Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.
School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.
Elife. 2021 Nov 15;10:e68623. doi: 10.7554/eLife.68623.
Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait - craniofacial shape - in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.
在方程的两侧，基因与形态的真实映射本质上都是多变量的。从信号通路、基因调控网络和形状与大小发育过程中基因作用的交织中，很明显可以看出协调基因对形态表型的影响是重要的。然而，目前的方法往往侧重于识别和定位单个基因的影响，很少利用高维表型的信息含量。在这里，我们在一个由来自多样性杂交（DO）实验品系的 1145 只老鼠组成的样本中，明确地对一组具有生物学一致性的基因对多变量特征（颅面形状）的共同作用进行建模。我们使用生物过程基因本体论（GO）注释来选择骨骼和面部发育基因集，并求解与基因集标记变化最大协变的形状变化轴。我们使用以过程为中心的多变量基因型-表型（过程 MGP）方法来确定与相关过程相关的基因对颅面变异的总体贡献，以及不同过程的变异如何对应于形状变异的多变量轴。此外，我们比较了突变在表型空间中的作用方向与与它们被认为作用的更广泛途径相关的主要形状变化轴。最后，我们利用突变和通路水平效应之间的关系来预测特定突变体在颅面形状以外的表型效应。我们还引入了一个在线应用程序，为用户提供在 DO 中自定义以过程为中心的颅面形状分析的方法。以过程为中心的方法通常适用于任何连续变化的表型，因此对复杂性状遗传学具有广泛的影响。
