Data from: Generalized linear mixed models for mapping multiple quantitative trait loci

Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.诸多生物学性状在表型上呈离散分布，而在遗传层面却呈连续分布，这是因为它们受多个基因与环境变异共同调控。由于遗传背景具备数量性状特性，这类多基因被称为数量性状基因座（Quantitative Trait Loci, QTL）。当将QTL效应视作随机效应时，即便QTL的数量远超样本量，仍可通过单一广义线性混合模型（Generalized Linear Mixed Model, GLMM）对其进行估计。但原始形式的GLMM无法用于存在基因型缺失的离散性状QTL定位分析。本研究探讨了两种替代的缺失基因型处理方法：期望法（Expectation Method）与过度离散法（Overdispersion Method）。模拟研究结果表明，在GLMM框架下，这两种方法可有效应用于多QTL定位（Multiple QTL Mapping, MQM）分析。在估计QTL效应的均方误差指标上，过度离散法略优于期望法。将上述两种GLMM方法应用于小麦雌性育性性状的MQM分析，最终检测到多个调控结实小穗数变异的QTL。