建立临床GWAS的最佳实践:解决计算和数据质量挑战
Giorgio Casaburi1, Ron McCullough2, Valeria D'Argenio3,4
1Department of Bioinformatics and Innovation Strategy, SOLVD Health, 1600 Faraday Ave., Carlsbad, CA 92008, USA.
International journal of molecular sciences
|July 12, 2025
概括
在全基因组关联研究 (GWAS) 中的基因型归算可以提高变体覆盖率,但可以引入偏见,特别是对于罕见的变体和代表性不足的群体,影响精准医学的准确性和公平性.
科学领域:
- 基因组学就是基因组学.
- 精准医学是一门精准的医学.
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组关联研究 (GWAS) 对于精准医学应用,如药物基因组学和疾病风险预测至关重要.
- 基因型归算是GWAS的一个关键计算方法,推断非类型化遗传变异以增加变异覆盖范围.
研究的目的:
- 审查GWASs中基因型归算错误的挑战和临床影响.
- 探索归算错误对治疗决策和多基因风险评分 (PRS) 的影响.
- 为准确和公平的临床GWAS实施提出最佳实践.
主要方法:
- 检查归算错误的来源及其在祖先种群中的表现差异.
- 对医疗保健公平和道德考虑下游影响的分析.
- 开发基于证据的最佳实践,用于临床GWAS整合.
主要成果:
- 归算引入了偏见,特别是影响罕见变体和代表性不足的人群,可能会损害临床准确性.
- 推算错误可能会对治疗决策和PRS等预测模型产生负面影响.
- 在不同的祖先群体中存在显著的绩效差异.
结论:
- 确保GWAS衍生的见解的准确性和包容性至关重要,因为基因组数据的采用在医疗保健中不断增长.
- 为可靠和公平的个性化医疗提出了一个负责任的临床整合基因数据的框架.
- 最佳实践包括可操作变异的直接基因型定型,跨种群验证,质量指标的透明报告以及使用祖先匹配的参考面板.
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