DipSkmer: Reference-free population genomics with diploid genome skims

Area of Science:

• Population genetics
• Genomics
• Biodiversity monitoring

Background:

• Genetic diversity is crucial for tracking population health and is measured by genomic distance.
• Whole-genome sequencing (WGS) is expensive, while genome skimming is cost-effective but difficult to analyze for population genetics.
• Existing methods like Mash and Skmer struggle with diploid organisms due to heterozygosity.

Purpose of the Study:

• To develop a method for accurate estimation of genetic diversity in diploid organisms using genome skimming.
• To address the limitations of existing alignment-free methods in population genetics.

Main Methods:

• Developed DipSkmer, a novel method based on coalescent theory and Jaccard similarity.
• Mathematically derived the relationship between Jaccard index and population size parameter (θ) for diploids.
• Estimated auxiliary variables like coverage and sequencing error from genome skims.

Main Results:

• DipSkmer provides more accurate estimates of coverage, sequencing error, and pairwise nucleotide distance for diploid samples.
• DipSkmer outperforms alignment-free methods (Mash, Skmer) for diploids with low genetic distances (<2%).
• DipSkmer's results closely approximate alignment-based tools like ANGSD.

Conclusions:

• DipSkmer enhances the utility of genome skimming for population genetics in diploid species.
• This method offers a cost-effective and accurate approach for biodiversity monitoring and conservation.
• DipSkmer represents a significant advancement for analyzing low-coverage WGS data in population studies.