包容性偏差会影响常见变体的发现和复制,在与卫生系统相关的生物银行中
Aditya Pimplaskar1, Junqiong Qiu2, Sandra Lapinska3
1Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA; Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, UCLA, Los Angeles, CA, USA.
American journal of human genetics
|March 11, 2026
概括
连接到电子健康记录 (EHR) 的生物库可以改进精准医学. 然而,选择同意导致参与偏见,影响遗传分析. 调整偏差可以提高对遗传关联的发现.
科学领域:
- 基因组学就是基因组学.
- 这是生物银行.
- 精准医学是一门精准的医学.
背景情况:
- 连接到电子健康记录 (EHR) 的生物库对于精准医学研究至关重要.
- 大多数生物银行使用的选择性同意模型可能会引入参与和招聘偏见.
- 这些偏见对遗传关联研究的影响尚不清楚.
研究的目的:
- 调查与EHR相关的生物库中偏见的来源.
- 评估参与偏差对遗传分析的影响.
- 评估减轻生物银行研究偏差的方法.
主要方法:
- 作为一个案例研究,利用了UCLA ATLAS社区卫生倡议.
- 使用EHR数据分析了与生物库参与相关的因素.
- 应用反向概率权重来调整入学概率.
- 对全基因组关联研究 (GWAS) 和多基因得分全现象关联研究 (PGS-PheWAS) 的评估影响.
主要成果:
- 许多因素,包括医疗保健利用率和社会人口统计数据,显著影响生物银行参与.
- 电子健康数据有效地将生物库参与者与一般医疗保健人口区分开来 (AUROC=0.85,AUPRC=0.82).
- 使用反向概率加权的偏差调整使已知GWAS变体的复制增加了54%并影响了PGS-PheWAS结果.
结论:
- 参与和招聘偏见可能会显著影响与EHR相关的生物库中的遗传分析.
- 医疗保健系统中的临时分析对于识别和潜在地减轻干扰因素至关重要.
- 解决偏见对于稳健的遗传发现和精准医学中的风险评估至关重要.
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