BackgroundPrimary ciliary dyskinesia (PCD) is a heterogeneous inherited disorder caused by mutations in approximately 50 cilia-related genes. PCD genotype-phenotype relationships have mostly arisen from small case series because existing statistical approaches to investigate relationships have been unsuitable for rare diseases.MethodsWe applied a topological data analysis (TDA) approach to investigate genotype-phenotype relationships in PCD. Data from separate training and validation cohorts included 396 genetically defined individuals carrying pathogenic variants in PCD genes. To develop the TDA models, twelve clinical and diagnostic variables were included. TDA-driven hypotheses were subsequently tested using traditional statistics.ResultsDisease severity at diagnosis measured by FEV1 z-score was (i) significantly worse in individuals with CCDC39 mutations compared to other gene mutations and (ii) better in those with DNAH11 mutations; the latter also reported less neonatal respiratory distress. Patients without neonatal respiratory distress had better preserved FEV1 at diagnosis. Individuals with DNAH5 mutations were phenotypically diverse. Cilia ultrastructure and beat pattern defects correlated closely to specific causative gene groups, confirming these tests can be used to support a genetic diagnosis.ConclusionsThis large scale multi-national study presents PCD as a syndrome with overlapping symptoms and variation in phenotype, according to genotype. TDA modelling confirmed genotype-phenotype relationships reported by smaller studies (e.g. FEV1 worse with CCDC39 mutations), and identified new relationships, including FEV1 preservation with DNAH11 mutations and diversity of severity with DNAH5 mutations.

Perinatal death, of a fetus or newborn, is a devastating event for families. Following nationwide multicentre recruitment, we assessed 'genomic autopsy' as an adjunct to standard autopsy for 200 families who experienced perinatal death, and provided a de nite or candidate genetic diagnosis in 105 families. From this understudied cohort, half of the (candidate) diagnoses were phenotype expansions or novel disease genes, revealing previously unknown in-utero presentations of existing developmental disorders, and genomic disorders that are likely incompatible with life. Among the de nite diagnoses, 43% were recessively or dominantly inherited, posing a 25% or 50% recurrence risk for future pregnancies. Ten families used their diagnosis for preimplantation or prenatal diagnosis of 12 pregnancies, facilitating the delivery of ten healthy newborns and management of two affected pregnancies. We emphasize the clinical importance of genomic investigations of perinatal death, with short turn-around times, enabling accurate counselling and options for families to prevent recurrence.

Costello syndrome (CS) is caused by heterozygous HRAS germline mutations. Most patients share the HRAS variant p.Gly12Ser that is associated with a typical, homogeneous phenotype. Rarer pathogenic HRAS variants (e.g., p.Thr56Ile) were identified in individuals with attenuated CS phenotypes. The obvious phenotypical variability reflects different dysfunctional consequences of distinct HRAS variants. We report on two boys with the novel de novo HRAS variant c.466 C > T p.(Phe156Leu). Both had severe feeding difficulties, airway obstruction and developmental delay, which are typical findings in CS. They showed subtle facial and dermatologic features consistent with attenuated CS. They significantly differed in their musculoskeletal, cardiovascular and endocrinologic manifestations underscoring the clinical variability of individuals with identical, in particular rarer pathogenic HRAS variants. Functional studies revealed enhanced effector-binding, increased downstream signaling activation and impaired growth factor-induced signaling dynamics in cells expressing HRASPhe156Leu. Our data further illustrate the molecular and phenotypic variability of CS.

Adaptive sampling strategies in PIV have been shown to efficiently combine the need for limited user-dependence with increased performances in terms of spatial resolution and computational effort, thus rendering such approaches of great interest. The allocation of correlation windows across the spatial image domain is dependent on the interpretation of an underlying objective function, and the distribution of windows accordingly. It is important that such allocation is computationally efficient, robust to changing objective functions and conditions, and conducive to high quality sampling. In this paper, an alternative sample distribution method, based on adaptive incremental stippling, is presented and shown to combine the speed of PDF-based methods with the quality of ‘ideal’ spring-force methods. Case-dependent parameter tuning is no longer necessary, thus improving robustness. In addition, an algorithm to adaptively size initial correlation windows is proposed to further minimise user dependence.