An averaging model for analysis and interpretation of high-order genetic interactions
An averaging model for analysis and interpretation of high-order genetic interactions
复制标题
用于分析和解释高阶遗传相互作用的平均模型
DOI：
--
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Fumiaki Katagiri
中科院分区：
文献类型：
--
作者：
Fumiaki Katagiri
虽然从生物系统(多基因系统)的数量表型由多基因的功能等位基因和非功能等位基因控制的组合遗传数据收集正在变得普遍，但对此类数据的标准分析方法尚未建立。基于方差分析和交互作用的非功能等位基因效应与功能等位基因效应的常见相加模型有三个问题。首先，尽管这是遗传学的悠久传统，但将非功能等位基因(零突变等位基因)的影响与功能等位基因(野生型等位基因)的影响进行对比建模，不适合于对多基因系统的机械性理解。其次，当表型反应不是线性时，加性模型不能估计两个以上基因之间的交互作用。第三，对加性模型定义的高阶相互作用的解释并不直观。我提出了一个适用于多基因系统机械性理解的平均模型：功能等位基因的作用与非功能等位基因的作用相比，更容易进行机械性解释；即使在表型反应不是线性的情况下，该模型对高阶相互作用的估计也是稳定的；它定义的高阶相互作用是高度直观的。然而，平均模型仍然是一个一般的线性模型，所以使用常用的统计工具进行模型拟合是容易和准确的。
While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by functional and non-functional alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. A common additive model of the non-functional allele effects contrasted against the functional alleles, based on ANOVA with interaction, has three issues. First, although it is a long tradition in genetics, modeling the effect of the non-functional allele (a null mutant allele) contrasted against that of the functional allele (the wild-type allele) is not suitable for mechanistic understanding of multi-gene systems. Second, an additive model fails in estimation of interactions among more than two genes when the phenotypic response is not linear. Third, interpretation of higher-order interactions defined by an additive model is not intuitive. I propose an averaging model, which is suitable for mechanistic understanding of multi-gene systems: the effect of the functional allele is contrasted against the effect of the non-functional allele for easier mechanistic interpretations; it is stable in estimation of higher-order interactions even when the phenotypic response is not linear; and the higher-order interactions it defines are highly intuitive. Yet, the averaging model is still a general linear model, so model fitting is easy and accurate using common statistical tools.