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，却在遗传层面呈现连续分布，这是因为它们受多基因与环境变异共同调控。由于遗传背景具备量化属性，这类调控性状的多基因被称为数量性状位点（quantitative trait loci, QTL）。当将QTL效应视作随机效应时，即便QTL的数量多于样本量，也可通过单一广义线性混合模型（generalized linear mixed model, GLMM）对其进行估计。若存在基因型缺失的情况，原始形式的广义线性混合模型无法直接应用于离散性状的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）与过度分散法（overdispersion method）。模拟研究结果表明，在广义线性混合模型框架下，这两种方法均可高效应用于多QTL定位（multiple QTL mapping, MQM）分析。在估计的QTL效应的均方误差更小这一方面，过度分散法相较于期望法展现出小幅优势。我们将这两种广义线性混合模型方法应用于小麦雌性育性性状的多QTL定位分析，最终检测到多个调控可育小穗数变异的QTL。