The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

中文翻译：

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基于模拟的环境和基因型 x 环境交互作用下 QTL 作图效率的评估。

这项基于模拟的研究的目的是评估基因、环境和基因型 x 环境 (GxE) 相互作用如何影响数量性状基因座 (QTL) 作图效率。模拟软件对 F2 的 300 个重组自交系 (RIL) 进行了 50 个采样，并在 6 个环境中进行了评估。对 RIL 的 977 个单核苷酸多态性 (SNP) 进行了基因分型，并对谷物产量进行了表型分析。平均 SNP 密度为 2 cM。我们定义了 6 个 QTL 和 190 个小基因。性状遗传力范围为30%至80%。我们将单QTL模型和多QTL模型拟合到多个表型上。环境和复杂的GxE相互作用效应导致QTL遗传力和功效之间的相关性较低。单环境和跨环境分析允许声明所有 QTL，平均功效为 28% 至 100%。在跨环境分析中，5 个 QTL 显示平均功效在 46% 至 82% 范围内。两种模型都可以很好地控制假阳性率（平均 6%）和 QTL 的精确定位（平均偏差为 2 cM）。对于加性、环境和 GxE 相互作用效应之和，每种环境中的 QTL 功效与 QTL 基因型之间的范围具有高度正相关性（0.76 至 0.96）。环境和 GxE 相互作用效应的大小和符号的不确定性使得多环境试验中的 QTL 作图变得不可预测。不幸的是，这种不确定性没有解决方案，因为遗传学家无法控制环境和 GxE 相互作用效应的大小和符号。然而，即使在 QTL 遗传力和功效之间的相关性较低的情况下，单环境和跨环境分析也是有效的。