Fragile X Syndrome (FXS) is the second cause of intellectual disability after Down syndrome and the most prevalent cause of intellectual disability in males, affecting 1:5000–7000 men and 1:4000–6000 women. It is caused by an alteration of the FMR1 gene, which maps at the Xq27.3 band: more than 99% of individuals have a CGG expansion (>200 triplets) in the 5′ UTR of the gene, and FMR1 mutations and duplication/deletion are responsible for the remaining (<1%) molecular diagnoses of FXS. The aim of this review was to gather the current clinical and molecular knowledge about FXS to provide clinicians with a tool to guide the initial assessment and follow-up of FXS and to offer to laboratory workers and researchers an update about the current diagnostic procedures. FXS is a well-known condition; however, most of the studies thus far have focused on neuropsychiatric features. Unfortunately, some of the available studies have limitations, such as the paucity of patients enrolled or bias due to the collection of the data in a single-country population, which may be not representative of the average global FXS population. In recent years, insight into the adult presentation of the disease has progressively increased. Pharmacological treatment of FXS is essentially symptom based, but the growing understanding of the molecular and biological mechanisms of the disease are paving the way to targeted therapy, which may reverse the effects of FMRP deficiency and be a real cure for the disease itself, not just its symptoms. The clinical spectrum of FXS is wide, presenting not only as an isolated intellectual disability but as a multi-systemic condition, involving predominantly the central nervous system but potentially affecting any apparatus. Given the relative high frequency of the condition and its complex clinical management, FXS appears to have an important economic and social burden.

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

Kleefstra syndrome (KS) is a rare genetic condition resulting from either 9q34.3 microdeletions or mutations in the EHMT1 gene located in the same genomic region. To date, approximately 100 patients have been reported, thereby allowing the core phenotype of KS to be defined as developmental delay/intellectual disability, generalized hypotonia, neuropsychiatric anomalies, and a distinctive facial appearance. Here, to further expand the knowledge on genotype and phenotype of this condition, we report 2 novel cases: one patient carrying a 46-kb 9q34.3 deletion and showing macrocephaly never described in KS, and a second patient carrying a classic 9q34.3 deletion, presenting with a previously unreported skeletal feature (postaxial polydactyly of the right foot) and an unusual brain anomaly (olfactory bulb hypoplasia) observed via magnetic resonance imaging. Further, we provide a review of the current literature regarding KS and compare these 2 patients with those previously described, thereby confirming that the genotype-phenotype correlation in KS remains difficult to determine.

WAC (WW Domain Containing Adaptor With Coiled-Coil) mutations have been reported in only 20 individuals presenting a neurodevelopmental disorder characterized by intellectual disability, neonatal hypotonia, behavioral problems, and mildly dysmorphic features. Using targeted deep sequencing, we screened a cohort of 630 individuals with variable degrees of intellectual disability and identified five WAC rare variants: two variants were inherited from healthy parents; two previously reported de novo mutations, c.1661_1664del (p.Ser554*) and c.374C>A (p.Ser125*); and a novel c.381+2T>C variant causing the skipping of exon 4 of the gene, inherited from a reportedly asymptomatic father with somatic mosaicism. A phenotypic evaluation of this individual evidenced areas of cognitive and behavioral deficits. The patient carrying the novel splicing mutation had a clinical history of encephalopathy related to status epilepticus during slow sleep (ESES), recently reported in another WAC individual. This first report of a WAC somatic mosaic remarks the contribution of mosaicism in the etiology of neurodevelopmental and neuropsychiatric disorders. We summarized the clinical data of reported individuals with WAC pathogenic mutations, which together with our findings, allowed for the expansion of the phenotypic spectrum of WAC-related disorders.