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Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach

Abstract

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

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Fig. 1: Study flowchart.
Fig. 2: Overview of the targeted sequencing panel design strategy from candidate genes and SNPs for MD and obesity.
Fig. 3: Venn diagram of the variants significantly associated with each of the three phenotypes evaluated.
Fig. 4: Distribution of the identified rare variants among the significantly associated genes with comorbid MD and obesity.
Fig. 5: Venn diagram of the genes significantly associated with each of three phenotypes evaluated through rare variation.
Fig. 6: Venn diagram of the gene-sets obtained in the GSEA for each of three phenotypes evaluated.
Fig. 7: Heatmap with GO and Reactome GSEA summary results.

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Data availability

The datasets analyzed during the current study are available in the European Genome-Phenome Archive repository (https://ega-archive.org/datasets/EGAD50000000476).

Code availability

Code for processing sequencing data is extensively explained in the “Methods” section of this manuscript and could be made available to editor and reviewers upon request.

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Acknowledgements

This study was partially funded by Consejería de Salud, Junta de Andalucía (PI322-2009), Consejería de Innovación, Proyecto de Excelencia (CTS-2010-6682), Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union (PI18/00238 and PI23/00201), FEDER/Junta de Andalucía (B-CTS-256-UGR20), the Marie Curie research Grants Scheme (FP7-626235) and by a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (22514). AMPG was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). ELI received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporación Program (grant code IJC2019-040080-I/AEI/10.13039/501100011033) and Ramon y Cajal Program (RYC2021-034816-I). RC was supported by a Postdoctoral Grant RH-0052-2021 from Junta de Andalucía and co-funded by the European Union, European Social Fund (FSE) 2014–2020.

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Contributions

Conceptualization: MR and LJMG. Methodology: MR, LJMG, JD, ELI, RC, CL and AMPG. Formal analysis: AMPG. Analysis supervision and results interpretation: RC, CL and ELI. Resources: MR, JAC and BG. Data curation: JPF and DLL. Writing – original draft: AMPG. Writing – review & editing: all authors. Visualization: AMPG. Supervision: MR, LJMG and JD. Funding acquisition: MR, EM, JAC and BG.

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Correspondence to Margarita Rivera.

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The authors declare no competing interests.

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The study was conducted in compliance with the Helsinki Declaration. The Research Ethics Committee of the University of Granada (UGR) approved the study and the written informed consents. Accordingly, the aforementioned Ethics Committee authorized the collection of biological samples and the development of genetic analyses on them at the meeting held on 24 April 2019.

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Pérez-Gutiérrez, A.M., Carmona, R., Loucera, C. et al. Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02609-2

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