JooEun Kang, Victor M Castro, Michael Ripperger, Sanan Venkatesh, David Burstein, Richard Karlsson Linnér, Daniel B Rocha, Yirui Hu, Drew Wilimitis, Theodore Morley, Lide Han, Rachel Youngjung Kim, Yen-Chen Anne Feng, Tian Ge, Stephan Heckers, Georgios Voloudakis, Christopher Chabris, Panos Roussos, Thomas H McCoy, Colin G Walsh, Roy H Perlis, Douglas M Ruderfer
{"title":"耐药性抑郁症的全基因组关联研究:与代谢特征共享的生物学。","authors":"JooEun Kang, Victor M Castro, Michael Ripperger, Sanan Venkatesh, David Burstein, Richard Karlsson Linnér, Daniel B Rocha, Yirui Hu, Drew Wilimitis, Theodore Morley, Lide Han, Rachel Youngjung Kim, Yen-Chen Anne Feng, Tian Ge, Stephan Heckers, Georgios Voloudakis, Christopher Chabris, Panos Roussos, Thomas H McCoy, Colin G Walsh, Roy H Perlis, Douglas M Ruderfer","doi":"10.1176/appi.ajp.20230247","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":7656,"journal":{"name":"American Journal of Psychiatry","volume":" ","pages":"608-619"},"PeriodicalIF":15.1000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.\",\"authors\":\"JooEun Kang, Victor M Castro, Michael Ripperger, Sanan Venkatesh, David Burstein, Richard Karlsson Linnér, Daniel B Rocha, Yirui Hu, Drew Wilimitis, Theodore Morley, Lide Han, Rachel Youngjung Kim, Yen-Chen Anne Feng, Tian Ge, Stephan Heckers, Georgios Voloudakis, Christopher Chabris, Panos Roussos, Thomas H McCoy, Colin G Walsh, Roy H Perlis, Douglas M Ruderfer\",\"doi\":\"10.1176/appi.ajp.20230247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":7656,\"journal\":{\"name\":\"American Journal of Psychiatry\",\"volume\":\" \",\"pages\":\"608-619\"},\"PeriodicalIF\":15.1000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1176/appi.ajp.20230247\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1176/appi.ajp.20230247","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/15 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.
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.
期刊介绍:
The American Journal of Psychiatry, dedicated to keeping psychiatry vibrant and relevant, publishes the latest advances in the diagnosis and treatment of mental illness. The journal covers the full spectrum of issues related to mental health diagnoses and treatment, presenting original articles on new developments in diagnosis, treatment, neuroscience, and patient populations.