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

Area of Science:

• Population genomics
• Bioinformatics
• Computational biology

Background:

• Estimating population genetic statistics (e.g., nucleotide diversity π, divergence dxy) from large genomic datasets is computationally intensive.
• Existing methods often require all-site VCF files, which are resource-heavy to generate for large-scale studies.
• Accurate diversity estimates depend on accounting for missing data, a challenge with large VCFs.

Purpose of the Study:

• To introduce Callable Loci And More (clam), a novel tool for efficient population genetic analysis.
• To enable accurate estimation of population genetic statistics using variant-only VCFs, bypassing the need for all-site VCFs.
• To improve computational performance and reduce storage requirements for large-scale population genomic studies.

Main Methods:

• Developed clam, a tool that utilizes callable loci identified from depth information.
• Implemented clam to estimate population genetic statistics (π, dxy, FST) using variant-only VCF files.
• Validated clam's accuracy against all-site VCF methods using simulated data.
• Benchmarked clam's performance on a large muskox population genomic dataset.

Main Results:

• clam produces accurate estimates of nucleotide diversity (π), divergence (dxy), and fixation index (FST), matching results from all-site VCF approaches.
• The tool significantly reduces computational runtime and storage footprint compared to existing methods.
• Benchmarking on a large muskox dataset confirmed accurate π estimates with substantial runtime reduction.

Conclusions:

• clam offers an efficient and scalable solution for population genomic analyses with large datasets.
• The tool facilitates the study of increasingly large and diverse genomic data by overcoming computational bottlenecks.
• clam is available as a standalone program and integrated into snpArcher for reproducible analyses.