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深度学习模型与罕见疾病队列中的临床级变异致病性分类之间的不一致

Sek Won Kong^1,2, In-Hee Lee¹, Lauren V Collen^2,3

¹Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215.

medRxiv : the preprint server for health sciences

|June 3, 2024

概括

像AlphaMissense这样的深度学习模型在罕见疾病中难以准确识别致病性误解变体,特别是那些本质上有障碍的区域. 这影响了临床遗传学应用和变异解释.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

基因检测对于诊断罕见的门德尔病至关重要.
解释误解变异仍然是临床遗传学的重大挑战.
深度学习显示出希望,但在变异性病原性预测方面面临局限性.

研究的目的:

评估深度学习模型AlphaMissense (AM) 在罕见疾病队列中识别致病误解变体的性能.
评估AM的基因基本性得分的可靠性,特别是对于具有内在失序区域 (IDR) 的基因.

主要方法:

研究了AlphaMissense在一组罕见疾病相关变异的表现.
分析了AlphaMissense在预测误解变异的病原性方面的准确性,重点关注IDR中的变异.
评估了IDR对基因水平基本性预测的影响.

主要成果:

在罕见疾病队列中,AlphaMissense在准确识别致病性误解变体方面表现出局限性.
该模型特别难以评估位于内在无序区域 (IDR) 的变异.
这导致含有IDR的基因的基因水平基本性得分不可靠.

结论:

在涉及罕见疾病的临床遗传学应用中,AlphaMissense的性能不足于最佳,特别是在IDR的变异方面.
目前的深度学习模型需要进一步改进,以准确解释不确定的意义上的误解变体 (VUS).
这些挑战强调了需要改进的计算工具来解释罕见疾病诊断中的遗传变异.

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