Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population

Koshiba, S.; Motoike, Ikuko N.; Saigusa, Daisuke; Inoue, Jin; Aoki, Yosuke; Tadaka, Shu; Shirota, Matsuyuki; Katsuoka, Fumiki; Tamiya, Gen; Minegishi, Naoko; Fuse, Nobuo; Kinoshita, Kengo; Yamamoto, Masayuki

doi:10.1038/s42003-020-01383-5

Cited by 21 publications

(16 citation statements)

References 68 publications

(127 reference statements)

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“…- The third variant, a stop-gain in ACSM2A (rs59261767, GrCh38, Chr16:20465682 C>T) increases the concentration of indolepropionic acid in plasma and completely abolishes the catalytic activity of ACSM2A (Koshiba et al, 2020). This variant, and the metabolic phenotype associated, are known to be more frequent in the Japanese population.…”

Section: Resultsmentioning

confidence: 99%

Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP

Correard

Arbour

Wasserman

2022

Preprint

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Genomic variation plays a crucial role in biology, serving as a base for evolution - allowing for adaptation on a species or population level. At the individual level, however, specific alleles can be implicated in diseases. To interpret genetic variants identified in an individual potentially affected with a rare genetic disease, it is fundamental to know the population frequency of each allele, ideally in an ancestry matched cohort. Equity in human genomics remains a challenge for the field, and there are not yet cohorts representing most populations. Currently, when ancestry matched cohorts are not available, pooled variant libraries are used, such as gnomAD, the Human Genome Diversity Project (HGDP) or the 1,000 Genomes Project (now known as IGSR: International Genome Sample Resource). When working with a pooled collection of variant frequencies, one of the challenges is to determine efficiently if a variant is broadly spread across populations or appears selectively in one or more populations. While this can be accomplished by reviewing tables of population frequencies, it can be advantageous to have a single score that summarizes the observed dispersion. This score would not require classifying individuals into populations, which can be complicated if it is a homogenous population, or can leave individuals excluded from all the predefined population groups. Moreover, a score would not display fine-scaled population information, which could have privacy implications and consequently be inappropriate to release. Therefore, we sought to develop a scoring method based on a Uniform Manifold Approximation and Projection (UMAP) where, for each allele, the score can range from 0 (the variant is limited to a subset of close individuals within the whole cohort) to 1 (the variant is spread among the individuals represented in the cohort). We call this score the Allele Dispersion Score (ADS). The scoring system was implemented on the IGSR dataset, and compared to the current method consisting in displaying variant frequencies for several populations in a table. The ADS correlates with the population frequencies, without requiring grouping of individuals.

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Section: Resultsmentioning

confidence: 99%

Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP

Correard

Arbour

Wasserman

2022

Preprint

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“…Moreover, the reference values should be evaluated by comparison with real clinical studies to determine whether they are more significant for biological variation than previous studies [ 59 ]. In addition, even if the methodologies are better for obtaining accurate values, it is essential to evaluate the utilization of metabolic profiles for biomarker research with large-scale data, such as genome-wide association studies and multiomics analyses [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…To date, most of the cohort metabolomics studies have used nuclear magnetic resonance, LC–MS, or GC–MS [ 1 ]. For example, the Tohoku Medical Megabank Organization (ToMMo), which is geographically close to the TMCS, established a protocol for metabolomic profiling using LC–MS for their cohort study to quantify 270 metabolites [ 3 , 36 ]. The CE-TOFMS that was used in the TMCS has the advantage of identifying ionic metabolites, which are major metabolites in cells.…”

Section: Discussionmentioning

confidence: 99%

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

Saito

Sugimoto

Hirayama

et al. 2021

JCM

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Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.

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Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population

Cited by 21 publications

References 68 publications

Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP

Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP

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

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

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