ABSTRACT
BACKGROUND: Primary ciliary dyskinesia (PCD) represents a group of rare hereditary disorders characterised by deficient ciliary airway clearance that can be associated with laterality defects. We aimed to describe the underlying gene defects, geographical differences in genotypes and their relationship to diagnostic findings and clinical phenotypes. METHODS: Genetic variants and clinical findings (age, sex, body mass index, laterality defects, forced expiratory volume in 1â s (FEV1)) were collected from 19 countries using the European Reference Network's ERN-LUNG international PCD Registry. Genetic data were evaluated according to American College of Medical Genetics and Genomics guidelines. We assessed regional distribution of implicated genes and genetic variants as well as genotype correlations with laterality defects and FEV1. RESULTS: The study included 1236 individuals carrying 908 distinct pathogenic DNA variants in 46 PCD genes. We found considerable variation in the distribution of PCD genotypes across countries due to the presence of distinct founder variants. The prevalence of PCD genotypes associated with pathognomonic ultrastructural defects (mean 72%, range 47-100%) and laterality defects (mean 42%, range 28-69%) varied widely among countries. The prevalence of laterality defects was significantly lower in PCD individuals without pathognomonic ciliary ultrastructure defects (18%). The PCD cohort had a reduced median FEV1 z-score (-1.66). Median FEV1 z-scores were significantly lower in CCNO (-3.26), CCDC39 (-2.49) and CCDC40 (-2.96) variant groups, while the FEV1 z-score reductions were significantly milder in DNAH11 (-0.83) and ODAD1 (-0.85) variant groups compared to the whole PCD cohort. CONCLUSION: This unprecedented multinational dataset of DNA variants and information on their distribution across countries facilitates interpretation of the genetic epidemiology of PCD and indicates that the genetic variant can predict diagnostic and phenotypic features such as the course of lung function.
Subject(s)
Genetic Association Studies , Genotype , Phenotype , Humans , Male , Female , Adult , Child , Adolescent , Young Adult , Middle Aged , Europe , Registries , Axonemal Dyneins/genetics , Forced Expiratory Volume , Child, Preschool , Kartagener Syndrome/genetics , Kartagener Syndrome/physiopathology , Genetic Variation , Mutation , Aged , Infant , Cytoskeletal Proteins , ProteinsABSTRACT
BACKGROUND: International guidelines disagree on how best to diagnose primary ciliary dyskinesia (PCD), not least because many tests rely on pattern recognition. We hypothesized that quantitative distribution of ciliary ultrastructural and motion abnormalities would detect most frequent PCD-causing groups of genes by soft computing analysis. METHODS: Archived data on transmission electron microscopy and high-speed video analysis from 212 PCD patients were re-examined to quantitate distribution of ultrastructural (10 parameters) and functional ciliary features (4 beat pattern and 2 frequency parameters). The correlation between ultrastructural and motion features was evaluated by blinded clustering analysis of the first two principal components, obtained from ultrastructural variables for each patient. Soft computing was applied to ultrastructure to predict ciliary beat frequency (CBF) and motion patterns by a regression model. Another model classified the patients into the five most frequent PCD-causing gene groups, from their ultrastructure, CBF and beat patterns. RESULTS: The patients were subdivided into six clusters with similar values to homologous ultrastructural phenotype, motion patterns, and CBF, except for clusters 1 and 4, attributable to normal ultrastructure. The regression model confirmed the ability to predict functional ciliary features from ultrastructural parameters. The genetic classification model identified most of the different groups of genes, starting from all quantitative parameters. CONCLUSIONS: Applying soft computing methodologies to PCD diagnostic tests optimizes their value by moving from pattern recognition to quantification. The approach may also be useful to evaluate atypical PCD, and novel genetic abnormalities of unclear disease-producing potential in the future.