김혜진 (성균관대학교 삼성융합의과학원) | 한빛사논문 > 한빛사

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김혜진Hyejin Kim

성균관대학교 삼성융합의과학원

Psychiatry Res., Available online 1 February 2024, 115753 | https://doi.org/10.1016/j.psychres.2024.115753 Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders

Hyejin Kim ^a†, Yeeun Ahn ^a†, Joohyun Yoon ^b†, Kyeongmin Jung ^a,b, Soyeon Kim ^a, Injeong Shim ^a, Tae Hwan Park ^c, Hyunwoong Ko ^d,e,f, Sang-Hyuk Jung ^a, Jaeyoung Kim ^a,b, Sanghyeon Park ^a, Dong June Lee ^g, Sunho Choi ^h, Soojin Cha ^a, Beomsu Kim ^a, Min Young Cho^a, Hyunbin Cho ^a, Dan Say Kim ^a, Yoonjeong Jang ^b,i, Hong Kyu Ihm ^b, Woong-Yang Park ^j, Hasan Bakhshi ^k, Kevin S. O'Connell ^l, Ole A. Andreassen^l, Kenneth S. Kendler ^m, Woojae Myung ^b,h, Hong-Hee Won ^a,j

^aDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea

^bDepartment of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea

^cDepartment of Plastic Surgery, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwasung, South Korea

^dInterdisciplinary Program in Cognitive Science, Seoul National University, Seoul, South Korea

^eDepartment of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea

^fDental Research Institute, Seoul National University School of Dentistry, Seoul, South Korea

^gDepartment of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Korea

^hDepartment of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea

ⁱDepartment of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea

^jSamsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

^kCreative Industries Policy and Evidence Centre, Nesta, London, United Kingdom

^lNorwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

^mDepartment of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA

^†These individuals contributed equally to this work as co-first authors.

Corresponding Authors: Hong-Hee Won, Woojae Myung

Abstract

Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.

논문정보

형식Research article
게재일2024년 02월 (BRIC 등록일 2024-02-05)
연구진국내(교신)+국외 연구진
분야 생명과학 > 유전학

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