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Claessens, N.

Publications and source records attributed to Claessens, N..

2 recordsLinked to original sources

The craniofacial shape of modern humans embodies genomic signatures of evolution, diversity, and clinical conditions

Human craniofacial morphology is a hallmark of our species diversity and evolutionary history, shaped by adaptation, introgression, and global dispersal. Cranial globularization and chin emergence are well-documented morphological transformations whose genetic basis remains poorly understood, whereas Neandertal introgression is primarily documented through genomic evidence. How these evolutionary phenomena relate to craniofacial variation in present-day humans remains largely unresolved. Here, we leverage 3D craniofacial data from over 50,000 UK Biobank participants and employ a multivariate, multiscale genome-wide association approach to define axes of variation aligned with inter-population allele frequency shifts, evolutionary processes, and clinical conditions. We identify continuous craniofacial trends within our cohort that mirror global patterns of genetic diversity, indicating that facial differences between human populations arise at the phenotypic axes already present within a single population. We further demonstrate that modern human-derived alleles underlie the origins of the human chin by reducing midfacial projection relative to other hominins and reveal the persistent effects of Neandertal introgression on craniofacial diversity today. We also model genetically informed endophenotypes for orofacial clefts, obstructive sleep apnoea, and myopia. These findings provide insights into our species evolutionary history and endophenotypes of clinical conditions and establish a framework for contextualizing craniofacial diversity into biologically meaningful axes of variation relevant to diverse scientific disciplines.

genetics↗

Optimizing Polygenic Scores for Complex Morphological Traits: A Case Study in Nasal Shape Prediction

Polygenic scores (PGS) facilitate the prediction of an individuals phenotype from their genotype. Typically, PGS methods apply regularization or use a clumping and thresholding (C+T) approach to handle SNP inclusion. To achieve good prediction accuracy, these approaches rely on effect size estimates from well-powered genome-wide association studies (GWAS). However, this is currently not feasible for morphological shape when phenotyped as univariate traits. Here, we introduce a novel framework to enhance polygenic prediction through three key components: (1)leveraging multivariate GWAS summary statistics for improved SNP selection, (2) defining genetically informative phenotypes, and (3) benchmarking PGS methods to select the optimal model. Our approach integrates multivariate GWAS, which performs an omnibus test against all phenotypic variables jointly with increased power. Specifically, our approach leverages P values from multivariate GWAS to improve SNP selection while maintaining the effect size estimates for the univariate trait under investigation, allowing the use of current PGS tools. We evaluated our proposed method for predicting 3D nasal morphology using a dataset of 52,896 individuals of European ancestry from the UK Biobank. Using the C+T method, SNP selection based on multivariate GWAS resulted in significantly improved phenotypic prediction (P = 9.74e-5) for eigen-shapes, with a mean variance explained of 3.88% (SD = 1.59%) compared to 2.02% (SD = 1.10%) using a traditional univariate approach in the test set (n = 2,896). We also tested whether heritability-optimized phenotypes were more predictable than eigen-shapes derived from principal component analysis (PCA). On average, with SNP selection based on multivariate GWAS using the C+T method, heritability-optimized phenotypes yielded greater predictive performance, with PGS scores explaining 2.72%-10.37% of phenotypic variance, compared to 1.05%-6.84% for eigen-shapes. Furthermore, benchmarking several PGS methods revealed that LDpred2 consistently achieved the best performance for predicting nasal morphology. Our results demonstrate that combining multivariate GWAS P values with optimized phenotypes and advanced PGS models leads to more accurate polygenic prediction for complex morphological traits.

bioinformatics↗