Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

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

doi:10.1016/j.isci.2023.108473

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

iScience. 2023 Nov 14;26(12):108473. doi: 10.1016/j.isci.2023.108473. eCollection 2023 Dec 15.

Authors

Cantin Baron^{1

2}, Sarah Cherkaoui^{2

3

4}, Sandra Therrien-Laperriere², Yann Ilboudo^{1

2}, Raphaël Poujol², Pamela Mehanna², Melanie E Garrett⁵, Marilyn J Telen⁶, Allison E Ashley-Koch⁵, Pablo Bartolucci^{7

8}, John D Rioux^{1

2

9}, Guillaume Lettre^{2

9}, Christine Des Rosiers^{1

2

10}, Matthieu Ruiz^{2

10}, Julie G Hussin^{2

9}

Affiliations

¹ Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada.
² Montreal Heart Institute, Montréal, QC, Canada.
³ Division of Oncology and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
⁴ Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, France.
⁵ Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA.
⁶ Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USA.
⁷ Université Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center - UMGGR, Créteil, France.
⁸ Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, France.
⁹ Département de Médecine, Université de Montréal, Montréal, QC, Canada.
¹⁰ Département de Nutrition, Université de Montréal, Montréal, QC, Canada.

Abstract

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.

Keywords: Association analysis; Computational bioinformatics; Quantitative genetics.

Abstract

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