Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits
Publication: American Journal of Psychiatry
Abstract
Objective:
Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD.
Methods:
Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks.
Results:
Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications.
Conclusions:
This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.
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History
Received: 27 March 2023
Revision received: 27 July 2023
Revision received: 7 September 2023
Revision received: 6 October 2023
Accepted: 12 October 2023
Published online: 15 May 2024
Published in print: July 01, 2024
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Competing Interests
Dr. McCoy has received support from grants to his institution from Koa Health and InterSystems, and he has received an honorarium from Springer Nature for editorial work. Dr. Perlis has served as a consultant or scientific advisory board member for Alkermes, Belle Artificial Intelligence, Burrage Capital, Circular Genomics, Genomind, and Vault Health; he serves as an associate editor for JAMA Network–Open; and he has equity in Belle Artificial Intelligence, Circular Genomics, Genomind, Psy Therapeutics, and Vault Health. Dr. Ruderfer has served on advisory boards for Alkermes and Illumina and has received research funds from PTC Therapeutics. The other authors report no financial relationships with commercial interests.
Funding Information
This work was funded by NIMH grants R01MH116269 (Drs. Ruderfer and Walsh), R01MH121455 (Drs. Ruderfer and Walsh), R01MH116270 (Dr. Perlis), and R01MH123804 (Dr. Perlis) and National Institute of General Medical Sciences grant 5T32GM007347 (Dr. Kang). The data sets used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding, private agencies, and federal grants. These include the NIH-funded Shared Instrumentation Grant S10RR025141 and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include grants U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, and R01HD074711, and additional funding sources listed at https://victr.vumc.org/biovu-funding/. This research is also based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award MVP006. This publication does not represent the views of the VA or the U.S government. This study was also supported by NIH under award numbers K08MH122911 (Dr. Voloudakis), R01MH125246, R01AG067025, U01MH116442, and R01MH109677 (Dr. Roussos), and by Veterans Affairs Merit grants BX002395 and BX004189 (Dr. Roussos). Dr. Kim was supported by NIMH grant K99 MH127366, the Phyllis and Jerome Lyle Rappaport Foundation, and the Foundation for the National Institutes of Health (FNIH) Deeda Blair Research Initiative for Disorders of the Brain. This study has also been funded in part by the Brain and Behavior Research Foundation via a 2020 NARSAD Young Investigator Grant (Dr. Voloudakis).
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