Thousands of long non-coding RNA (lncRNA) genes are annotated in the human genome. Recent studies showed the key role of lncRNAs in a variety of fundamental cellular processes. Dysregulation of lncRNAs can drive tumorigenesis and they are now considered to be a promising therapeutic target in cancer. However, how lncRNAs contribute to the development of hereditary diseases in human is still mostly unknown. This review is focused on hereditary diseases in the pathogenesis of which long non-coding RNAs play an important role. Fundamental research in the field of molecular genetics of lncRNA is necessary for a more complete understanding of their significance. Future research will help translate this knowledge into clinical practice which will not only lead to an increase in the diagnostic rate but also in the future can help with the development of etiotropic treatments for hereditary diseases.

An increasing number of studies emphasize the role of non-coding variants in the development of hereditary diseases. However, the interpretation of such variants in clinical genetic testing still remains a critical challenge due to poor knowledge of their pathogenicity mechanisms. It was previously shown that variants in 5′-untranslated regions (5′UTRs) can lead to hereditary diseases due to disruption of upstream open reading frames (uORFs). Here, we performed a manual annotation of upstream translation initiation sites (TISs) in human disease-associated genes from the OMIM database and revealed ∼4.7 thousand of TISs related to uORFs. We compared our TISs with the previous studies and provided a list of ‘high confidence’ uORFs. Using a luciferase assay, we experimentally validated the translation of uORFs in the ETFDH, PAX9, MAST1, HTT, TTN,GLI2 and COL2A1 genes, as well as existence of N-terminal CDS extension in the ZIC2 gene. Besides, we created a tool to annotate the effects of genetic variants located in uORFs. We revealed the variants from the HGMD and ClinVar databases that disrupt uORFs and thereby could lead to Mendelian disorders. We also showed that the distribution of uORFs-affecting variants differs between pathogenic and population variants. Finally, drawing on manually curated data, we developed a machine-learning algorithm that allows us to predict the TISs in other human genes.

Facial-scapular-humeral myodystrophy Landouzy-Dejerine (FSHD) is an autosomal dominant disease, the basis of its pathogenesis is ectopic expression of the transcription factor DUX4 in skeletal muscle. There are two types of the disease: FSHD1 (MIM:158900) and FSHD2 (MIM: 158901), which have different genetic causes but are phenotypically indistinguishable. In FSHD1, partial deletion of the D4Z4 repeats on the 4th chromosome affects the expression of DUX4, whereas FSHD2 is caused by the mutations in the protein regulating the methylation status of chromatin - SMCHD1. High variability of clinical picture, both intra - and inter-family indicates a large number of factors influencing clinical picture. There are key genetic, epigenetic and gender factors that influence the expressivity and penetrance of the disease. Using only one of these factors allows just a rough prediction of the course of the disease, which indicates the combined effect of all of the factors on the DUX4 expression and on the clinical picture. In this paper, we analyzed the impact of genetic, epigenetic and gender differences on phenotype and the possibility of using them for disease prognosis and family counselling. Key pathogenesis factors have been identified for FSHD. However, the pronounced intra - and inter-family polymorphism of manifestations indicates a large number of modifiers of the pathological process, many of which remain unknown.

Variants that disrupt normal pre-mRNA splicing are increasingly being recognized as a major cause of monogenic disorders. The SCN1A gene, a key epilepsy gene that is linked to various epilepsy phenotypes, is no exception. Approximately 10% of all reported variants in the SCN1A gene are designated as splicing variants, with many located outside of the canonical donor and acceptor splice sites, and most have not been functionally investigated. However, given its restricted expression pattern, functional analysis of splicing variants in the SCN1A gene could not be routinely performed. In this study, we conducted a comprehensive analysis of all reported SCN1A variants and their potential to impact SCN1A splicing and conclude that splicing variants are substantially misannotated and underrepresented. We created a splicing reporter system that consisting of 18 splicing vectors covering all 26 protein-coding exons with different genomic contexts and several promoters of varying strengths in order to reproduce the wild-type splicing pattern of the SCN1A gene, revealing cis-regulatory elements essential for proper recognition of SCN1A exons. Functional analysis of 95 SCN1A variants was carried out, including all 68 reported in the literature intronic variants, located outside of the splice sites canonical dinucleotides; 21 exonic variants of different classes (synonymous, missense, nonsense, and in-frame deletion), and six variants observed in patients with epilepsy. Interestingly, almost 20% of tested intronic variants had no influence on SCN1A splicing, despite being reported as causative in the literature. Moreover, we confirmed that the majority of predicted exonic variants affect splicing unraveling their true molecular mechanism. We utilized functional data to perform genotype-phenotype correlation, revealing distinct distribution patterns for missense and splice-affecting "missense" variants and observed no difference in the phenotype severity of variants leading to in-frame and out-of-frame isoforms, indicating that the Naᵥ1.1 protein is highly intolerant to structural variations. Our work demonstrates the importance of functional analysis in proper variant annotation and provides a tool for high-throughput delineation of splice-affecting variants in SCN1A in a whole-gene manner.