斯坦福大学Jonathan K. Pritchard小组宣布他们研究出从调控因子到程序再到性状的基因效应因果模型。相关论文发表在2025年12月10日出版的《自然》杂志上。

然而，确定大多数关联的生物学机制是具有挑战性的，而且该团队缺乏基因组尺度的方法来推断从基因到细胞功能到性状的热机制途径。

在这里，课题组研究人员提出了一些方法来弥补这一差距，通过结合从功能丧失负担测试中获得的基因-性状关系的定量估计和从相关细胞类型的Perturb-seq实验中推断的基因调控联系。通过结合这两种形式的数据，课题组人员的目标是建立纵向图，其中基因与性状的定向关联可以通过它们对生物程序的调节作用或对性状的直接作用来解释。作为概念的证明，研究人员构建了一个共同控制三个部分共同调节的血液性状的基因调控层次结构的纵向图。研究组提出，性状相关细胞类型的扰动研究，加上性状的基因水平效应大小，可以弥合遗传关联和生物学机制之间的差距。

据了解，遗传关联研究为确定基因与人类性状和疾病之间的候选遗传联系提供了一种独特的工具。

附：英文原文

Title: Causal modelling of gene effects from regulators to programs to traits

Author: Ota, Mineto, Spence, Jeffrey P., Zeng, Tony, Dann, Emma, Milind, Nikhil, Marson, Alexander, Pritchard, Jonathan K.

Issue&Volume: 2025-12-10

Abstract: Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene–trait relationships from loss-of-function burden tests1 with gene-regulatory connections inferred from Perturb-seq experiments2 in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait3. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

DOI: 10.1038/s41586-025-09866-3

Source: https://www.nature.com/articles/s41586-025-09866-3