Abstract
Background: Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. Methods: A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Outcomes: Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses.Interpretation: This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.
Competing Interest Statement
PFS is consultant and shareholder at Neumora Therapeutics. No other authors report potential conflicts of interest.
Funding Statement
This work was supported by the US National Institutes of Mental Health (R01MH123724), the European Unions Horizon 2020 Research and Innovation Programme (CoMorMent project; Grant #847776) and the European Research Council (grant agreement ID 101042183). The OSMR analyses have been conducted using the UK Biobank Resource under Application Number 22224. They were enabled by resources in project sens2017519 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973, and in project 2023/5-402 provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at PDC, partially funded by the Swedish Research Council through grant agreement no. 2022-06725. JAP was funded by Forte - The Swedish Research Council For Health, Working Life And Welfare (agreement 2022-00814), the US National Institutes of Mental Health (R01MH123724), and by the Amsterdam University Medical Center Postdoc Career Bridging grant (27527). KWC was supported in part by funding from the National Institute of Mental Health (K08MH127413). PFS gratefully acknowledges support from the Swedish Research Council (Vetenskapsradet, award D0886501) and the US National Institutes of Mental Health (R01s MH124871, MH121545, and MH123724). YL was supported by the European Research Council (grant agreement ID 101042183).
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
This study has made use of summary statistics of genome-wide association studies, most of which are available at https://www.ebi.ac.uk/gwas/. In addition, individual-level data from UK-Biobank was used, accessed under application 22224. These data are available to researchers and are IRB-approved; https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics
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Yes
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Data Availability
Sources to download publicly available data are listed in the paper's supplementary materials. Code will be published upon journal acceptance https://github.com/KI-PGI
