The first genetic landscape of inherited retinal dystrophies in Portuguese patients identifies recurrent homozygous mutations as a frequent cause of pathogenesis.

Inherited retinal diseases (IRDs) are a group of ocular conditions characterized by an elevated genetic and clinical heterogeneity. They are transmitted almost invariantly as monogenic traits. However, with more than 280 disease genes identified so far, association of clinical phenotypes with genotypes can be very challenging, and molecular diagnosis is essential for genetic counseling and correct management of the disease. In addition, the prevalence and the assortment of IRD mutations are often population-specific. In this work, we examined 230 families from Portugal, with individuals suffering from a variety of IRD diagnostic classes (270 subjects in total). Overall, we identified 157 unique mutations (34 previously unreported) in 57 distinct genes, with a diagnostic rate of 76%. The IRD mutational landscape was, to some extent, different from those reported in other European populations, including Spanish cohorts. For instance, the EYS gene appeared to be the most frequently mutated, with a prevalence of 10% among all IRD cases. This was, in part, due to the presence of a recurrent and seemingly founder mutation involving the deletion of exons 13 and 14 of this gene. Moreover, our analysis highlighted that as many as 51% of our cases had mutations in a homozygous state. To our knowledge, this is the first study assessing a cross-sectional genotype-phenotype landscape of IRDs in Portugal. Our data reveal a rather unique distribution of mutations, possibly shaped by a small number of rare ancestral events that have now become prevalent alleles in patients

De novo variants in CACNA1E found in patients with intellectual disability, developmental regression and social cognition deficit but no seizures

Background De novo variants in the voltage-gated calcium channel subunit α1 E gene (CACNA1E) have been described as causative of epileptic encephalopathy with contractures, macrocephaly and dyskinesias. Methods Following the observation of an index patient with developmental delay and autism spectrum disorder (ASD) without seizures who had a de novo deleterious CACNA1E variant, we screened GeneMatcher for other individuals with CACNA1E variants and neurodevelopmental phenotypes without epilepsy. The spectrum of pathogenic CACNA1E variants was compared to the mutational landscape of variants in the gnomAD control population database. Results We identified seven unrelated individuals with intellectual disability, developmental regression and ASD-like behavioral profile, and notably without epilepsy, who had de novo heterozygous putatively pathogenic variants in CACNA1E. Age of onset of clinical manifestation, presence or absence of regression and degree of severity were variable, and no clear-cut genotype–phenotype association could be recognized. The analysis of disease-associated variants and their comparison to benign variants from the control population allowed for the identification of regions in the CACNA1E protein that seem to be intolerant to substitutions and thus more likely to harbor pathogenic variants. As in a few reported cases with CACNA1E variants and epilepsy, one patient showed a positive clinical behavioral response to topiramate, a specific calcium channel modulator. Limitations The significance of our study is limited by the absence of functional experiments of the effect of identified variants, the small sample size and the lack of systematic ASD assessment in all participants. Moreover, topiramate was given to one patient only and for a short period of time. Conclusions Our results indicate that CACNA1E variants may result in neurodevelopmental disorders without epilepsy and expand the mutational and phenotypic spectrum of this gene. CACNA1E deserves to be included in gene panels for non-specific developmental disorders, including ASD, and not limited to patients with seizures, to improve diagnostic recognition and explore the possible efficacy of topiramate

CNV Detection from Exome Sequencing Data in Routine Diagnostics of Rare Genetic Disorders: Opportunities and Limitations

To assess the potential of detecting copy number variations (CNVs) directly from exome sequencing (ES) data in diagnostic settings, we developed a CNV-detection pipeline based on ExomeDepth software and applied it to ES data of 450 individuals. Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.

In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N = 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome

A hypomorphic variant in EYS detected by genome-wide association study contributes toward retinitis pigmentosa

The genetic basis of Japanese autosomal recessive retinitis pigmentosa (ARRP) remains largely unknown. Herein, we applied a 2-step genome-wide association study (GWAS) in 640 Japanese patients. Meta-GWAS identified three independent peaks at P A failed to restore rhodopsin mislocalization induced by morpholino-mediated knockdown of eys in zebrafish, consistent with the variant being pathogenic. c.2528 G > A solved an additional 7.0% of Japanese ARRP cases. The third peak was in linkage disequilibrium with a common non-synonymous variant (c.7666 A > T, p.S2556C), possibly representing an unreported disease-susceptibility signal. GWAS successfully unraveled genetic causes of a rare monogenic disorder and identified a high frequency variant potentially linked to development of local genome therapeutics

Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity

We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer. When applied to 840 genes from the ClinVar database, this approach detected a significant non-random distribution of pathogenic and benign variants in 387 (46%) and 172 (20%) genes, respectively, revealing that variant clustering is widespread across the human exome. This clustering likely occurs as a consequence of mechanisms shaping pathogenicity at the protein level, as illustrated by the overlap of some clusters with known functional domains. We then took advantage of these findings to develop a pathogenicity predictor, MutScore, that integrates qualitative features of DNA substitutions with the new additional information derived from this positional clustering. Using a random forest approach, MutScore was able to identify pathogenic missense mutations with very high accuracy, outperforming existing predictive tools, especially for variants associated with autosomal-dominant disease and cancer. Thus, the within-gene clustering of pathogenic and benign DNA changes is an important and previously underappreciated feature of the human exome, which can be harnessed to improve the prediction of pathogenicity and disambiguation of DNA variants of uncertain significance