Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder.

Cluster analyzes of facial models of autistic patients aim to clarify whether it is possible to diagnose autism on the basis of facial features and further to stratify the autism spectrum disorder. We performed a cluster analysis of sets of 3D scans of ASD patients (116) and controls (157) using Euclidean and geodesic distances in order to recapitulate the published results on the Czech population. In the presented work, we show that the major factor determining the clustering structure and consequently also the correlation of resulting clusters with autism severity degree is body mass index corrected for age (BMIFA).

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English.

Zimmermann-Laband syndrome in monozygotic twins with a mild neurobehavioral phenotype lacking gingival overgrowth-A case report of a novel KCNN3 gene variant.

Zimmermann-Laband syndrome is a rare, heterogeneous disorder characterized by gingival hypertrophy or fibromatosis, aplastic/hypoplastic nails, hypoplasia of the distal phalanges, hypertrichosis, various degrees of intellectual disability, and distinctive facial features. Three genes are considered causative for ZLS: KCNH1, KCNN3, and ATP6V1B2. We report on a pair of female concordant monozygotic twins, both carrying a novel pathogenic variant in the KCNN3 gene, identified using exome sequencing.

Expanding the phenotype spectrum associated with pathogenic variants in the COL2A1 and COL11A1 genes.

We report the clinical findings of 26 individuals from 16 unrelated families carrying variants in the COL2A1 or COL11A1 genes. Using Sanger and next-generation sequencing, 11 different COL2A1 variants (seven novel), were identified in 13 families (19 affected individuals), all diagnosed with Stickler syndrome (STL) type 1. In nine families, the COL2A1 disease-causing variant arose de novo.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies.

A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics.

Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes.

Cost-of-illness analysis and regression modeling in cystic fibrosis: a retrospective prevalence-based study.

Background: Economic data pertaining to cystic fibrosis (CF), is limited in Europe generally, and completely lacking in Central and Eastern Europe. We performed an analysis of all direct costs associated with CF relative to key disease features and laboratory examinations.

Methods: A retrospective prevalence-based cost-of-illness (COI) study was performed in a representative cohort of 242 CF patients in the Czech Republic, which represents about 65 % of all Czech CF patients.

Distribution of CFTR mutations in the Czech population: positive impact of integrated clinical and laboratory expertise, detection of novel/de novo alleles and relevance for related/derived populations.

Background: This two decade long study presents a comprehensive overview of the CFTR mutation distribution in a representative cohort of 600 Czech CF patients derived from all regions of the Czech Republic.

Methods: We examined the most common CF-causing mutations using the Elucigene CF-EU2v1™ assay, followed by MLPA, mutation scanning and/or sequencing of the entire CFTR coding region and splice site junctions.

Results: We identified 99.