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通过机器学习扩大生物库药物基因组学,调用结构变异的结构变化

Brett Vanderwerff¹, Amy L Pasternak^2,3, Lars G Fritsche¹

¹Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.

|May 9, 2025

概括

生物库可以通过将遗传数据与临床记录联系起来来增强药物遗传学 (PGx) 研究. 新方法改善了PGx等位基因的识别,包括那些被标准基因型定型遗漏的基因,以预测药物反应和不良反应.

科学领域:

基因组学和生物信息学
药物基因组学药物基因组学
临床研究临床研究

背景情况:

整合遗传和临床数据的生物库对药物基因组学 (PGx) 研究非常有价值.
现有的基于数组的基因型可以识别许多PGx等位基因,但复杂变异仍然存在挑战.

研究的目的:

为密歇根基因组学倡议 (MGI) 生物库中超过8万名参与者创建一个全面的药物基因组学 (PGx) 亚基因和表型呼叫集.
开发和验证一种新的计算方法来识别依赖于结构变异的PGx等位基因,特别是CYP2D6*5删除.

主要方法:

在TOPMed上使用PyPGx软件计算基因型,生成中央PGx调用集.
开发了一种支持向量机器模型,以基因型阵列SNV探针强度进行训练,以检测CYP2D6*5的结构变异.
经过验证的基于阵列的PGx调用与PCR验证的临床数据相比.

主要成果:

在基于数组的PGx调用和PCR验证的等位基因之间实现了>92%的一致性.
支持矢量机在识别CYP2D6*5载体时显示了>99%的准确性.
大约7%的非裔美国人和4%的白人参与者被重新分类为较低活性代谢器表型,这表明药物不良反应增加的可能性.

结论:

从现有的生物库数据中获得的中央PGx呼叫集是可行的,对研究有价值的.
定制的计算方法可以有效地识别具有挑战性的PGx等位基因,包括结构变异,提高生物库的效用.
通过先进的方法来增加标准PGx呼叫集,扩大了药物基因组学研究潜力和临床适用性.

关键词:

一个等位基因,一个等位基因.阵列阵列阵列阵列.生物银行生物银行在cyp2d6d6中使用.基因型定制是基因型定制.学习学习学习学习学习学习机器机器机器机器机器机器药物遗传学药物遗传学药物基因组学药物基因组学一个明星的星星.

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