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Shared heritability of human face and brain shape

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Abstract

Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain–face cross-talk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain–face genome-wide association study signals and variants affecting behavioral–cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.

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Fig. 1: Multivariate genome-wide association study of brain shape.
Fig. 2: Loci affecting both brain and face shape.
Fig. 3: Genome-wide sharing of signals with neuropsychiatric disorders and behavioral–cognitive traits.
Fig. 4: Partitioned heritability enrichments based on cell-type-specific regulatory annotations.

Data availability

All the data and detailed information for the UKB, including genetic markers, covariates and MRI images are available to bona fide researchers via the UKB data access process (http://www.ukbiobank.ac.uk/register-apply/).

All the data and detailed information for the ABCD Study, including genetic markers, covariates and MRI images are also available to bona fide researchers through the ABCD data depository (https://nda.nih.gov/abcd/request-access/; controlled access due to highly identifiable facial scans and brain MRIs linked to genotype data).

Relevant data and materials from the facial GWAS study are available online (https://doi.org/10.6084/m9.figshare.c.4667261)124. Full facial GWAS summary statistics are available from the NHGRI-EBI GWAS catalog (study accession GCST90007181). Furthermore, relevant files generated from the face and brain GWAS summary statistics as input to (S-)LDSC regression and Spearman correlations are available on FigShare (Supplementary Table 7). Full brain GWAS summary statistics are available from the GWAS catalog under prepublished/unpublished studies (accessions GCST90012880GCST90013164, one accession number per brain segment). Gene expression data from 3D forebrain organoids (accession GSE132403) as well as CNCCs and derived chondrocytes (accession GSE145327) are available through the Gene Expression Omnibus.

All relevant additional data related to this work are provided in the FigShare repository for this work (https://doi.org/10.6084/m9.figshare.c.5089841.v1). This includes additional figures, input files and updated implementations, listed in Supplementary Table 7.

Code availability

MATLAB implementations of the hierarchical spectral clustering to obtain phenotypic shape segmentations are available from a previous publication (https://doi.org/10.6084/m9.figshare.7649024.v1)7. Updated implementations used in this work are provided in Supplementary Table 7. The statistical analyses in this work were based on functions of the statistical toolbox in MATLAB (Methods). Other materials and software used are available online. No other custom software packages were used.

Change history

  • 16 April 2021

    In the version of the article originally published, the Supplementary Information file was corrupted. The error has been corrected in the HTML version of the article.

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Acknowledgements

J.W. was supported by the Howard Hughes Medical Institute, a Lorry Lokey endowed professorship and a Stinehart Reed award. S.N. was supported by a Helen Hay Whitney Fellowship. The KU Leuven research team and analyses were supported by the National Institutes of Health (NIH; 1-R01-DE027023 and 2-R01-DE027023), The Research Fund KU Leuven (BOF-C1, C14/15/081 and C14/20/081) and The Research Program of the Research Foundation in Flanders (FWO; G078518N). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO and the Flemish Government (department EWI). J.P.S. was supported by an NIH training grant (5T32HG000044-23). J.K.P. was supported by the NIH (HG008140 and HG009431). Pittsburgh personnel, data collection and analyses were supported by the National Institute of Dental and Craniofacial Research (U01-DE020078, R01-DE016148 and R01-DE027023). Funding for genotyping by the National Human Genome Research Institute (X01-HG007821 and X01-HG007485) and funding for initial genomic data cleaning by the University of Washington were provided by contract HHSN268201200008I from the National Institute for Dental and Craniofacial Research awarded to the Center for Inherited Disease Research (https://www.cidr.jhmi.edu/). J.T. was supported by the NIH (5R01-DA033431-07) and the National Science Foundation (1922598). This research has been conducted, in part, using the UKB resource under application no. 43193 (understanding the genetic architecture of human brain shape from MRI using global-to-local shape segmentations), and we are grateful for all the participants in that resource. This manuscript reflects the views of the authors and may not reflect the opinions or views of the UKB funders and investigators. Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the NIH and additional federal partners under award nos. U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093 and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A list of participating sites and study investigators can be found at https://abcdstudy.org/wp-content/uploads/2019/04/Consortium_Members.pdf. ABCD consortium investigators provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

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Conceptualization: P.C., J.W. and S.N.; methodology: J.P.S., P.C. and J.T.; software: P.C., H.H., K.I., R.J.E., J.T., S.N. and Y.S.; formal analysis: J.P.S. and P.C.; investigation: S.N., Y.S., P.C., R.B. and A.R.; resources: S.R., M.D.S., J.R.S., S.M.W., S.W. and P.C.; data curation: S.N., Y.S., P.C., H.H. and K.I.; writing—original draft: S.N., Y.S., H.P., J.W., P.C., A.R., S.S. and R.B.; writing—review and editing: H.P., Y.S., H.H., K.I., J.T., J.K.P., J.P.S., S.W., S.M.W., J.R.S., M.D.S. and R.J.E.; visualization: P.C., Y.S., S.N. and A.R.; supervision: P.C., J.W., H.P., S.S., J.K.P. and S.W.; project administration: P.C., J.W., H.P., S.W., S.R., M.D.S., J.R.S. and S.M.W.; funding acquisition: P.C., J.W., H.P., S.W., S.R., M.D.S., J.R.S. and S.M.W.

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Correspondence to Sahin Naqvi, Joanna Wysocka or Peter Claes.

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Extended data

Extended Data Fig. 1 Number of additional brain shape loci contributed by hierarchical levels.

For all genome-wide (left) or study-wide (right) significant associations, associations with all segments in hierarchical levels up to the indicated number were masked, and the number of remaining associations was assessed.

Extended Data Fig. 2 Point-wise SNP heritability estimates across the mid-cortical surface.

Colors represent the total SNP heritability (computed by a linear mixed model approach, see Methods) at each point on the mid-cortical surface, represented by a set of three-dimensional coordinates in each individual.

Extended Data Fig. 3 Replication rates in the ABCD cohort by hierarchical level.

Only segments in the indicated hierarchical level were considered, and all loci (left) or locus-segment pairs (right) reaching genome-wide significance in those segments were tested for replication in the ABCD cohort at a 5% FDR.

Extended Data Fig. 4 Overlap between genome-wide significant brain shape loci and genome-wide significant loci from 430 other studies.

GWAS hits (number on x-axis) for other studies were obtained from the NCBI-EBI GWAS Catalog, and P-values (left, y-axis) and odds ratios (right, y-axis) for significance of overlap with regions in LD (> 0.2) with brain shape loci were computed using bedtools’ fisher function (see Methods). Note that relative to other traits with equivalent numbers of GWAS hits, face shape shows overlap with brain shape loci greater in both significance and magnitude.

Extended Data Fig. 5 Comparison of LDSC genetic correlations and Spearman correlation between pairs of univariate traits.

Each point represents a pair of univariate traits (of all those considered in this study, see Methods), while the x- and y-axes indicate the absolute value of the LDSC-estimated genetic correlation and the estimated genome-wide sharing of effects by the Spearman correlation method. Point colors and shapes indicate significance (P < 0.05) from LDSC or the Spearman correlation method, respectively. Exact p-values are provided in Supplementary Table 6.

Extended Data Fig. 6 Genetic correlations between RA (rheumatoid arthritis) and univariate brain-related traits.

Points (center of error bars) represent estimated genetic correlations. Error bars represent 95% confidence intervals. *, 5% FDR.

Extended Data Fig. 7 Genetic correlations between the most heritable brain (top two rows) or face (bottom two rows) shape PCs and other traits.

Points (center of error bars) represent estimated genetic correlations (rg) between the top ten shape PCs (for segment 1, the full brain or face) with heritability z-score > 3 and each of the indicated univariate traits using LD score regression. Error bars represent 95% confidence intervals. *, 5% FDR for indicated PC; +, 10% FDR.

Extended Data Fig. 8 SNP heritability of individual face shape PCs and multivariate face shape estimated by LDSC.

Points (center of error bars) represent estimated SNP heritability of each PC. Error bars represent 95% confidence intervals. The red line represents the mean heritability of all 70 PCs, and the blue line indicates the heritability obtained by applying LDSC to corrected χ2 statistics from the multivariate CCA GWAS using all 70 PCs.

Extended Data Fig. 9 Partitioned heritability enrichments for brain shape with respect to stage- and cell-type-specific brain organoid open chromatin.

S-LDSC coefficient Z-scores and heritability fold-enrichment for annotations corresponding to the indicated cell-type and differentiation day were computed as described in Methods. Regression lines represent the linear best fit with intercept and organoid differentiation day as dependent variable, and grey areas represent 95% confidence intervals. P-values are from a two-tailed F-test.

Extended Data Fig. 10 Partitioned heritability enrichments for brain shape with respect to open chromatin in CNCCs or early glial organoid cells, with or without 76 brain-face shared loci.

S-LDSC Z-scores were calculated using full brain shape as the trait and the most enriched craniofacial (top) or brain organoid (bottom) ATAC-seq dataset as annotations. Z-scores were re-estimated (blue) after removing all SNPs in the same approximately independent LD block as one of the 76 brain-face shared loci (see Methods for details).

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Naqvi, S., Sleyp, Y., Hoskens, H. et al. Shared heritability of human face and brain shape. Nat Genet 53, 830–839 (2021). https://doi.org/10.1038/s41588-021-00827-w

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