Detecting Genetic Association of Common Human Facial Morphological Variation Using High Density 3D Image Registration

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.

人类面部形态是诸多复杂性状的组合体。目前学界对常见面部形态变异的遗传基础尚知之甚少。现有关联研究大多以简单的标志点间距作为面部复杂形态表型的替代指标，但该方法会导致统计效力下降，且难以明确推断形态变化。本研究采用一种新型图像配准方法，可自动识别显著标志点，并利用高密度像素点对齐样本面部。基于该高密度配准方案，本研究采用三种不同的表型数据策略，对常见面部形态变异与10个候选单核苷酸多态性（Single Nucleotide Polymorphism，SNP）之间的关联进行检验，并对比了三者的性能。第一种策略采用传统的标志点间距法；第二种基于15个标志点的几何分析；第三种则依托约30000个三维点的高密度配准开展几何分析。研究发现，两种几何分析方法在形态变化检测上具有高度一致性。其中采用高密度配准的几何分析方法，在形态变化的精细推断与三维面部建模中展现出显著优势。多个候选SNP与不同面部特征存在潜在关联。尤为关键的是，IRF6基因中的rs642961——作为已知的非综合征性唇腭裂风险位点——经验证可有力预测汉族女性的正常唇形变异。本研究进一步证实，高密度面部配准可显著提升常见面部变异中遗传关联的检测与特征解析能力。