Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape.

Jose D Aponte,Fernando Andrade,Marta Vidal-García,Charles C Roseman,Daniela M Roth,Steven A Murray,James Cheverud,Wei Liu,Benedikt Hallgrímsson,Ralph S Marcucio,David C Katz,Daniel Graf

doi:10.7554/elife.68623

Abstract

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.

Highlights

71 Variation in human craniofacial shape is moderately to highly heritable (~30-70% (Cole et al., 2017; Tsagkrasoulis et al, 2017)), and resemblances among close relatives as well as twins underscore the strong relationship between shared genetics and shared phenotype (Johannsdottir et al, 2005; Nakata, 1985)
For each of the example process multivariate genotype-phenotype (MGP) analyses shown, we chose the regularization strength that best represented the tradeoff between minimizing model error and maximizing interpretability of marker effects and the similarity of phenotypic effects with mouse mutant models
We used the process MGP approach to model the joint effects of genomic markers on multivariate craniofacial shape

Summary

Introduction

71 Variation in human craniofacial shape is moderately to highly heritable (~30-70% (Cole et al., 2017; Tsagkrasoulis et al, 2017)), and resemblances among close relatives as well as twins underscore the strong relationship between shared genetics and shared phenotype (Johannsdottir et al, 2005; Nakata, 1985). Despite many studies in humans and in mice (Claes et al, 2018; Cole et al, 2016; Shaffer et al, 2016), we know very little about the genetic basis for variation in craniofacial shape. This is likely due to genetic complexity (Katz et al, 2019; Richtsmeier and Flaherty, 2013; Visscher, 2008; Wood et al, 2014; Wray et al, 2013). These issues likely arise because genetic influences act through multiple layers of interacting developmental processes to influence phenotypic traits, resulting in complex patterns of epistasis and variance heterogeneity(Hallgrimsson et al, 2018, 2014; Kawauchi et al, 2009; Wagner and Zhang, 2011)

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