在使用高维甲基化介质的纵向研究中进行调解分析
Yidan Cui1, Qingmin Lin2, Xin Yuan1
1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd, 200240 Shanghai, China.
Briefings in bioinformatics
|October 15, 2024
概括
我们开发了一种用于高维纵向介导分析的新方法,提高了准确性并减少了错误. 这种方法确定了新的DNA甲基化位点,将父亲的BMI与后代的生长联系起来.
科学领域:
- 生物统计学 生物统计学
- 遗传学 是一个遗传学.
- 流行病学 流行病学
背景情况:
- 调解分析对于理解暴露-结果途径至关重要.
- 现有的方法难以处理高维的纵向数据.
- 在复杂的生物系统中识别介质仍然具有挑战性.
研究的目的:
- 开发和验证一种用于纵向数据的新型高维介导分析方法.
- 为了使可靠的变量选择和间接影响评估.
- 在父亲的BMI与后代的BMI关系中识别新的调解因素.
主要方法:
- 提出了一种结合线性混合效应模型和通用估计方程的新方法.
- 利用确定的独立性选来缩小候选调解员的规模.
- 使用Sobel测试与Bonferroni校正进行间接效应假设测试.
主要成果:
- 与线性方法相比,拟议的方法表现出优异的性能,F1得分更高.
- 实现了更准确的参数估计和明显较低的错误发现率.
- 确定了两个新的CpG位点,调解了父亲的BMI对后代BMI的影响,在一个大型队列中.
结论:
- 这种新方法有效地解决了纵向研究中的高维介导分析.
- 这种方法增强了生物途径和潜在治疗点的识别.
- 这些发现为BMI的代际影响提供了新的见解.
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