美国加州大学安德森分校管理学院Alexander Strudwick Young研究团队报道了以家族为基础的全基因组关联研究设计提高了研究的有效性和稳健性。相关论文发表在2025年3月10日出版的《自然—遗传学》杂志上。

在这里，研究人员引入了一个“统一估计器”，包括没有基因型亲属的个体，统一标准和FGWAS，同时提高了DGE估计的能力。研究团队还引入了一个“机器人估计器”，它在结构化和/或混合种群中没有偏倚。在对英国生物银行19种表型的分析中，与兄弟姐妹之间的遗传差异相比，英国白人亚样本的统一估计器和robthemt估计器（没有血统限制）分别将遗传差异的有效样本量增加了46.9%至106.5%和10.3%至21.0%。与其他基于家庭的方法相比，来自统一估计器的多基因预测器显示出更好的样本外预测能力。研究小组在一个有效的线性混合模型中实现了软件包snipar中的方法，该模型考虑了样本相关性和兄弟姐妹共享环境。

据悉，基于家庭的全基因组关联研究（FGWASs）的主题是随机的，家庭内的遗传变异，以消除直接遗传效应（DGEs）估计中的混淆。

附：英文原文

Title: Family-based genome-wide association study designs for increased power and robustness

Author: Guan, Junming, Tan, Tammy, Nehzati, Seyed Moeen, Bennett, Michael, Turley, Patrick, Benjamin, Daniel J., Young, Alexander Strudwick

Issue&Volume: 2025-03-10

Abstract: Family-based genome-wide association studies (FGWASs) use random, within-family genetic variation to remove confounding from estimates of direct genetic effects (DGEs). Here we introduce a ‘unified estimator’ that includes individuals without genotyped relatives, unifying standard and FGWAS while increasing power for DGE estimation. We also introduce a ‘robust estimator’ that is not biased in structured and/or admixed populations. In an analysis of 19 phenotypes in the UK Biobank, the unified estimator in the White British subsample and the robust estimator (applied without ancestry restrictions) increased the effective sample size for DGEs by 46.9% to 106.5% and 10.3% to 21.0%, respectively, compared to using genetic differences between siblings. Polygenic predictors derived from the unified estimator demonstrated superior out-of-sample prediction ability compared to other family-based methods. We implemented the methods in the software package snipar in an efficient linear mixed model that accounts for sample relatedness and sibling shared environment.

DOI: 10.1038/s41588-025-02118-0

Source: https://www.nature.com/articles/s41588-025-02118-0