and started dominating the epithelium but accumulated DNA damage that triggered carcinogenesis upon carcinogen exposure. CONCLUSIONS: Constitutive NRF2 activation promotes the selective elimination of epithelial cells via cell competition, but this competition induces DNA damage in neighboring KEAP1-normal cells, which predisposes them to chemical-induced ESCC.

Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling.

BACKGROUND: Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC. METHODS: In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry. RESULTS: The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression. CONCLUSIONS: Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment.

BACKGROUND: Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed. METHODS: Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI. FINDINGS: We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI. INTERPRETATION: The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

Association Between Olfactory Test Data with Multiple Levels of Odor Intensity and Suspected Cognitive Impairment: A Cross-Sectional Study.

BACKGROUND: Olfactory function decline has recently been reported to be associated with a risk of cognitive impairment. Few population-based studies have included younger adults when examining the association between olfactory test data with multiple odor intensities and suspected cognitive impairment. OBJECTIVE: We investigated the association between high-resolution olfactory test data with fewer odors and suspected cognitive impairments. We also examined the differences between older and younger adults in this association. METHODS: The Japanese version of the Montreal Cognitive Assessment (MoCA-J) was administered to 1,450 participants, with three odor-intensity-level olfactometry using six different odors. Logistic regressions to discriminate suspected cognitive impairment were conducted to examine the association, adjusted for age, sex, education duration, and smoking history. Data were collected from the Program by Tohoku University Tohoku Medical Megabank Organization, with an additional olfactory test conducted between 2019 and 2021. RESULTS: We generally observed that the lower the limit of distinguishable odor intensity was, the higher the MoCA-J score was. The combination of spearmint and stuffy socks contributed most to the distinction between suspected and unsuspected cognitive impairment. Furthermore, the association was significant in women aged 60-74 years (adjusted odds ratio 0.881, 95% confidence interval [0.790, 0.983], p = 0.024). CONCLUSIONS: The results indicate an association between the limit of distinguishable odor intensity and cognitive function. The olfactory test with multiple odor intensity levels using fewer odors may be applicable for the early detection of mild cognitive impairment, especially in older women aged 60-74 years.

Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study: Rationale, Design, and Background.

The Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study (TMM Brain MRI Study) was established to collect multimodal information through neuroimaging and neuropsychological assessments to evaluate the cognitive function and mental health of residents who experienced the Great East Japan Earthquake (GEJE) and associated tsunami. The study also aimed to promote advances in personalized healthcare and medicine related to mental health and cognitive function among the general population. We recruited participants for the first (baseline) survey starting in July 2014, enrolling individuals who were participating in either the TMM Community-Based Cohort Study (TMM CommCohort Study) or the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study). We collected multiple magnetic resonance imaging (MRI) sequences, including 3D T1-weighted sequences, magnetic resonance angiography (MRA), diffusion tensor imaging (DTI), pseudo-continuous arterial spin labeling (pCASL), and three-dimensional fluid-attenuated inversion recovery (FLAIR) sequences. To assess neuropsychological status, we used both questionnaire- and interview-based rating scales. The former assessments included the Tri-axial Coping Scale, Impact of Event Scale in Japanese, Profile of Mood States, and 15-item Depression, Anxiety, and Stress Scale, whereas the latter assessments included the Mini-Mental State Examination, Japanese version. A total of 12,164 individuals were recruited for the first (baseline) survey, including those unable to complete all assessments. In parallel, we returned the MRI results to the participants and subsequently shared the MRI data through the TMM Biobank. At present, the second (first follow-up) survey of the study started in October 2019 is underway. In this study, we established a large and comprehensive database that included robust neuroimaging data as well as psychological and cognitive assessment data. In combination with genomic and omics data already contained in the TMM Biobank database, these data could provide new insights into the relationships of pathological processes with neuropsychological disorders, including age-related cognitive impairment.

Plasma metabolic disturbances during pregnancy and postpartum in women with depression.

Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development.

Genome-wide Association Study of Axial Length in Population-based Cohorts in Japan: The Tohoku Medical Megabank Organization Eye Study.

Purpose: To elucidate the differences in ocular biometric parameters by generation and gender and to identify axial length (AL)-associated genetic variants in Japanese individuals, we analyzed Tohoku Medical Megabank Organization (ToMMo) Eye Study data. Design: We designed the ToMMo Eye Study, examined AL variations, and conducted genome-wide association studies (GWASs). Participants: In total, 33 483 participants aged > 18 years who were recruited into the community-based cohort (CommCohort) and the birth and three-generation cohort (BirThree Cohort) of the ToMMo Eye Study were examined. Methods: Each participant was screened with an interview, ophthalmic examinations, and a microarray analysis. The GWASs were performed in 22 379 participants in the CommCohort (discovery stage) and 11 104 participants in the BirThree Cohort (replication stage). We evaluated the associations of single nucleotide polymorphisms (SNPs) with AL using a genome-wide significance threshold (5 × 10-8) in each stage of the study and in the subsequent meta-analysis. Main Outcome Measures: We identified the association of SNPs with AL and distributions of AL in right and left eyes and individuals of different sexes and ages. Results: In the discovery stage, the mean AL of the right eye (23.99 mm) was significantly greater than that of the left eye (23.95 mm). This difference was reproducible across sexes and ages. The GWASs revealed 703 and 215 AL-associated SNPs with genome-wide significance in the discovery and validation stages, respectively, and many of the SNPs in the discovery stage were replicated in the validation stage. Validated SNPs and their associated loci were meta-analyzed for statistical significance (P < 5 × 10-8). This study identified 1478 SNPs spread over 31 loci. Of the 31 loci, 5 are known AL loci, 15 are known refractive-error loci, 4 are known corneal-curvature loci, and 7 loci are newly identified loci that are not known to be associated with AL. Of note, some of them shared functional relationships with previously identified loci. Conclusions: Our large-scale GWASs exploiting ToMMo Eye Study data identified 31 loci linked to variations in AL, 7 of which are newly reported in this article. The results revealed genetic heterogeneity and similarity in SNPs related to ethnic variations in AL.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values.

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.