Comprehensive Evaluation of Genotype Imputation Tools for Ultra-low-depth Whole-Genome Sequencing Data

Ultra-low-depth sequencing (ULDS) is a cost-effective strategy for large-scale genomic studies, but its utility hinges on accurate genotype imputation. This study evaluates three imputation tools -- STITCH, QUILT2, and GLIMPSE2 -- across varying sequencing depths and sample sizes, using the China Kadoorie Biobank (CKB) and The 1000 Genomes Project (1KGP) East Asian (EAS) reference panels. Critical performance divergences are demonstrated: Sample size sensitivity: STITCH's accuracy improved markedly with larger samples, whereas QUILT2 and GLIMPSE2 showed minimal dependence on sample size. Reference panel optimization: Population-specific CKB significantly enhanced accuracy for QUILT2 and GLIMPSE2 but had a negligible impact on STITCH, which relies on internal haplotype inference. Depth thresholds: All tools achieved robust accuracy at moderate sequencing depths (≥ 0.5x), but STITCH underperformed drastically at ultra-low depths (≤ 0.1x). GLIMPSE2 with CKB delivered the highest overall accuracy, while QUILT2 balanced precision and computational efficiency. For non-invasive prenatal testing (NIPT) data, GLIMPSE2+CKB maintained sufficient accuracy for downstream analyses. A decision framework is proposed, prioritizing population-matched panels and depth-adapted tools, offering actionable guidelines for optimizing ULDS-WGS in diverse research settings. These insights bridge methodological advancements with practical implementation, enabling cost-effective scaling of genomic studies without compromising data quality.