混合建模方法用于表征长度表型中的遗传效应
Pei Zhang1, Paul S Albert1, Hyokyoung G Hong1
1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute.
The annals of applied statistics
|September 2, 2025
概括
这项研究引入了一种新的方法来估计随着时间的推移对前列腺特异性抗原 (PSA) 水平的遗传影响. 了解这些遗传影响可以提高个人前列腺癌查的准确性.
科学领域:
- 生物统计学
- 遗传学
- 癌症流行病学
背景情况:
- 在估计个体遗传效应时,纵向表型的使用不足.
- 目前的方法通常集中在单个时间点上, 忽略了轨迹变化.
- 了解基因对纵向生物标志物的影响对于个性化医学至关重要.
研究的目的:
- 开发和应用混合建模方法来估计基因影响的基线和纵向轨迹的斜率.
- 使用这种新的方法,在没有前列腺癌的个体中描述前列腺特异性抗原 (PSA) 轨迹.
- 评估遗传因素在PSA变异性中的作用及其对前列腺癌查的影响.
主要方法:
- 一种结合遗传和个人特异性随机效应的混合建模方法.
- 使用平均信息限制最大概率 (AI-ReML) 算法进行差异成分估计.
- 分析前列腺,肺,结肠直肠和卵巢癌查试验参与者的纵向PSA数据.
主要成果:
- 在最初的PSA水平和随着时间的推移而变化的速度方面,发现了显著的遗传贡献.
- 该方法成功识别了具有高基因倾向的个体,其PSA基线和轨迹升高.
- 在没有前列腺癌的个体中,遗传因素会影响PSA的变化.
结论:
- 将遗传因素纳入纵向PSA监测可以提高前列腺癌检测的准确性.
- 基因洞察力可以帮助识别因PSA轨迹导致错误阳性查的风险.
- 这种方法提供了对癌症生物标志物遗传影响的更细致的理解.
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