Investigations into the genetic architecture of the human face

Open Access
- Author:
- White, Julie
- Graduate Program:
- Anthropology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 04, 2019
- Committee Members:
- Mark Shriver, Dissertation Advisor/Co-Advisor
Mark Shriver, Committee Chair/Co-Chair
George H Perry, Committee Member
Joan Therese Richtsmeier, Committee Member
Hannah M.C. Schreier, Outside Member
Mary Katherine Shenk, Program Head/Chair - Keywords:
- geometric morphometrics
GWAS
facial genetics
facial asymmetry
3D imaging - Abstract:
- The human face is a highly complex and multipartite structure likely affected by both genetic and environmental factors. Perhaps because of this complexity, even large studies of typical-range facial shape often only identify a few loci associated with facial variation. In this dissertation, I combine large scale genomics and its phenotypic complement, phenomics, to characterize variation in the human face and explore the genomic connections to symmetric and asymmetric variation in facial shape. In Chapter One, I provide a literature review of the methodology used to explore 3D facial structures, what is known about the genetic architecture of facial shape, and the prevailing hypothesis surrounding asymmetry in humans. In Chapter Two, I validate a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. As validation, we used a sample of 3D facial images (n = 41) to test the registration accuracy of MeshMonk, finding a 1.26 mm average error between placements of 19 landmarks manually by human observers and by the MeshMonk toolbox. We also find no variation in landmark position or centroid size significantly attributable to landmarking method used. Though the initial focus of this dissertation was the associations between genomics and facial shape variation, it was critical to explore and understand the relative contribution of potential sources of error to the structures under study. Accordingly, in Chapter Three, we investigated the level of error in 3D facial images attributable to: 1) the MeshMonk registration toolbox, 2) mechanical variation internal to the 3dMDface and Vectra H1 3D camera systems, 3) movement or changes in participant expressions, and 4) systematic imaging differences between the 3dMDface and Vectra H1 systems. This analysis finds an upper bound of error, assuming careful operation, potentially introduced when using either the 3dMDface or the Vectra H1 systems to image human participants and then the MeshMonk registration toolbox to phenotype the images at 0.44 mm and 0.40 mm, respectively. Additionally, we find systematic iv biases in the shape of the eyes, nostrils, and lips when comparing images from the 3dMDface and Vectra H1 image systems. In Chapter Four, we used two meta-analysis GWAS to identify 203 genomic regions associated with typical-range facial variation, 53 of which are novel. These GWAS regions are enriched with genes relevant to craniofacial and limb morphogenesis and enhancer activity in cranial neural crest cells and craniofacial tissues. In addition, SNPs grouped by their contribution to similar facial variation show high within-group correlation of enhancer activity and four SNP pairs display evidence of epistasis, indicating potential coordinated actions of variants within the same cell types or tissues. Finally, in Chapter Five, my analyses show no evidence for an association between fluctuating facial asymmetry and indicators of indirect genetic benefits, as expected by the “good genes” hypothesis. In sum, the research within this dissertation uses high dimensional phenotyping and multivariate statistics to explore an emerging technique with which to describe facial variation, provides new insights for the genotype-phenotype map of a complex trait in terms of individual SNP variation and coordinated genomic actions, and applies large scale data to test one hypothesis regarding associations between the genome and asymmetric variation in facial shape.