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The Intersection of Metabolomics and Genomics and Their Role in Human Health

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A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 37784

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Prof. Dr. Karsten Suhre

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Weill Cornell Medicine-Qatar, Education City, Qatar
Interests: phenome-; genome-; epi-genome wide association studies with deep molecular phenotypes

Special Issue Information

Dear Colleagues,

Over 65 genome-wide association studies with metabolomics (mGWAS) have been published so far (http://www.metabolomix.com/list-of-all-published-gwas-with-metabolomics/), reporting hundreds of gene variant-to-metabolite associations (mQTLs). These mQTLs are of high value for many biomedical and pharmaceutical research applications. For instance, when mQTLs overlap (colocalize) with disease associations, they can support drug target prioritization by providing functional evidence in humans for the hypothesized pathogenic pathways. mQTLs are also a major source for biomedical hypothesis generation and can lead to targeted experiments that characterize gene function or unknown metabolites. The topic of this special issue, “The Intersection of Metabolomics and and Genomics and Their Role in Human Health” is broadly defined to include all studies that bring a metabolomics perspective to the role of genetic and epi-genetic variation in human health. This special issue therefore includes, but is not limited to, papers on new mQTL association studies (including replications in new populations), broad systems biology approaches that use existing mGWAS data (including studies on polygenic scores and Mendelian randomization with metabolites), and also dedicated targeted experimental studies with metabolites that are inspired by hypotheses generated from mQTLs (e.g. the characterization of gene function, unknown metabolites, identification of causal genes, etc.). Papers that link metabolites to epi-genetic modifications are also welcome.

Prof. Dr. Karsten Suhre
Guest Editor

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Keywords

Genome-wide association studies with metabolomics (mGWAS)
Epigenome-wide association studies with metabolomics (mEWAS)
Functional characterization of mQTLs
Causal gene identification
Mendelian randomisation
Polygenic scores
Gene-environment interaction

Published Papers (12 papers)

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Research

18 pages, 2339 KiB

Open AccessArticle

The Roles of Gut Microbiome and Plasma Metabolites in the Associations between ABO Blood Groups and Insulin Homeostasis: The Microbiome and Insulin Longitudinal Evaluation Study (MILES)

by Ruifang Li-Gao, Kirk Grubbs, Alain G. Bertoni, Kristi L. Hoffman, Joseph F. Petrosino, Gautam Ramesh, Martin Wu, Jerome I. Rotter, Yii-Der Ida Chen, Anne M. Evans, Richard J. Robinson, Laura Sommerville, Dennis Mook-Kanamori, Mark O. Goodarzi, Gregory A. Michelotti and Patricia A. Sheridan

Metabolites 2022, 12(9), 787; https://doi.org/10.3390/metabo12090787 - 25 Aug 2022

Viewed by 2094

Abstract

Non-O blood groups are associated with decreased insulin sensitivity and risk of type 2 diabetes. A recent study pinpointed the associations between ABO blood groups and gut microbiome, which may serve as potential mediators for the observed increased disease risks. We aimed to characterize associations between ABO haplotypes and insulin-related traits as well as potential mediating pathways. We assessed insulin homeostasis in African Americans (AAs; n = 109) and non-Hispanic whites (n = 210) from the Microbiome and Insulin Longitudinal Evaluation Study. The ABO haplotype was determined by six SNPs located in the ABO gene. Based on prior knowledge, we included 21 gut bacteria and 13 plasma metabolites for mediation analysis. In the white study cohort (60 ± 9 years, 42% male), compared to the O1 haplotype, A1 was associated with a higher Matsuda insulin sensitivity index, while a lower relative abundance of Bacteroides massiliensis and lactate levels. Lactate was a likely mediator of this association but not Bacteroides massiliensis. In the AAs group (57 ± 8 years, 33% male), we found no association between any haplotype and insulin-related traits. In conclusion, the A1 haplotype may promote healthy insulin sensitivity in non-Hispanic whites and lactate likely play a role in this process but not selected gut bacteria. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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15 pages, 1114 KiB

Open AccessArticle

Crosstalk between Host Genome and Metabolome among People with HIV in South Africa

by Chang Liu, Zicheng Wang, Qin Hui, Yiyun Chiang, Junyu Chen, Jaysingh Brijkumar, Johnathan A. Edwards, Claudia E. Ordonez, Mathew R. Dudgeon, Henry Sunpath, Selvan Pillay, Pravi Moodley, Daniel R. Kuritzkes, Mohamed Y. S. Moosa, Dean P. Jones, Vincent C. Marconi and Yan V. Sun

Metabolites 2022, 12(7), 624; https://doi.org/10.3390/metabo12070624 - 6 Jul 2022

Cited by 3 | Viewed by 1948

Abstract

Genome-wide association studies (GWAS) of circulating metabolites have revealed the role of genetic regulation on the human metabolome. Most previous investigations focused on European ancestry, and few studies have been conducted among populations of African descent living in Africa, where the infectious disease burden is high (e.g., human immunodeficiency virus (HIV)). It is important to understand the genetic associations of the metabolome in diverse at-risk populations including people with HIV (PWH) living in Africa. After a thorough literature review, the reported significant gene–metabolite associations were tested among 490 PWH in South Africa. Linear regression was used to test associations between the candidate metabolites and genetic variants. GWAS of 154 plasma metabolites were performed to identify novel genetic associations. Among the 29 gene–metabolite associations identified in the literature, we replicated 10 in South Africans with HIV. The UGT1A cluster was associated with plasma levels of biliverdin and bilirubin; SLC16A9 and CPS1 were associated with carnitine and creatine, respectively. We also identified 22 genetic associations with metabolites using a genome-wide significance threshold (p-value < 5 × 10⁻⁸). In a GWAS of plasma metabolites in South African PWH, we replicated reported genetic associations across ancestries, and identified novel genetic associations using a metabolomics approach. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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22 pages, 2299 KiB

Open AccessArticle

Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort

by Eva König, Johannes Rainer, Vinicius Verri Hernandes, Giuseppe Paglia, Fabiola Del Greco M., Daniele Bottigliengo, Xianyong Yin, Lap Sum Chan, Alexander Teumer, Peter P. Pramstaller, Adam E. Locke and Christian Fuchsberger

Metabolites 2022, 12(7), 604; https://doi.org/10.3390/metabo12070604 - 29 Jun 2022

Cited by 4 | Viewed by 2948

Abstract

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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16 pages, 1445 KiB

Open AccessArticle

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy

by Francois Brial, Lyamine Hedjazi, Kazuhiro Sonomura, Cynthia Al Hageh, Pierre Zalloua, Fumihiko Matsuda and Dominique Gauguier

Metabolites 2022, 12(7), 596; https://doi.org/10.3390/metabo12070596 - 27 Jun 2022

Cited by 1 | Viewed by 1975

Abstract

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography–high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10⁻⁸) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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17 pages, 1919 KiB

Open AccessArticle

Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight

by Ciarrah-Jane Shannon Barry, Deborah A. Lawlor, Chin Yang Shapland, Eleanor Sanderson and Maria Carolina Borges

Metabolites 2022, 12(6), 537; https://doi.org/10.3390/metabo12060537 - 10 Jun 2022

Cited by 2 | Viewed by 2344

Abstract

Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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14 pages, 2273 KiB

Open AccessArticle

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

by Le Chang, Guangyan Zhou, Huiting Ou and Jianguo Xia

Metabolites 2022, 12(6), 526; https://doi.org/10.3390/metabo12060526 - 7 Jun 2022

Cited by 6 | Viewed by 3243

Abstract

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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17 pages, 1902 KiB

Open AccessArticle

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies

by Alan Kuang, M. Geoffrey Hayes, Marie-France Hivert, Raji Balasubramanian, William L. Lowe, Jr. and Denise M. Scholtens

Metabolites 2022, 12(6), 512; https://doi.org/10.3390/metabo12060512 - 2 Jun 2022

Cited by 1 | Viewed by 2328

Abstract

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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11 pages, 1941 KiB

Open AccessArticle

Ratios of Acetaminophen Metabolites Identify New Loci of Pharmacogenetic Relevance in a Genome-Wide Association Study

by Gaurav Thareja, Anne M. Evans, Spencer D. Wood, Nisha Stephan, Shaza Zaghlool, Anna Halama, Gabi Kastenmüller, Aziz Belkadi, Omar M. E. Albagha, The Qatar Genome Program Research Consortium and Karsten Suhre

Metabolites 2022, 12(6), 496; https://doi.org/10.3390/metabo12060496 - 30 May 2022

Cited by 4 | Viewed by 3194

Abstract

Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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17 pages, 1972 KiB

Open AccessArticle

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

by Fiona A. Hagenbeek, Jenny van Dongen, René Pool, Amy C. Harms, Peter J. Roetman, Vassilios Fanos, Britt J. van Keulen, Brian R. Walker, Naama Karu, Hilleke E. Hulshoff Pol, Joost Rotteveel, Martijn J. J. Finken, Robert R. J. M. Vermeiren, Cornelis Kluft, Meike Bartels, Thomas Hankemeier and Dorret I. Boomsma

Metabolites 2022, 12(6), 474; https://doi.org/10.3390/metabo12060474 - 24 May 2022

Cited by 4 | Viewed by 2212

Abstract

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7–12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25–0.64), 0.50 (range: 0.33–0.62), and 0.64 (range: 0.43–0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37–0.68), 0.50 (range; 0.23–0.61), and 0.47 (range: 0.32–0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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11 pages, 1068 KiB

Open AccessArticle

Mendelian Randomization Analysis Identifies Blood Tyrosine Levels as a Biomarker of Non-Alcoholic Fatty Liver Disease

by Émilie Gobeil, Ina Maltais-Payette, Nele Taba, Francis Brière, Nooshin Ghodsian, Erik Abner, Jérôme Bourgault, Eloi Gagnon, Hasanga D. Manikpurage, Christian Couture, Patricia L. Mitchell, Patrick Mathieu, François Julien, Jacques Corbeil, Marie-Claude Vohl, Sébastien Thériault, Tõnu Esko, André Tchernof and Benoit J. Arsenault

Metabolites 2022, 12(5), 440; https://doi.org/10.3390/metabo12050440 - 13 May 2022

Cited by 16 | Viewed by 3719

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a complex disease associated with premature mortality. Its diagnosis is challenging, and the identification of biomarkers causally influenced by NAFLD may be clinically useful. We aimed at identifying blood metabolites causally impacted by NAFLD using two-sample Mendelian randomization (MR) with validation in a population-based biobank. Our instrument for genetically predicted NAFLD included all independent genetic variants from a recent genome-wide association study. The outcomes included 123 blood metabolites from 24,925 individuals. After correction for multiple testing, a positive effect of NAFLD on plasma tyrosine levels but not on other metabolites was identified. This association was consistent across MR methods and was robust to outliers and pleiotropy. In observational analyses performed in the Estonian Biobank (10,809 individuals including 359 patients with NAFLD), after multivariable adjustment, tyrosine levels were positively associated with the presence of NAFLD (odds ratio per 1 SD increment = 1.23 [95% confidence interval = 1.12–1.36], p = 2.19 × 10⁻⁵). In a small proof-of-concept study on bariatric surgery patients, blood tyrosine levels were higher in patients with NAFLD than without. This study revealed a potentially causal effect of NAFLD on blood tyrosine levels, suggesting it may represent a new biomarker of NAFLD. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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13 pages, 915 KiB

Open AccessArticle

Metabolite Signature in the Carriers of Pathogenic Genetic Variants for Cardiomyopathy: A Population-Based METSIM Study

by Rowmika Ravi, Lilian Fernandes Silva, Jagadish Vangipurapu, Maleeha Maria, Joose Raivo, Seppo Helisalmi and Markku Laakso

Metabolites 2022, 12(5), 437; https://doi.org/10.3390/metabo12050437 - 13 May 2022

Cited by 1 | Viewed by 2390

Abstract

Hypertrophic (HCM) and dilated (DCM) cardiomyopathies are among the leading causes of sudden cardiac death. We identified 38 pathogenic or likely pathogenic variant carriers for HCM in three sarcomere genes (MYH7, MYBPC3, TPMI) among 9.928 participants of the METSIM Study having whole exome sequencing data available. Eight of them had a clinical diagnosis of HCM. We also identified 20 pathogenic or likely pathogenic variant carriers for DCM in the TTN gene, and six of them had a clinical diagnosis of DCM. The aim of our study was to investigate the metabolite signature in the carriers of the pathogenic or likely pathogenic genetic variants for HCM and DCM, compared to age- and body-mass-index-matched controls. Our novel findings were that the carriers of pathogenic or likely pathogenic variants for HCM had significantly increased concentrations of bradykinin (des-arg 9), vanillactate, and dimethylglycine and decreased concentrations of polysaturated fatty acids (PUFAs) and lysophosphatidylcholines compared with the controls without HCM. Additionally, our novel findings were that the carriers of pathogenic or likely pathogenic variants for DCM had significantly decreased concentrations of 1,5-anhydrogluticol, histidine betaine, N-acetyltryptophan, and methylsuccinate and increased concentrations of trans-4-hydroxyproline compared to the controls without DCM. Our population-based study shows that the metabolite signature of the genetic variants for HCM and DCM includes several novel metabolic pathways not previously described. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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13 pages, 891 KiB

Open AccessArticle

Metabolome Genome-Wide Association Study Identifies 74 Novel Genomic Regions Influencing Plasma Metabolites Levels

by Pirro G. Hysi, Massimo Mangino, Paraskevi Christofidou, Mario Falchi, Edward D. Karoly, NIHR Bioresource Investigators, Robert P. Mohney, Ana M. Valdes, Tim D. Spector and Cristina Menni

Metabolites 2022, 12(1), 61; https://doi.org/10.3390/metabo12010061 - 11 Jan 2022

Cited by 13 | Viewed by 6875

Abstract

Metabolites are small products of metabolism that provide a snapshot of the wellbeing of an organism and the mechanisms that control key physiological processes involved in health and disease. Here we report the results of a genome-wide association study of 722 circulating metabolite levels in 8809 subjects of European origin, providing both breadth and depth. These analyses identified 202 unique genomic regions whose variations are associated with the circulating levels of 478 different metabolites. Replication with a subset of 208 metabolites that were available in an independent dataset for a cohort of 1768 European subjects confirmed the robust associations, including 74 novel genomic regions not associated with any metabolites in previous works. This study enhances our knowledge of genetic mechanisms controlling human metabolism. Our findings have major potential for identifying novel targets and developing new therapeutic strategies. Full article

(This article belongs to the Special Issue The Intersection of Metabolomics and Genomics and Their Role in Human Health)

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