Seppe Goovaerts

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

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

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.

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