Learning a refinement model for variant analysis in non-human primate genomes

Abstract

Abstract Background Accurate variant calling is essential for genomic studies but is highly dependent on sequence alignment (SA) quality. In non-human primates, the lack of well-curated variant resources limits alignment postprocessing, leading to suboptimal SA and increased miscalls. DeepVariant, a leading variant caller, demonstrates high accuracy in human genomes but exhibits performance degradation under suboptimal SA conditions. Results To address this, we developed a decision tree-based refinement model that integrates alignment quality metrics and DeepVariant confidence scores to filter miscalls effectively. We defined suboptimal SA and optimal SA based on the presence or absence of postprocessing steps and confirmed that suboptimal SA significantly increases miscalls in both human and rhesus macaque genomes. Applying the refinement model to human suboptimal SA reduced the miscalling ratio (MR) by 52.54%, demonstrating its effectiveness. When applied to rhesus macaque genomes, the model achieved a 76.20% MR reduction, showing its potential for non-human primate studies. Alternative base ratio (ABR) analysis further revealed that the model refines homozygous SNVs more effectively than heterozygous SNVs, improving variant classification reliability. Conclusions Our refinement model significantly improves variant calling in suboptimal SA conditions, which is particularly beneficial for non-human primate studies where alignment postprocessing is often limited. We packaged our model into the Genome Variant Refinement Pipeline (GVRP), providing for researchers working on population genetics and molecular evolution. This work establishes a framework for enhancing variant calling accuracy in species with limited genomic resources