Genome-wide mapping of global-to-local genetic effects on human facial shape

Abstract

Genome-wide association scans of complex multipartite traits like the human face typically use preselected phenotypic measures. Here we report a data-driven approach to phenotyping facial shape at multiple levels of organization, allowing for an open-ended description of facial variation while preserving statistical power. In a sample of 2,329 persons of European ancestry, we identified 38 loci, 15 of which replicated in an independent European sample (n = 1,719). Four loci were completely new. For the others, additional support (n = 9) or pleiotropic effects (n = 2) were found in the literature, but the results reported here were further refined. All 15 replicated loci highlighted distinctive patterns of global-to-local genetic effects on facial shape and showed enrichment for active chromatin elements in human cranial neural crest cells, suggesting an early developmental origin of the facial variation captured. These results have implications for studies of facial genetics and other complex morphological traits.

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Fig. 1: Hierarchical spectral clustering of facial shape.
Fig. 2: Fifteen replicating loci.
Fig. 3: Facial shape effects.
Fig. 4: Preferential activity in CNCCs.
Fig. 5: Regulatory regions active in CNCCs.

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Acknowledgements

This investigation was supported by KU Leuven, BOF funds GOA, CREA and C1. The collaborators at the University of Pittsburgh were supported by the National Institute for Dental and Craniofacial Research (see URLs) through the following grants: U01-DE020078, U01-DE020057, R01-DE016148, K99-DE02560 and 1-R01-DE027023. Funding for genotyping was provided by the National Human Genome Research Institute (see URLs): X01-HG007821 and X01-HG007485. Funding for initial genomic data cleaning by the University of Washington was provided by contract HHSN268201200008I from the National Institute for Dental and Craniofacial Research (see URLs) awarded to the Center for Inherited Disease Research (CIDR). The collaborators at Penn State University were supported in part by grants from the Center for Human Evolution and Development at Penn State, the Science Foundation of Ireland Walton Fellowship (04.W4/B643), the US National Institute of Justice (see URLs; 2008-DN-BX-K125) and the US Department of Defense (see URLs). The collaborators at the Stanford University School of Medicine were supported by the Howard Hughes Medical Institute, NIH U01 DE024430 and the March of Dimes Foundation 1-FY15-312 (J.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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This work is the result of a four-center collaboration between KU Leuven, the University of Pittsburgh, Penn State University and the Stanford University School of Medicine, led by P.C., S.M.W., M.D.S. and J.W., respectively. All four centers contributed equally to this work. P.C. and D.S. with input from P.S. and D.V. conceptualized and implemented the global-to-local facial segmentations. J.L. provided genomic data support and analysis at the KU Leuven site and together with P.C. ran the GWAS. M.K.L., J.R., E.J.L., J.C.C., E.O., E.F., M.L.M., J.R.S. and S.M.W. organized the PITT cohort and co-analyzed the GWAS results, generated LocusZoom plots and organized the results into 38 loci. J.D.W., A.Z., B.C.M., C.L., L.P., T.G. and M.D.S. organized the PSU cohort, imputed the PSU genetic data and co-analyzed the GWAS results. T.S. and J.W. performed the GREAT analysis and the association with CNCCs. P.C. wrote the manuscript with extensive input from all co-authors.

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Correspondence to Peter Claes or Joanna Wysocka or Mark D. Shriver or Seth M. Weinberg.

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Integrated supplementary information

Supplementary Figure 1 Hierarchical spectral clustering on the PSU cohort

A global-to-local facial segmentation of the PSU cohort obtained using hierarchical spectral clustering. Note that the order of quadrants, or facial segments at each level, is not necessarily the same as for Fig. 1, on the PITT cohort. The reason is the randomness in the clustering that does not preserve such order and hence the use of the normalized mutual information as a measure of overlap.

Supplementary Figure 2 Biological shape variables per facial segment

Left, the number of principal components retained after parallel analysis for each facial segment. Right, the amount of variation explained by the principal components expressed as percentage for each facial segment.

Supplementary Figure 3 GREAT analysis GREAT GO gene ontology analysis results for the 15 top replicated SNPs in

Table 1. Plotted is the binomial test FDR (cyan) and binomial enrichment (magenta) for indicated top associated biological processes, phenotypes and expression pattern categories.

Supplementary Figure 4 Statistical replication result for rs2045323

a, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (8.01 × 10–5), i.e., the Bonferroni-corrected P value for literature replication. Black-encircled facial segments have reached nominal replication (P = 0.05). b, The canonical correlation [0 1]. c, The normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of facial segment 45 going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

Supplementary Figure 5 Statistical discovery result for rs2424390

a, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (1.28 × 10–9), i.e., the Bonferroni-corrected P value for discovery. Black-encircled facial segments have reached nominal genome-wide significance (P ≤ 5 × 10–8). b, The canonical correlation [0 1]. c, The normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of facial segment 11, going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

Supplementary Figure 6 Two loci associated with different aspects of nasal shape

a, 6p21.1 locus with peak SNP rs227833 and candidate gene SUPT3H. b, 19q13.11 locus with peak SNP rs287104 and candidate gene KCTD15. The locus in a is primarily affecting the nasal bridge and ridge, leaving the nose tip unaffected. The locus in b is focused on the nose tip only, which could indicate potentially different underlying soft tissue regulations. Top, –log10 (P value) of the canonical correlation per facial segment ranging from 0 to –log10 (1.28 × 10–9), i.e., the Bonferroni-corrected P value for discovery. Bottom, the normal displacement (displacement in the direction locally normal to the facial surface) in each quasi-landmark of a representative facial segment per locus, going from the major to the minor allele SNP variant. Blue, inward depression; red, outward protrusion.

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Supplementary Figures 1–6, Supplementary Tables 1–7 and Supplementary Notes 1 and 2

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Claes, P., Roosenboom, J., White, J.D. et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat Genet 50, 414–423 (2018). https://doi.org/10.1038/s41588-018-0057-4

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