解码人类视网膜中非编码变异的细胞类特定作用

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Leah S VandenBosch¹, Amy S Leonardson^1,2, Timothy J Cherry^3,4,5

¹Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.

Scientific reports

|December 11, 2025

概括

机器学习模型预测遗传变异如何影响遗传性视网膜疾病 (IRD). 使用单核表观遗传学数据,这些模型准确地识别了对监管元素的功能影响,并优先考虑了进一步研究的变体.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
计算生物学计算生物学

背景情况:

在cis-regulatory元素中的非编码变体与遗传性视网膜疾病 (IRDs) 有关.
这些调控变异的功能性表征是理解IRD病原学的重大挑战.
识别具有影响力的变体对于诊断和潜在治疗IRD至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测非编码变体对视网膜 cis-regulatory 元素的功能影响.
加强在遗传性视网膜疾病中引起疾病的变异的识别和优先考虑.
为了利用单核ATAC-seq数据进行细胞类特定变异效应预测.

主要方法:

实施了一个空隙k-mer支持向量机器 (SVM) 方法,该方法是在人类视网膜的单核ATAC-seq数据上训练的.
开发了18种不同的ML模型来预测变异对39437个细胞类特定监管元素的影响.
利用变异影响预测 (VIP) 评分,并将其与大规模并行报告员测试 (MPRA) 相关联,以验证.

主要成果:

在开发的ML模型中实现了超过90%的预测准确性 (AUROC),具有高细胞类特异性.
VIP评分有效突出了监管元素中的序列,包括对突变敏感的转录因子结合动机.
MPRA相关性证实了单核酸变异和indels的VIP分数的细胞类特定预测能力.

结论:

单核表观遗传学数据可以有效地用于预测非编码序列变异的功能影响.
开发的ML模型和VIP分数提供了一个强大的工具,用于在IRD中快速对患者变异进行功能分析.
这种方法促进了对遗传视网膜疾病的遗传贡献的理解,并促进了变异解释.

关键词:

关联调节元素的关联调节元素.机器学习是机器学习.没有编码的变体.视网膜疾病视网膜疾病

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