| Title: | Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders |
| Journal: | Psychiatry Research |
| Published: | 1 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38335777/ |
| DOI: | https://doi.org/10.1016/j.psychres.2024.115753 |
Publication 8808
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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.</p>
27 Authors
- Hyejin Kim
- Yeeun Ahn
- Joohyun Yoon
- Kyeongmin Jung
- Soyeon Kim
- Injeong Shim
- Tae Hwan Park
- Hyunwoong Ko
- Sang-Hyuk Jung
- Jaeyoung Kim
- Sanghyeon Park
- Dong June Lee
- Sunho Choi
- Soojin Cha
- Beomsu Kim
- Min Young Cho
- Hyunbin Cho
- Dan Say Kim
- Yoonjeong Jang
- Hong Kyu Ihm
- Woong-Yang Park
- Hasan Bakhshi
- Kevin S O Connell
- Ole A Andreassen
- Kenneth S Kendler
- Woojae Myung
- Hong-Hee Won
1 Application
| Application ID | Title |
|---|---|
| 33002 | Development of a computational method to predict disease risk |
Enabling scientific discoveries that improve human health