药物基因组学中的多基因建模和机器学习方法:对全基因组关联研究数据下游分析的重要性
1Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
全基因组关联研究 (GWAS) 揭示了与药物不良反应的遗传联系. 后GWAS方法,如分析多基因特征和非编码变体,为药物基因组学 (PGx) 和个性化医学提供了关键的见解.
科学领域:
- 药物基因组学 (PGx) 是一个学科.
- 遗传学 遗传学 是一个
- 药物开发 药物开发
背景情况:
- 全基因组关联研究 (GWAS) 确定与药物不良影响相关的遗传变异.
- 在药物基因组学 (PGx) 中解释这些GWAS发现的生物学意义是具有挑战性的.
- 了解对药物诱导性肝损伤 (DILI) 的遗传倾向至关重要.
研究的目的:
- 审查有希望的GWAS后方法,以推进药物基因组学 (PGx) 研究.
- 突出解释PGx特征的多基因架构的方法,特别是DILI.
- 讨论阐明非编码变体在药物反应中的功能作用的策略.
主要方法:
- 使用人类初级肝细胞和肝脏器官的实验建模来研究DILI易感性.
- 表达量的特征位置 (eQTL) 分析和大规模并行报告员测试 (MPRA).
- 使用机器学习 (ML) 在晶体管调节中进行变异解释的基变异发生.
主要成果:
- 实验模型证实了DILI易感性的多基因性质.
- 在基因高风险个体中确定了对DILI的生物脆弱性.
- 拟议的ML驱动的基变异作为解释非编码变异角色的方法.
结论:
- 后GWAS方法对于获得对药物基因组学 (PGx) 的关键见解至关重要.
- 这些方法可以帮助揭开药物反应和毒性的遗传基础.
- 在PGx解释方面的进步可以加速药物开发,并使个性化治疗成为可能.
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