3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies

The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation. Author summary The human face is a highly variable trait composed of distinct features, each influenced by genetic and environmental factors. The strong genetic component is primarily evidenced by the facial similarity between identical twins and the clear facial resemblances within families. Over the past decade, a powerful methodological toolbox of computational and statistical genetics has been developed to study the genetic architecture of complex traits. However, these methods typically require one-dimensional phenotypic measurements (e.g. width of the nose or spacing between the eyes) that fail to accurately describe 3D facial shape. In this paper, we learn from 3D facial data itself, a series of relevant traits that are guided by the facial similarity observed between sibling pairs. Importantly, while preserving the structural convolution of the face, these traits also fit the requirements as input to the well-established statistical tools. In doing so, we have identified many genetic loci that are associated with a wide range of facial features. Some of these regions contained genes related to embryonic facial development, and craniofacial malformations. An improved understanding of the genetic basis of facial shape can have several important applications, for example in developmental biology, medical genetics and forensic sciences.