Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results
Summary: Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical si...
| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Langue: | English |
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Elsevier
2023-12-01
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| Collection: | iScience |
| Sujets: | |
| Accès en ligne: | http://www.sciencedirect.com/science/article/pii/S2589004223025506 |
| _version_ | 1827582073646350336 |
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| author | Cantin Baron Sarah Cherkaoui Sandra Therrien-Laperriere Yann Ilboudo Raphaël Poujol Pamela Mehanna Melanie E. Garrett Marilyn J. Telen Allison E. Ashley-Koch Pablo Bartolucci John D. Rioux Guillaume Lettre Christine Des Rosiers Matthieu Ruiz Julie G. Hussin |
| author_facet | Cantin Baron Sarah Cherkaoui Sandra Therrien-Laperriere Yann Ilboudo Raphaël Poujol Pamela Mehanna Melanie E. Garrett Marilyn J. Telen Allison E. Ashley-Koch Pablo Bartolucci John D. Rioux Guillaume Lettre Christine Des Rosiers Matthieu Ruiz Julie G. Hussin |
| author_sort | Cantin Baron |
| collection | DOAJ |
| description | Summary: Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks. |
| first_indexed | 2024-03-08T22:45:18Z |
| format | Article |
| id | doaj.art-7aa4fc355e824ed1a1b9f6bcb1ad3e8f |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T22:45:18Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-7aa4fc355e824ed1a1b9f6bcb1ad3e8f2023-12-17T06:40:55ZengElsevieriScience2589-00422023-12-012612108473Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies resultsCantin Baron0Sarah Cherkaoui1Sandra Therrien-Laperriere2Yann Ilboudo3Raphaël Poujol4Pamela Mehanna5Melanie E. Garrett6Marilyn J. Telen7Allison E. Ashley-Koch8Pablo Bartolucci9John D. Rioux10Guillaume Lettre11Christine Des Rosiers12Matthieu Ruiz13Julie G. Hussin14Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada; Montreal Heart Institute, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, Canada; Division of Oncology and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, FranceMontreal Heart Institute, Montréal, QC, CanadaDépartement de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada; Montreal Heart Institute, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, CanadaDuke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USADivision of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USADuke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USAUniversité Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center – UMGGR, Créteil, France; Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, FranceDépartement de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada; Montreal Heart Institute, Montréal, QC, Canada; Département de Médecine, Université de Montréal, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, Canada; Département de Médecine, Université de Montréal, Montréal, QC, CanadaDépartement de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada; Montreal Heart Institute, Montréal, QC, Canada; Département de Nutrition, Université de Montréal, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, Canada; Département de Nutrition, Université de Montréal, Montréal, QC, CanadaMontreal Heart Institute, Montréal, QC, Canada; Département de Médecine, Université de Montréal, Montréal, QC, Canada; Corresponding authorSummary: Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.http://www.sciencedirect.com/science/article/pii/S2589004223025506Association analysisComputational bioinformaticsQuantitative genetics |
| spellingShingle | Cantin Baron Sarah Cherkaoui Sandra Therrien-Laperriere Yann Ilboudo Raphaël Poujol Pamela Mehanna Melanie E. Garrett Marilyn J. Telen Allison E. Ashley-Koch Pablo Bartolucci John D. Rioux Guillaume Lettre Christine Des Rosiers Matthieu Ruiz Julie G. Hussin Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results iScience Association analysis Computational bioinformatics Quantitative genetics |
| title | Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results |
| title_full | Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results |
| title_fullStr | Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results |
| title_full_unstemmed | Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results |
| title_short | Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results |
| title_sort | gene metabolite annotation with shortest reactional distance enhances metabolite genome wide association studies results |
| topic | Association analysis Computational bioinformatics Quantitative genetics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223025506 |
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