Functional Interpretation of Genetic Variation: From Bioinformatics Pipelines to Epigenetic Modifiers

Genomics has produced increasingly comprehensive catalogs of genetic variation across populations and species, but determining how individual variants influence molecular, cellular, and organismal phenotypes remains a major challenge. This is particularly important for non-coding variants, whose effects may depend on cell type, developmental stage, environmental context, or disease state. Recent advances in genome-wide association studies, functional genomics, single-cell profiling, long-read sequencing, and machine learning are improving the annotation and prioritization of candidate variants. However, questions remain about the reliability, interpretability, and transferability of computational predictions, as well as how genetic variation alters chromatin accessibility, DNA methylation, histone modifications, transcription-factor binding, and three-dimensional genome organization. Integrative computational and experimental strategies are therefore needed to connect genetic variation with gene regulation and phenotype.This Research Topic aims to advance the functional interpretation of genetic variants by bringing together bioinformatics, statistical genetics, epigenomics, transcriptomics, and experimental biology. It will address how computational models and multi-omics approaches can identify variants with regulatory or phenotypic effects, how these predictions can be evaluated across biological contexts, and how epigenetic mechanisms mediate relationships between genotype and phenotype. Contributions may also examine the reproducibility, interpretability, and scalability of current analytical frameworks, and explore how genome editing, single-cell technologies, and other experimental systems can establish causal relationships. By connecting algorithm development with functional validation, the Research Topic seeks to support more accurate and biologically meaningful models of variant impact.To gather further insights into the computational and experimental interpretation of genetic variation, this Research Topic will focus on regulatory and epigenetic mechanisms across human, animal, plant, and other biological systems, while recognizing that studies may be limited by data quality, tissue accessibility, model-system relevance, and the availability of functional validation. We welcome contributions addressing, but not limited to, the following themes:o Computational methods, artificial intelligence, and machine learning for variant annotation and prioritizationo Integrative analyses of genetic, epigenomic, transcriptomic, proteomic, and phenotypic datao Functional interpretation of non-coding variants and regulatory elementso Prediction of variant effects on chromatin accessibility, DNA methylation, histone modifications, and transcription-factor bindingo Integration of genome-wide association studies with epigenomic and transcriptomic evidenceo Single-cell and spatial approaches to cell-type-specific variant interpretationo Long-read sequencing and haplotype-resolved analysis of regulatory variationo Genome editing and other experimental approaches for causal variant validationo Epigenetic modifiers and their roles in genotype–phenotype relationshipso Variant interpretation in complex traits, disease, development, evolution, and environmental responseso Benchmarking, reproducibility, explainability, and standardization of functional genomics pipelines Topic editors: Sagnik Sen, Abhijit Dasgupta, Tirso Pons, Kesava Asam Source: https://www.frontiersin.org/research-topics/84132/functional-interpretation-of-genetic-variation-from-bioinformatics-pipelines-to-epigenetic-modifiers