加州大学徐士忠团队取得一项新突破。他们的最新研究提出了偏离正态分布性状的全基因组关联研究与QTL定位。2026年3月23日，国际知名学术期刊《国家科学评论》发表了这一成果。

在广义线性混合模型（GLMM）框架下，课题组开发了统计模型和软件包来绘制这些非正态性状的QTL并进行关联研究。研究小组开发了一种伪响应（PSR）方法，通过生成一个伪响应变量来估计多基因方差，该伪响应变量被视为传统的数量性状。然后，研究人员扫描了基因组中与线性混合模型中PSR变量相关的标记。该方法被称为伪响应广义线性混合模型（PSR-GLMM）。以水稻紫色性状（二元性状）和一组模拟非正常性状为例进行了说明。

然后，研究组将该方法应用于他们的数据集：来自拟南芥群体的二元性状，来自猪群体的二项式性状，来自同一猪群体的泊松性状和来自狗群体的有序性状。在R语言中开发了一个软件包，用于二项、二项、泊松和有序性状（PSR-GLMM/R）的GWAS和QTL定位，包括正态分布性状作为特例。R包还可以用于执行广义线性混合模型分析，用于QTL映射和GWAS之外的一般目的。

据介绍，数量性状是全基因组关联研究（GWAS）和数量性状定位（QTL）的目标。在调整了系统效应后，假设这些性状呈正态分布，从而可以利用典型的线性模型（LM）和线性混合模型（LMM）来检测与QTL相关的标记。然而，农作物、动物和人类的许多性状并不遵循假定的正态分布，其中许多性状甚至不是连续分布的。

附：英文原文

Title: Genome-wide Association Studies and QTL Mapping for Traits Deviating from Normal Distribution

Author: Tang, You, Li, Mingliang, Liu, Defu, Jiang, Liping, Wang, Jiabo, Yu, Helong, Xu, Shizhong

Issue&Volume: 2026-03-23

Abstract: Quantitative traits are the targets for genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping. After adjusting for systematic effects, these traits are assumed to be normally distributed so that typical linear models (LM) and linear mixed models (LMM) can be used to detect markers associated with QTLs. Many traits in crops, animals and humans, however, do not follow the assumed normal distribution and many of them are not even continuously distributed. We developed statistical models and software packages to map QTLs and perform association studies for such non-normal traits under the generalized linear mixed model (GLMM) framework. We developed a pseudo response (PSR) method to estimate the polygenic variance by generating a pseudo response variable that is treated as a conventional quantitative trait. We then scanned the genome for markers associated with the PSR variable in the usual linear mixed model. The new method is called the pseudo response generalized linear mixed model (PSR-GLMM). We illustrated the method with the purple color trait of rice (binary trait) and a set of simulated non-normal traits. We then applied the method to four datasets: a binary trait from an Arabidopsis population, a binomial trait from a pig population, a Poisson trait from the same pig population and an ordinal trait from a dog population. A software package has been developed in R to perform GWAS and QTL mapping for binary, binomial, Poisson and ordinal traits (PSR-GLMM/R), including normally distributed traits as a special case. The R package can also be applied to perform generalized linear mixed model analysis for a general purpose beyond QTL mapping and GWAS.

DOI: 10.1093/nsr/nwag184

Source: https://academic.oup.com/nsr/advance-article/doi/10.1093/nsr/nwag184/8537784searchresult=1