利用基因型和表型数据进行人口规模变异分类,使用大型语言模型和贝叶斯推理
Toby R Manders1, Christopher A Tan2, Yuya Kobayashi2
1Labcorp Genetics Inc, 1400 16th Street, San Francisco, CA, 94103, USA. toby.manders@labcorp.com.
Human genetics
|April 23, 2025
概括
一种新的机器学习方法有效地利用患者数据来改善遗传变异分类,在遗传性疾病测试中显著减少不确定的意义 (VUS) 的变异,并帮助临床决策.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 不确定意义的变异 (VUS) 在遗传性疾病的遗传检测中构成挑战.
- 由于缺乏可扩展的策略,临床数据的不足利用阻碍了VUS的减少.
研究的目的:
- 通过基因型和表型数据评估机器学习方法,以改善变异分类和减少VUS.
- 确定机器学习是否可以利用未充分利用的临床数据来更准确地解释变异.
主要方法:
- 开发了一种多步骤的机器学习模型,使用来自测试要求表格的患者数据来生成"患者得分"和"变异得分"用于致病性推断.
- 该研究包括350万名患者,模型评估了歧视,分类性能和与其他致病性措施的一致性.
- 临床变异模型 (CVM) 被整合到分类框架中,对高可信度预测进行专家审查.
主要成果:
- 在1334个开发的临床变异模型 (CVM) 中,595个显示出高性能 (AUROC患者≥0.8和AUROC变异≥0.8).
- 高可信度CVM预测在200,174名患者中提供了5362个VUS的证据,解决了研究基因中23.4%的VUS观察.
- 在17个频繁测试的基因中,CVM重新分类了超过1000个独特的VUS,每种疾病的VUS报告率降低了9-49%.
结论:
- 一种可扩展的机器学习方法有效地使用未充分利用的临床数据来改善遗传变异分类.
- 这种方法显著降低了不确定的意义 (VUS) 的变异率,提高了遗传性疾病遗传检测的实用性.
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