Efficient Estimation of Nucleotide Diversity and Divergence Using Callable Loci (and More)

The increasing scale of population genomic datasets presents computational challenges in estimating summary statistics such as nucleotide diversity (π) and divergence (dxy). Accurate estimates of diversity require knowledge of missing data, and existing tools require all-site VCFs. However, generating these files is computationally expensive for large datasets. Here, we introduce Callable Loci And More (clam), a tool that leverages callable loci-determined from depth information-to estimate population genetic statistics using a variant-only VCF. This approach offers improvements in storage footprint and computational performance compared to contemporary methods. We validate clam's accuracy using simulated data, demonstrating that it produces estimates of π, dxy, and fixation index (FST) identical to those from all-site VCF approaches. We then benchmark clam using a large muskox dataset and demonstrate that it produces accurate estimates of π while substantially reducing runtime requirements compared to current best-practice methods. clam provides an efficient and scalable alternative for population genomic analyses, facilitating the study of increasingly large and diverse datasets. clam is available as a standalone program and integrated into snpArcher for efficient reproducible population genomic analysis.