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Abstract：
Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.
摘要：
多亲本种群（MPPs）对遗传和育种研究具有吸引力，因为它们将遗传多样性与易于控制的种群结构相结合。在MPP中映射QTL的大多数方法都集中在单个环境中QTL的检测。在多环境试验（MET）中绘制QTL以及检测和建模QTL与环境的相互作用（QEI）很少受到关注。我们提出了用于检测和建模一致与环境相关的QTL的混合模型方法，即，QTL-逐环境相互作用(QEI)。假定QTL效应呈正态分布，其方差表示对环境和家庭的一致性或依赖性。相应设计矩阵的条目是父母和后代之间的血统身份（IBD）概率的函数，并且从后代DNA的父母起源开始。将多基因效应添加到模型中以解释背景遗传变异。我们通过分析几个公共MPP数据集和来自MET的观察结果来说明我们方法的广泛适用性。例子包括Diallel，嵌套关联映射(NAM)，和多亲本高级交叉(MAGIC)种群。我们方法的结果与使用定制方法的先前研究的结果相比具有优势。
