使用基于机器学习的表型化进行全基因组关联分析,揭示了职业创造力的遗传架构,并与精神疾病重叠
Hyejin Kim1, Yeeun Ahn1, Joohyun Yoon2
1Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Psychiatry research
|February 9, 2024
概括
这项研究使用机器学习在一项大型全基因组关联研究 (GWAS) 中表型职业创造力 (OC). 研究结果揭示了创造力和精神障碍之间的遗传联系,促进了对它们复杂关系的理解.
科学领域:
- 遗传学 遗传学 是一个
- 精神病学是一个精神病学.
- 认知神经科学 认知神经科学
背景情况:
- 创造力是遗传的,并与精神疾病结合在一起,但它们之间的关系是复杂的.
- 研究创造力一直是具有挑战性的,因为在精确的定义和测量方面存在困难.
研究的目的:
- 通过基于机器学习的表型化方法研究职业创造力 (OC) 的遗传结构.
- 探索创造力和精神疾病之间的遗传重叠和关系.
主要方法:
- 进行了对OC最大的全基因组关联研究 (GWAS),涉及241,736名英国生物库参与者.
- 运用了基于机器学习 (ML) 的扩展和验证的表型化方法,用于OC.
- 进行了GWAS后的分析,包括对双变异因果混合物的建模,以评估遗传重叠.
主要成果:
- 确定了25种新的变体和3种与教育成就和精神疾病相关的候选因果基因.
- 揭示了OC和精神疾病之间广泛的遗传重叠,具有混合效应的方向.
- 在OC GWAS和教育调整后的GWAS之间发现了强烈的遗传相关性 (r_g = 0.95).
结论:
- 基于ML的OC的表型识别能够更深入地了解其遗传结构.
- 这些发现表明,创造力和精神疾病之间存在共同的遗传影响.
- 这项研究可能会为人类认知和心理健康的未来遗传发现和预测提供信息.
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