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Yun Wei^1,2, Hao Chen^1,2, Xinhui Liu³

¹Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 12550 Erhuan East Road, Shizhong District, Jinan 250000, Shandong, China.

Briefings in bioinformatics

|February 9, 2026

概括

人口异质性挑战因果推理. 基于转移学习的门德尔随机化 (TLMR) 通过在人群之间转移暴露数据来解决这一问题,为体质指数和肺功能提供了可靠的估计.

科学领域:

流行病学流行病学
统计遗传学统计遗传学
因果推理因果推理

背景情况:

种群异质性,即共同变量分布在不同种群之间存在差异,对两样本的门德尔随机化 (MR) 构成重大挑战.
这种异质性可能导致因果效应估计偏差,特别是当共变量同时充当混因子和效应修饰因子时.
现有的MR方法往往难以解释这些复杂的人口差异.

研究的目的:

开发一种新的方法,转移基于学习的门德尔随机化 (TLMR),以解决MR的种群异质性.
通过利用源人群的数据,使目标人群中准确的因果效应估计成为可能.
提供适用于各种结果类型和设置的灵活和强大的MR方法.

主要方法:

TLMR利用可观测效应修饰器将预测的暴露从源向目标人群转移.
该方法采用最小的建模假设,允许灵活的暴露建模.
TLMR支持连续和二进制结果,并在结果模型中包括反向转移的扩展.

主要成果:

模拟表明,TLMR在异构种群中提供了可靠和一致的估计.
TLMR的性能优于八种广泛使用的MR方法,这些方法表现出大量的估计偏差.
在同质群体中或没有效果修改的情况下,TLMR的性能与现有方法相比.
该研究系统地评估了使用TLMR的身体质量指数和肺功能之间的因果关系.

结论:

TLMR有效地解决了两个样本MR中的种群异质性,产生了准确的因果效应估计.
与现有的MR方法相比,该方法提供了更好的准确性和实际实用性.
在不同的人群中,TLMR是用于因果推理的有价值工具,其适用于人体质量指数和肺功能证明了这一点.

关键词:

门德尔的随机化效果修改器效果修改器异质性的异质性转移学习转移学习

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