Decoding Complex Genotype-Phenotype Interactions by Discretizing the Genome

Background: Despite the ease and affordability of genome sequencing in biomedical research, the genetic causes of many diseases or their subtypes remain unknown due to diverse biological mechanisms that complicate genotype-phenotype relationships. Most previous studies have focused on single variants or sets of variants presumed to be directly causal for the disease. However, incomplete penetrance, in which some individuals carry disease-associated variants yet exhibit no phenotype, suggests that these variants, the genomic background and other secondary factors combine to shape the susceptibility to the disease.Results: Here, we introduce a new methodology for genotype-phenotype mapping based on genomic hashes, unique representations of local genomic background. Each hash corresponds to a haplotype-resolved set of variants within one recombination-defined genomic region (haploblock). We provide a practical guide for using genomic hashes to train machine learning models that link genomic background and specific variant sets to phenotypic outcomes. We implemented this framework as a ready-to-use bioinformatics pipeline capable of fast, scalable, hash-based genome comparison. The pipeline is available on GitHub: https://github.com/collaborativebioinformatics/Haploblock_Clusters_ElixirBH25How it benefits the community: Genomic hashes offer a computationally efficient framework for large-scale genotype-phenotype mapping. By discretizing the genome into haploblocks, this approach will facilitate the search for causes of complex phenotypes across the entire genome and the prediction of precision prevention points and treatments.