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Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS, Su CY, Raina P, Greenwood CMT, Farjoun Y, Forgetta V, Langenberg C, Zhou S, Ohlsson C, Richards JB. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 2023; 55:44-53. [PMID: 36635386 PMCID: PMC7614162 DOI: 10.1038/s41588-022-01270-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
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Affiliation(s)
- Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Agustin Cerani
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Julian Daniel Sunday Willett
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Fulcrum Genomics, Boulder, CO, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- 5 Prime Sciences Inc, Montreal, Quebec, Canada.
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Twin Research, King's College London, London, UK.
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2
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Whole-Genome Profile of Greek Patients with Teratozοοspermia: Identification of Candidate Variants and Genes. Genes (Basel) 2022; 13:genes13091606. [PMID: 36140773 PMCID: PMC9498395 DOI: 10.3390/genes13091606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/23/2022] [Accepted: 08/30/2022] [Indexed: 01/09/2023] Open
Abstract
Male infertility is a global health problem that affects a large number of couples worldwide. It can be categorized into specific subtypes, including teratozoospermia. The present study aimed to identify new variants associated with teratozoospermia in the Greek population and to explore the role of genes on which these were identified. For this reason, whole-genome sequencing (WGS) was performed on normozoospermic and teratozoospermic individuals, and after selecting only variants found in teratozoospermic men, these were further prioritized using a wide range of tools, functional and predictive algorithms, etc. An average of 600,000 variants were identified, and of them, 61 were characterized as high impact and 153 as moderate impact. Many of these are mapped in genes previously associated with male infertility, yet others are related for the first time to teratozoospermia. Furthermore, pathway enrichment analysis and Gene ontology (GO) analyses revealed the important role of the extracellular matrix in teratozoospermia. Therefore, the present study confirms the contribution of genes studied in the past to male infertility and sheds light on new molecular mechanisms by providing a list of variants and candidate genes associated with teratozoospermia in the Greek population.
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3
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Molecular Portrait of an Athlete. Diagnostics (Basel) 2021; 11:diagnostics11061095. [PMID: 34203902 PMCID: PMC8232626 DOI: 10.3390/diagnostics11061095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/01/2021] [Accepted: 06/11/2021] [Indexed: 01/15/2023] Open
Abstract
Sequencing of the human genome and further developments in "omics" technologies have opened up new possibilities in the study of molecular mechanisms underlying athletic performance. It is expected that molecular markers associated with the development and manifestation of physical qualities (speed, strength, endurance, agility, and flexibility) can be successfully used in the selection systems in sports. This includes the choice of sports specialization, optimization of the training process, and assessment of the current functional state of an athlete (such as overtraining). This review summarizes and analyzes the genomic, proteomic, and metabolomic studies conducted in the field of sports medicine.
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4
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Yazdani H, Cheng LL, Christiani DC, Yazdani A. Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:526-535. [PMID: 30222581 PMCID: PMC10350680 DOI: 10.1109/tcbb.2018.2869757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables us to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with the potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of a healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.
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Al-Khelaifi F, Diboun I, Donati F, Botrè F, Abraham D, Hingorani A, Albagha O, Georgakopoulos C, Suhre K, Yousri NA, Elrayess MA. Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance. Sci Rep 2019; 9:19889. [PMID: 31882771 PMCID: PMC6934758 DOI: 10.1038/s41598-019-56496-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 12/12/2019] [Indexed: 01/10/2023] Open
Abstract
Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.
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Affiliation(s)
- Fatima Al-Khelaifi
- Anti Doping Laboratory Qatar, Sports City, Doha, Qatar.,Division of Medicine, University College London, London, NW3 2PF, United Kingdom
| | - Ilhame Diboun
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Francesco Donati
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy
| | - Francesco Botrè
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy
| | - David Abraham
- Division of Medicine, University College London, London, NW3 2PF, United Kingdom
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, University College London, London, WC1E 6BT, United Kingdom
| | - Omar Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.,Center for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar-Foundation, P.O. Box 24144, Doha, Qatar
| | - Noha A Yousri
- Department of Genetic Medicine, Weill Cornell Medical College in Qatar, Qatar-Foundation, P.O. Box 24144, Doha, Qatar.,Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
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6
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Yazdani A, Yazdani A, Elsea SH, Schaid DJ, Kosorok MR, Dangol G, Samiei A. Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics. BMC Genomics 2019; 20:395. [PMID: 31113383 PMCID: PMC6528192 DOI: 10.1186/s12864-019-5772-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 05/03/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. RESULTS The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. CONCLUSION Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.
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Affiliation(s)
| | - Akram Yazdani
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029 USA
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Daniel J. Schaid
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905 USA
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Gita Dangol
- Health Science Center, The University of Texas MD Anderson Cancer Center, Austin, TX 77030 USA
| | - Ahmad Samiei
- Hasso Plattner Institute, 14482 Potsdam, Germany
- Climax Data Pattern, Boston, MA USA
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7
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Yazdani A, Yazdani A, Méndez Giráldez R, Aguilar D, Sartore L. A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function. Sci Rep 2019; 9:5845. [PMID: 30971721 PMCID: PMC6458140 DOI: 10.1038/s41598-019-41362-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 03/05/2019] [Indexed: 01/29/2023] Open
Abstract
Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.
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Affiliation(s)
- Akram Yazdani
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Climax Data Pattern, Boston, MA, USA.
| | - Azam Yazdani
- School of Medicine, Boston University, Boston, MA, USA
| | - Raúl Méndez Giráldez
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Luca Sartore
- National Institute of Statistical Science, Washington, DC, USA
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8
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Graham E, Lee J, Price M, Tarailo-Graovac M, Matthews A, Engelke U, Tang J, Kluijtmans LAJ, Wevers RA, Wasserman WW, van Karnebeek CDM, Mostafavi S. Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review. J Inherit Metab Dis 2018; 41:435-445. [PMID: 29721916 PMCID: PMC5959954 DOI: 10.1007/s10545-018-0139-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Revised: 12/19/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
Abstract
Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.
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Affiliation(s)
- Emma Graham
- Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Jessica Lee
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Magda Price
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Maja Tarailo-Graovac
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Allison Matthews
- Department of Pediatrics, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Udo Engelke
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeffrey Tang
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Leo A J Kluijtmans
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ron A Wevers
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Wyeth W Wasserman
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Clara D M van Karnebeek
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.
- Department of Pediatrics, BC Children's Hospital Research Institute, Vancouver, BC, Canada.
- Departments of Pediatrics and Clinical Genetics, Emma Children's Hospital, Academic Medical Centre, Amsterdam, The Netherlands.
| | - Sara Mostafavi
- BC Children's Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada.
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9
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Yousri NA, Fakhro KA, Robay A, Rodriguez-Flores JL, Mohney RP, Zeriri H, Odeh T, Kader SA, Aldous EK, Thareja G, Kumar M, Al-Shakaki A, Chidiac OM, Mohamoud YA, Mezey JG, Malek JA, Crystal RG, Suhre K. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat Commun 2018; 9:333. [PMID: 29362361 PMCID: PMC5780481 DOI: 10.1038/s41467-017-01972-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 10/30/2017] [Indexed: 12/30/2022] Open
Abstract
Metabolomics-genome-wide association studies (mGWAS) have uncovered many metabolic quantitative trait loci (mQTLs) influencing human metabolic individuality, though predominantly in European cohorts. By combining whole-exome sequencing with a high-resolution metabolomics profiling for a highly consanguineous Middle Eastern population, we discover 21 common variant and 12 functional rare variant mQTLs, of which 45% are novel altogether. We fine-map 10 common variant mQTLs to new metabolite ratio associations, and 11 common variant mQTLs to putative protein-altering variants. This is the first work to report common and rare variant mQTLs linked to diseases and/or pharmacological targets in a consanguineous Arab cohort, with wide implications for precision medicine in the Middle East. Blood metabolites are influenced by a combination of genetic and environmental factors. Here, Yousri and colleagues perform a whole-exome sequencing study in combination with a metabolomics analysis to identify metabolic quantitative trait loci in a Middle Eastern population.
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Affiliation(s)
- Noha A Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar. .,Computer and Systems Engineering, Alexandria University, Alexandria, Egypt.
| | - Khalid A Fakhro
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar. .,Sidra Medical Research Center, Department of Human Genetics, PO Box 26999, Doha, Qatar.
| | - Amal Robay
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | | | | | - Hassina Zeriri
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Tala Odeh
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Sara Abdul Kader
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Eman K Aldous
- Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Gaurav Thareja
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Manish Kumar
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Alya Al-Shakaki
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Omar M Chidiac
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Yasmin A Mohamoud
- Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Jason G Mezey
- Genetic Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Joel A Malek
- Genetic Medicine, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar.,Genomics Core, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar
| | - Ronald G Crystal
- Genetic Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Karsten Suhre
- Physiology and Biophysics, Weill Cornell Medicine-Qatar, PO Box 24144, Doha, Qatar.
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10
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Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet 2017; 49:568-578. [PMID: 28263315 DOI: 10.1038/ng.3809] [Citation(s) in RCA: 269] [Impact Index Per Article: 38.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 02/10/2017] [Indexed: 02/07/2023]
Abstract
Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10-11). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.
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