Biomedical subjects

Peter Claes

Publications and source records attributed to Peter Claes.

3 recordsLinked to original sources

PubMed2026

Shared genetic basis and structure of syndromic and normal facial variation.

The question of how gene mutations of large effect and common variants of small effect relate to phenotypic variation dates from the origins of genetics. Mendelian diseases result from rare germline variants with major effects, while complex traits are associated with multiple, mostly common variants of small effect. High-dimensional phenotypes, such as facial shape, can shed new light on this age-old dichotomy, as their variation can be characterized in terms of directions in multivariate morphospace. Within such spaces, do Mendelian disease mutations move phenotypes along the same directions as common variants, or do they forge new directions that diverge from the common structure of background variation? Here, we analyze facial shape variation for 66 syndromes, quantify multivariate axes of facial shape variation for each syndrome, and test whether common genetic variants in cohorts of non-syndromic subjects are associated with phenotypic position along these same axes. We find that syndromic facial shape generally follows the background variance-covariance structure of facial shape in the general population. Furthermore, syndromic probands' unaffected relatives have subtle facial morphology resembling the syndromes of their affected relatives. These results suggest that Mendelian disease variants act on facial shape in ways similar to common variants. Syndromic probands with higher "severity" likely occur on genetic backgrounds with higher cumulative severity of common variants for each syndromic axis. These findings position Mendelian diseases at extremes along phenotypic continua that exist in the background population rather than as qualitatively different phenotypes distinct from the overall structure of normal human phenotypic variation.

Humans↗

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗

PubMed2025

Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants.

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Humans↗