Chimeric Reference Panels for Genomic Imputation

Abstract

Despite transformative advances in genomic technologies, missing data remains a fundamental constraint that limits the full potential of genomic research across biological systems. Genotype imputation offers a remedy by inferring unobserved genotypes from observed data. However, conventional methods typically rely on external reference panels constructed from complete genome sequences of hundreds of individuals, a costly approach largely inaccessible for non-model organisms. Moreover, these methods generally overlook novel genomic positions not captured in existing panels. To overcome these limitations, we developed Retriever, a framework that bypasses the need for external reference panels. Retriever constructs a chimeric reference panel directly from the target samples using a sliding-window approach to identify and retrieve genomic partitions with complete data. By exploiting the complementary distribution of missing data across samples, Retriever assembles a panel that preserves local patterns of linkage disequilibrium and captures novel variants. Integrated with Beagle, Retriever achieves genotype imputation with accuracy consistently exceeding 95% across diverse datasets, including plants, animals, and fungi. By eliminating the need for costly external panels, Retriever offers an accessible, cost-effective solution that broadens the application of sophisticated genomic analyses across species.