使用染色体尺度长度变化改善了乳腺癌风险预测
Yasaman Fatapour1, James P Brody2
1Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, 92697, Irvine, CA, USA.
Human genomics
|June 11, 2025
概括
使用染色体尺度长度变化的新遗传测试显示,有望识别患乳腺癌高风险的女性,其表现优于传统的基于SNP的多基因风险评分.
科学领域:
- 基因组学就是基因组学.
- 医学遗传学 医学遗传学
- 癌症研究 癌症研究
背景情况:
- 早期发现乳腺癌可以提高存活率.
- 使用SNP的当前多基因风险评分 (PRS) 具有局限性,特别是在非欧洲祖先种群中.
- 染色体尺度长度变化 (CLV) 为PRS的基因组表征提供了一种新的方法.
研究的目的:
- 开发和评估使用CLV的乳腺癌遗传风险评分.
- 评估基于CLV的PRS在NIH"我们所有人"数据集中的不同种族群体的表现.
- 为了比较基于CLV的PRS性能,当训练在不同的人口子组.
主要方法:
- 利用NIH我们所有人的数据集,包括4,533名患有乳腺癌的妇女和44,518名对照.
- 在22个自体体内计算了88个CLV参数 (平均log R比) 对每个个体.
- 采用机器学习算法构建一个预测模型,根据CLV参数区分乳腺癌病例与对照病例.
主要成果:
- 基于CLV的最佳PRS在所有美国人群中实现了0.70的曲线下的面积 (AUC).
- 风险分数最高的五分之一的人患乳腺癌的可能性是最低五分之一的人的九倍.
- 基于CLV的PRS显示了相对于现有的基于SNP的PRS的实质性改进.
结论:
- 基于CLV的基因风险评分比基于SNP的乳腺癌多基因风险评分有显著的进步.
- 在白人女性身上训练的模特在白人女性身上进行测试时,通常表现更好于在黑人女性身上训练的模特.
- 在对黑人妇女进行测试时,在接受白人或黑人妇女训练的模型之间没有观察到显著的性能差异.
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