基于使用BPC方法的多基因预测估计疾病概率
medRxiv : the preprint server for health sciences
|January 23, 2024
概括
贝叶斯多基因分数 (PGSs) 对二进制疾病特征的校准不佳. 新的贝叶斯多基因分数概率转换 (BPC) 方法改进了使用GWAS数据对绝对疾病概率预测的校准.
科学领域:
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 多基因分数 (PGSs) 使用全基因组关联研究 (GWAS) 数据汇总特征的遗传倾向.
- 现有的贝叶斯式PGS方法为连续特征提供了更好的预测准确性,但在确定的样本中缺乏对二进制失序特征的校准.
- 准确的PGS校准对于估计临床应用的绝对个体疾病概率至关重要.
研究的目的:
- 引入和评估贝叶斯多基因分数概率转换 (BPC) 方法,用于对二进制疾病特征进行PGS校准.
- 为了使个人绝对疾病概率的可靠计算.
主要方法:
- BPC方法使用了GWAS总结统计,贝叶斯式PGS方法 (例如PRScs,SBayesR),个体基因型数据和先前疾病概率.
- 它涉及将PGS转换为负债规模,计算PGS在案例和控制中的差异,并应用贝叶斯定理.
- 该方法很实用,因为它不需要单独的调样本,包括基因型和表型数据.
主要成果:
- BPC方法在九种疾病的广泛模拟和经验数据上展示了精确校准的结果.
- 与最近发表的另一种校准方法相比,性能始终优越.
- 该方法有效地将PGS转换为准确的绝对乱概率.
结论:
- 贝叶斯多基因分数概率转换 (BPC) 方法为校准多基因分数用于二进制疾病预测提供了强大而实用的解决方案.
- 这种方法有助于对绝对个体疾病概率的可靠估计,为临床实施铺平了道路.
- 与现有的疾病遗传风险预测方法相比,BPC提供了更好的准确性和校准.
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