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.
摘要：
已知创造力是可遗传的，并表现出患有精神疾病的家族聚集性；然而，他们关系的复杂性还没有得到很好的确立。在本研究中,我们证明，使用扩展和验证的基于机器学习（ML）的职业创造力表型（OC）可以让我们进一步了解创造力的特点，这在以前很难定义和研究。我们对来自英国生物库的241,736名参与者进行了最大的全基因组关联研究（GWAS），并确定了25个尚未报告的铅变异和三个候选因果基因，这些基因以前与教育程度和精神疾病有关。通过各种后GWAS分析，我们发现OC和精神疾病之间存在广泛的遗传重叠，具有混合效应方向。包括双变量因果混合模型。此外,我们发现我们的原始GWAS与针对教育年限调整的GWAS之间存在强烈的遗传相关性(rg=0.95).我们通过基于ML的表型分析进行的GWAS分析有助于理解创造力的遗传结构，这可能为人类认知和精神疾病的遗传发现和遗传预测提供信息。
