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PLoS Genet. 2014 Mar 20;10(3):e1004224. doi: 10.1371/journal.pgen.1004224. eCollection 2014 Mar.

Modeling 3D facial shape from DNA.

Author information

1
Medical Image Computing, ESAT/PSI, Department of Electrical Engineering, KU Leuven, Medical Imaging Research Center, KU Leuven & UZ Leuven, iMinds-KU Leuven Future Health Department, Leuven, Belgium.
2
Department of Anthropology, Penn State University, University Park, Pennsylvania, United States of America.
3
Smurfit Institute of Genetics, Dublin, Ireland.
4
Department of Genetics, Stanford University, Palo Alto, California, United States of America.
5
Department of Genetics, Stanford University, Palo Alto, California, United States of America; HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, United States of America.
6
HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, United States of America.
7
CIBIO: Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Porto, Portugal; Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal.
8
Department of Genetics, Stanford University, Palo Alto, California, United States of America; IPATIMUP: Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.
9
Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasilia, Brasil.
10
School of Paediatrics and Child Health, University of Western Australia, Perth, Australia; Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Australia; Genetic Services of Western Australia, King Edward Memorial Hospital, Perth, Australia.
11
Center for the Integration of Genetic Healthcare Technologies, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
12
Department of Anthropology, University of Connecticut, Storrs, Connecticut, United States of America.

Abstract

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. The facial effects of these variables are summarized as response-based imputed predictor (RIP) variables, which are validated using self-reported sex, genomic ancestry, and observer-based facial ratings (femininity and proportional ancestry) and judgments (sex and population group). By jointly modeling sex, genomic ancestry, and genotype, the independent effects of particular alleles on facial features can be uncovered. Results on a set of 20 genes showing significant effects on facial features provide support for this approach as a novel means to identify genes affecting normal-range facial features and for approximating the appearance of a face from genetic markers.

PMID:
24651127
PMCID:
PMC3961191
DOI:
10.1371/journal.pgen.1004224
[Indexed for MEDLINE]
Free PMC Article

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