Biometris, Wageningen University and Research, 6708 PB Wageningen, Netherlands
Biometris, Wageningen University and Research, 6708 PB Wageningen, Netherlands.
Genetics. 2020 Apr;214(4):781-807. doi: 10.1534/genetics.119.302949. Epub 2020 Feb 3.
Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, , through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the "missing heritability" case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.
一个表型性状的遗传方差可以来自直接遗传效应，也可以来自间接遗传效应，通过遗传效应对其他性状的影响，从而影响感兴趣的性状。这种区分通常非常重要，例如，当试图提高作物产量并同时控制株高时。正如 Sewall Wright 所建议的，评估直接和间接效应的贡献需要了解（1）每个性状是否存在直接遗传效应，以及（2）性状之间的功能关系。由于对这些关系进行实验验证通常是不可行的，因此越来越多地使用因果推理方法来重建它们。然而，大多数当前的方法要求所有的遗传方差都由少数具有固定效应的数量性状位点 (QTL) 来解释。只有少数作者考虑了“缺失遗传力”的情况，即许多不可检测的 QTL 的贡献用随机效应来建模。通常，这些被视为干扰项，需要通过从多性状混合模型 (MTM) 中取残差来消除。但是拟合这样的 MTM 具有挑战性，并且不可能推断出直接遗传效应的存在。在这里，我们提出了一种替代策略，其中遗传效应在图中被正式包含。这具有重要的优势：（1）遗传效应可以直接纳入因果推理中，通过我们的 PCgen 算法来实现，该算法可以分析更多的性状；（2）我们可以测试直接遗传效应的存在，并改善性状之间的边缘方向。最后，我们表明，如果使用个体植物或地块数据而不是基因型均值，重建会更加准确。我们已经在 R 包 pcgen 中实现了 PCgen 算法。
