缓解GxE PRS中的1型错误膨胀和功率损失:多基因风险评分模型中的基因型环境相互作用
Dovini Jayasinghe1,2,3, Md Moksedul Momin1,2,3,4, Kerri Beckmann2
1Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia.
Genetic epidemiology
|February 2, 2024
概括
新的基因型与环境相互作用多基因风险评分 (GxE PRS) 模型提高了基因风险预测的准确性和控制错误. 这些模型通过考虑环境因素来提高预测复杂特征和疾病的可靠性.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 统计遗传学 统计遗传学
- 计算生物学 计算生物学
背景情况:
- 多基因风险评分 (PRS) 模型使用遗传资料预测复杂的特征和疾病.
- 现有的基因型与环境相互作用 (GxE) PRS模型经常遭受不适当的使用,导致膨胀的1型错误率和受损的结果.
- 强大的GxE建模对于准确的遗传风险预测和理解基因环境相互作用至关重要.
研究的目的:
- 开发和验证新的GxE PRS模型,这些模型将添加物和相互作用遗传效应共同纳入,并为非遗传共变量提供二次术语.
- 提高GxE PRS模型对模型错误规范的稳定性,并提高它们在控制1型错误率和统计能力方面的性能.
- 为实现这些先进的GxE PRS模型提供一个实用的R软件包 (GxEprs).
主要方法:
- 提出了新的GxE PRS模型,包括添加和相互作用的遗传效应,以及非遗传共变量的二次术语.
- 进行了广泛的模拟,以评估模型性能与现有方法相比,在1型错误控制和统计能力方面进行了广泛的模拟.
- 将拟议的模型应用于量化和二进制特征的现实数据,以确定显著的GxE效应.
主要成果:
- 与现有模型相比,提出的GxE PRS模型在控制1型错误率和增强统计能力方面表现出卓越的性能.
- 在真实数据分析中发现了显著的GxE效应,突出显示了新模型的实际实用性.
- 对于定量特征,通过酒精摄入频率发现了身体质量指数的GxE调制.
- 对于二进制特征,通过腰部和部的比率确定了高血压风险的GxE调制.
结论:
- 开发的GxE PRS模型通过准确计算基因与环境相互作用,为基因风险预测提供了更强大的方法.
- 这些模型有效控制1型错误率,防止虚假的GxE信号,提高遗传发现的可靠性.
- GxEprs R包促进了这些先进模型的实施,支持未来的个性化医学和遗传风险评估研究.
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