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Hilde Peeters

Publications and source records attributed to Hilde Peeters.

3 recordsLinked to original sources

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

Evidence supporting the role of GIGYF2 in synapse development and autism.

Autism spectrum disorder (ASD) is a heterogeneous condition in which genetically defined subtypes offered insights into underlying biological mechanisms and potential targeted treatments. Here, we investigate the clinical and pathogenic significance of GIGYF2 variants in ASD through an integrated approach combining clinical genetics, conditional knockout (cKO) mouse models, neurobiology, and molecular studies. Through targeted sequencing, large-scale genomic data analysis of neurodevelopmental disorder cohorts, and international collaborations, we identified ten affected individuals from eight families harboring de novo or dominantly inherited likely gene-disruptive (LGD) variants and 13 affected individuals from 13 families with de novo missense variants in GIGYF2. Clinical characterization of 16 probands with GIGYF2 variants revealed common features, including ASD, language problems, intellectual disability, and anxiety. In a Gigyf2 cKO mouse model, we observed pronounced autistic-like behaviors, cognitive deficits, and anxiety-like behaviors, mirroring phenotypes observed in affected individuals. Mechanistically, Gigyf2 deficiency disrupted synaptic homeostasis, as evidenced by altered spine density and miniature excitatory postsynaptic currents, and impaired IGF-1R/mTOR signaling, along with dysregulation of synapse-related genes such as Nrp2. Pharmacological inhibition of mTOR with rapamycin or Torin1, as well as Nrp2 knockdown rescued synaptic defects in Gigyf2 KO neurons. These findings define a novel ASD subtype associated with GIGYF2 variants and establish GIGYF2 as a key regulator of synaptic development and function, implicating GIGYF2 dysfunction in ASD pathogenesis and highlighting the IGF-1R/mTOR pathway as a potential therapeutic target for GIGYF2-related ASD subtype.

Journal Article

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