Detecting Genetic Association of Common Human Facial Morphological Variation Using High Density 3D Image Registration. S Peng, J Tan, S Hu, H Zhou, J Guo, L Jin, K Tang.
Date: December 2013.
Source: PLoS Computational Biology, Public Library of Science.
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.
Article: Detecting Genetic Association of Common Human Facial Morphological Variation Using High Density 3D Image Registration.
Authors: Shouneng Peng, Jingze Tan, Sile Hu, Hang Zhou, Jing Guo, Li Jin, Kun Tang.