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

Abstract

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.

Three-dimensional human facial morphologies as robust aging markers

“…We applied our previously developed high-density point-wise registration algorithm to align different faces across all 3D facial images [20,21], which consists of three major steps: (1) Landmark recognition: starting with the collected raw 3D face images, the nose tip is first automatically recognized on each face by the sphere pattern fitting approach, and then pose normalization is performed to render all faces to a uniform frontal view. Other facial landmarks are then automatically annotated by a principle component analysis (PCA)-based feature recognition approach.…”

“…Given P = [p 1 |p 2 | …, |p n ] as the output loading matrix and b = [b 1 , b 2 , …, b n ] as a vector of SD of projection scores of n components to be used in different scales between −2 and +2 that controls the shape variation, the computed loading values p i can be treated as decomposition of the whole face most strongly associated with the observations and can be directly added to the coordinates of the vertices of the average face to synthesize new faces [21].…”

“…As our 3D facial data are spatial coordinates, we can use a precise transformation to first align all the facial images together using our previously developed 3D facial image registration tool [20,21]. After alignment, each vertex can be directly used as a scaled landmark to be compared for facial morphological or geometric changes during aging where any changes between different images are already relative position changes.…”

Toward DNA-based facial composites: Preliminary results and validation

“…In order to improve statistical modeling of the relationships between genetic variation and facial morphology, the physical complexity cannot be simplified a priori. Instead it should be modeled systematically and impartially as recently shown in Peng et al [23] and Claes et al [24].…”

A comparative study of craniofacial measurements between Ryukyuan and mainland Japanese females using lateral cephalometric images

“…Only a few genome-wide association studies (GWASs) have addressed human facial morphology; notably, single nucleotide polymorphisms (SNPs) in paired box 3 (PAX3) are significantly associated with the shape of the nasal root in people of European descent (Paternoster et al, 2012;Liu et al, 2012). An association study focusing on 10 candidate SNPs shows that one SNP in interferon regulatory factor 6 (IRF6), which is known as a risk factor for nonsyndromic cleft lips/palates, affects normal variation in lip shape in Han Chinese females (Peng et al, 2013). Additionally, growth hormone receptor (GHR) polymorphisms are reportedly associated with mandibular ramus height (Yamaguchi et al, 2001;Tomoyasu et al, 2009).…”