Modeling 3D Facial Shape from DNA

P. Claes, D.K. Liberton, K. Daniels, K.M. Rosana, E.E. Quillen, L.N. Pearson, B.P. Mcevoy, M. Bauchet, A.A. Zaidi, W. Yao, H. Tang, G.S. Barsh, D.M. Absher, D.A. Puts, J.L. Rocha, S.S. Beleza, R.W. Pereira, Gareth Baynam, P.L. Suetens, D. VandermeulenJ.K. Wagner, J.S. Boster, M.D. Shriver

    Research output: Contribution to journalArticlepeer-review

    148 Citations (Scopus)


    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. © 2014 Claes et al.
    Original languageEnglish
    JournalPLoS Genetics
    Issue number3
    Publication statusPublished - 2014


    Dive into the research topics of 'Modeling 3D Facial Shape from DNA'. Together they form a unique fingerprint.

    Cite this