Variant Curation and Classification in Rare Disease Genomics: Standards and Emerging Tools for Single-Nucleotide Variant Analysis.

The widespread adoption of next-generation sequencing (NGS) for rare disease diagnosis has transformed clinical genomics. Multiple approaches have been proposed to standardize and improve the analysis, classification, interpretation, and reporting of genetic variants in clinical settings. This review provides a focused, practical overview of variant curation and current classification frameworks, particularly for single-nucleotide variants (SNVs) and small insertions/deletions within coding regions. These variant types remain the most frequent findings in clinical sequencing and are the primary targets of existing classification guidelines. While highly relevant, other variant types and analytical approaches fall outside the scope of this focused review and are addressed elsewhere in the literature and within this special issue. We distinguish between three related yet conceptually distinct processes: variant curation, defined as the systematic collection and evaluation of evidence; variant classification, defined as the standardized assignment of pathogenicity categories according to established guidelines; and clinical interpretation, which contextualizes a classified variant within an individual patient's phenotype to inform medical decision-making. We will examine advances in the field of variant classification that contribute to improving molecular diagnosis of rare diseases, highlighting the achievements, limitations, and challenges present in each aspect addressed. Key developments in variant classification are reviewed, including the 2015 guidelines and subsequent refinements, population databases, computational predictors, multiplexed functional studies, reanalysis efforts, and collaborative initiatives. Many challenges remain to be addressed, such as the interpretation of non-coding variants, the transition to updated classification frameworks, the diversity in population databases, and the development of new predictors, among others.