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深度学习衍生的与眼相关的内类型使得全基因组的新型遗传和功能发现成为可能

Liyin Chen^1,2, Yan Zhao¹, Saber Kazeminasab Hashemabad¹

¹Department of Ophthalmology, Mass Eye and Ear, Harvard Medical School; Boston, Massachusetts, MA, USA.

medRxiv : the preprint server for health sciences

|November 24, 2025

概括

机器学习通过分析光学连贯断层扫描来确定初级开角光眼 (POAG) 的新型遗传位置. 这种方法扩大了对POAG的理解.

科学领域:

眼科医生眼科眼科
遗传学是一个遗传学.
人工智能的人工智能

背景情况:

主要开角青光眼 (POAG) 是导致不可逆转失明的主要原因,其遗传基础尚不完全理解.
之前的全基因组关联研究 (GWAS) 受到来自电子健康记录的不精确表型的限制.

研究的目的:

通过机器学习 (ML) 框架和精确的内分类型,识别与POAG相关的新型遗传位点.
阐明POAG.背后的遗传结构和致病机制.

主要方法:

开发了一种疾病训练,任务转移的ML框架,从光学连贯断层扫描 (OCT) 扫描中得出与眼相关的损伤模式.
在英国生物库参与者中,在欧洲,非洲和亚洲的祖先中,向GWAS应用了ML衍生的OCT内类型,随后进行了跨祖先的元分析.
进行了广泛的功能分析,包括贝叶斯的局部化和门德尔的随机化,以确定高可信度的效应基因.

主要成果:

确定了36个 (欧元) 和43个 (交叉祖先) 新型基因组广泛关联研究 (GWAS) 位点,用于POAG.
超过三分之二的确定的基因与已知的POAG关联重叠,验证了ML方法.
发现了21个新型基因位点和11个高可信度效应基因,五个新型的玻璃眼,涉及Wnt介导的外流和视网膜质细胞脆弱性.

结论:

基于ML的策略有效地确定了新的POAG遗传关联和潜在的治疗点.
结果提供了在细胞类型分辨率下对POAG病原体的机制性见解.
这种可通用的ML方法可以加速复杂疾病的发现.

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