在大脑和心脏的细胞和发育背景中绘制常见和罕见的非编码变异的调节效应
Andrew R Marderstein1,2, Soumya Kundu3,4, Evin M Padhi1
1Department of Pathology, Stanford University, Stanford, CA, USA.
bioRxiv : the preprint server for biology
|March 3, 2025
概括
研究人员使用深度学习和单细胞数据来预测变异效应,识别与自闭症等神经发育障碍相关的罕见突变. 这种方法有助于确定引起疾病的遗传变化.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 人类遗传学 人类遗传学
背景情况:
- 全基因组关联研究 (GWAS) 强调了非编码变异在疾病中的作用.
- 优先考虑因果非编码变体和理解进化压力仍然具有挑战性.
研究的目的:
- 预测数以百万计的非编码变体在不同细胞环境中的功能影响.
- 为了确定人类特征和疾病的候选因果变异.
- 开发一个模型来优先考虑神经发育障碍中的de novo突变.
主要方法:
- 深度学习模型训练了来自132种人类大脑和心脏细胞类型的单细胞ATAC-seq数据.
- 产生近20亿个特定背景变异效应预测.
- 开发和应用FLARE,一种功能性基因组约束模型,以优先考虑新的突变.
主要成果:
- 常见和罕见的非编码变体的独特的特定环境影响.
- 确定了针对胎儿大脑神经元变异的选择性压力.
- 弗莱尔成功地优先考虑了自闭症家族中的新发病性突变,优于现有方法,并确定了接近CNTNAP2的变异.
结论:
- 整合单细胞基因组学,种群遗传学和深度学习可以推进变异效应预测.
- 罕见的变异在对神经发育障碍的遗传贡献中起着重要的作用.
- 这一框架有助于理解疾病机制和对非编码基因组的进化影响.
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