Data_Sheet_6_Spatially Dense 3D Facial Heritability and Modules of Co-heritability in a Father-Offspring Design.PDF

Introduction: The human face is a complex trait displaying a strong genetic component as illustrated by various studies on facial heritability. Most of these start from sparse descriptions of facial shape using a limited set of landmarks. Subsequently, facial features are preselected as univariate measurements or principal components and the heritability is estimated for each of these features separately. However, none of these studies investigated multivariate facial features, nor the co-heritability between different facial features. Here we report a spatially dense multivariate analysis of facial heritability and co-heritability starting from data from fathers and their children available within ALSPAC. Additionally, we provide an elaborate overview of related craniofacial heritability studies.
Methods: In total, 3D facial images of 762 father-offspring pairs were retained after quality control. An anthropometric mask was applied to these images to establish spatially dense quasi-landmark configurations. Partial least squares regression was performed and the (co-)heritability for all quasi-landmarks (∼7160) was computed as twice the regression coefficient. Subsequently, these were used as input to a hierarchical facial segmentation, resulting in the definition of facial modules that are internally integrated through the biological mechanisms of inheritance. Finally, multivariate heritability estimates were obtained for each of the resulting modules.
Results: Nearly all modular estimates reached statistical significance under 1,000,000 permutations and after multiple testing correction (p ≤ 1.3889 × 10-3), displaying low to high heritability scores. Particular facial areas showing the greatest heritability were similar for both sons and daughters. However, higher estimates were obtained in the former. These areas included the global face, upper facial part (encompassing the nasion, zygomas and forehead) and nose, with values reaching 82% in boys and 72% in girls. The lower parts of the face only showed low to moderate levels of heritability.
Conclusion: In this work, we refrain from reducing facial variation to a series of individual measurements and analyze the heritability and co-heritability from spatially dense landmark configurations at multiple levels of organization. Finally, a multivariate estimation of heritability for global-to-local facial segments is reported. Knowledge of the genetic determination of facial shape is useful in the identification of genetic variants that underlie normal-range facial variation.

引言：人类面部是一类复杂性状，具有较强的遗传基础，诸多面部遗传力（facial heritability）相关研究均证实了这一点。此类研究大多基于有限数量的标志点（landmarks）对面部形态进行稀疏描述，随后将面部特征预选为单变量测量指标或主成分，并针对每一项特征单独估算遗传力。然而，现有研究均未探讨多变量面部特征，亦未分析不同面部特征间的共遗传力（co-heritability）。本研究基于雅芳纵向父母与儿童研究（ALSPAC）队列中的父亲-子代数据，开展了空间高密度的面部遗传力与共遗传力多变量分析；此外，本研究还系统梳理了相关颅面遗传力研究进展。

研究方法：经质量控制后，最终纳入762对父亲-子代配对样本的三维面部影像数据。研究人员对这些影像施加人体测量掩码（anthropometric mask），以构建空间高密度的类标志点（quasi-landmark）配置方案。随后开展偏最小二乘回归分析，将回归系数的两倍作为所有类标志点（约7160个）的（共）遗传力估算值。将上述估算结果作为层级化面部分割的输入数据，最终得到通过遗传生物学机制实现内部整合的面部模块定义；最后针对每个生成的模块，获取其多变量遗传力估算结果。

研究结果：经100万次置换检验与多重检验校正（p ≤ 1.3889 × 10^-3）后，几乎所有模块的遗传力估算值均达到统计学显著性，其遗传力得分呈现低至高不等的分布。遗传力最高的特定面部区域在子代性别间具有相似性，但男性子代的估算值更高。此类区域包括全脸、面部上半区（涵盖鼻根点、颧骨与前额）以及鼻部，男孩的对应遗传力值可达82%，女孩则为72%；面部下半区的遗传力仅处于低至中等水平。

研究结论：本研究未将面部变异简化为一系列单一测量指标，而是基于空间高密度的标志点配置方案，从多组织层级层面分析面部的遗传力与共遗传力；最终报道了面向全局至局部面部节段的多变量遗传力估算结果。解析面部形态的遗传决定机制，有助于识别调控正常范围面部变异的遗传变异位点。