Peter D. Claes

This study uses a pleiotropy-informed GWAS method to identify new orofacial cleft risk loci by leveraging genetic associations with facial shape.

The paper uses 3D craniofacial data and genome-wide association analysis to link craniofacial variation to genetic diversity, evolutionary processes, and clinical conditions in modern humans.

This paper uses 3D facial morphometry to analyze facial shape changes in Cri-du-Chat syndrome and related genetic conditions, identifying genotype-phenotype correlations and critical genomic regions.

This paper improves polygenic prediction of nasal shape by using multivariate GWAS, optimized phenotypes, and benchmarked PGS methods, achieving higher variance explained than traditional approaches.

INMTD integrates omics and 3D imaging data to produce unconfounded individual subgroups with biologically meaningful embeddings.

This study compares univariate phenotyping strategies for 3D facial shape in GWAS, finding that inter-landmark distances and AE-derived dimensions show higher heritability than facial resemblance to gestalts.

The paper identifies genomic loci associated with achondroplasia-like facial variation in the general population using a syndrome-informed phenotyping approach.

The paper introduces four VAE-based deconfounding frameworks for multi-omics clustering, showing they reduce confounding effects and improve biological clustering.

The paper identifies a SNP that regulates facial shape and disease risk by affecting PKDCC expression during chondrogenesis, showing its impact on both normal variation and clefting.

This paper introduces a single-surface craniofacial bone mask and MeshMonk framework for automated 3D landmarking of human skulls, achieving high accuracy and reliability in phenotyping.

The paper shows how DNA motifs guide transcription factor cooperativity in embryonic face and limb mesenchyme, shaping cell identity and morphology.

netMUG identifies actionable subgroups using network-guided multi-view clustering, showing improved performance over existing methods in synthetic and real data.

This paper introduces a method to extract genetically relevant facial phenotypes by optimizing trait heritability, showing higher heritability and better GWAS performance compared to principal components.

This study performs a joint multi-ancestry and admixed GWAS on cranial vault shape, identifying 30 genome-wide significant loci and highlighting genetic pathways involved in craniofacial development.

The paper evaluates ensemble learning for classifying high-dimensional phenotypes across 20 datasets, finding ensemble models improve accuracy by up to 3% over individual learners.

The paper modulates SOX9 levels in human facial progenitors to study dosage sensitivity and identifies regulatory elements and genes that are sensitive to SOX9 changes.

This paper introduces a hybrid autoencoder method for robust population structure inference, improving accuracy in the presence of missing data and relatedness.

This study identifies a new role for Twist1 in neural tube development and epithelial-to-mesenchymal transition in cranial neural crest cells, showing its interaction with Irf6 and impact on cytoskeletal remodeling and facial morphology.

The paper investigates genetic differences between bilateral and unilateral cleft lip and palate subtypes using genome-wide association studies and finds distinct genetic architectures.

The paper investigates the impact of low-frequency coding variants on facial shape in Europeans, identifying seven genes associated with facial morphology and showing a specific variant's effect on chin shape.

This study identifies 203 genomic regions associated with facial variation using GWAS on multivariate shape phenotypes, revealing novel regions and epistatic interactions.

This study identifies shared genetic loci influencing brain and facial shape, distinct from cognitive traits, suggesting a developmental link between brain and face formation.

This paper introduces SUGIBS, a robust genome-wide ancestry inference method, and applies it to predict ancestry faces using the 1000 Genomes Project and facial imaging data.

The paper analyzes genomic data from Mesolithic Scandinavians to show two colonization routes and genetic adaptations to high-latitude environments.