HIGH-PRECISION MOLECULAR GENETIC DIAGNOSIS OF RARE HEREDITARY SYNDROMES: MODERN ALGORITHMS

Abstract

Clinical verification of rare hereditary pathologies remains one of the most resource-intensive tasks of modern medicine: with a combined population prevalence of 4-8%, more than 80% of nosologies are monogenic in nature, however, traditional phenotype-oriented approaches demonstrate limited sensitivity in conditions of pronounced locus heterogeneity and variable expressivity. The aim of the work is to systematize the methodological principles of constructing diagnostic algorithms based on new generation sequencing (NGS), critically evaluate their reproducibility and formulate adaptive strategies for interpreting genetic variants for routine clinical practice.

In the course of the work, a critical analytical review of the literature (2015-2024) on PubMed, Scopus, Web of Science, ClinVar and Orphanet databases was conducted using the principles of PRISMA 2020 for narrative synthesis. Technological platforms (targeted panels, WES, WGS, long reads), bioinformatic pipelines (alignment, colling, annotation), phenotype-based prioritization tools (Exomiser, PhenIX), and machine learning algorithms for predicting pathogenicity were evaluated.

It was found that diagnostic effectiveness varies depending on the study design: gene panels provide 25-60% detectability in clearly phenotyped syndromes, exomic sequencing in the trio design increases effectiveness by up to 40%, and the integration of long reads and multimix data (transcriptomics, methylomic analysis) provides an additional 10-15% increase in refractory cases. The key limitation remains the interpretation of variants of uncertain significance (VUS), where AI algorithms (CADD, REVEL, AlphaMissense) perform an auxiliary function, but not a substitute for the expert. The necessity of implementing dynamic reinterpretation protocols is shown: repeated analysis after 18-24 months makes it possible to verify the diagnosis in an additional 8-10% of patients.

The optimal diagnostic algorithm is not a static protocol, but an adaptive multi-level system, where the choice of platform, bioinformatic pipeline and interpretation strategy is determined by the clinical context. The sustainable implementation of high-precision methods requires standardization of pipelines (containerization, external quality assessment), interdisciplinary verification of results and the development of network models of expertise to ensure equal access to personalized diagnostics.