Dcifer: an IBD-based method to calculate genetic distance between polyclonal infections

Area of Science:

• Genomics
• Epidemiology
• Parasitology

Background:

• Reconstructing pathogen transmission relies on genetic distance between infections.
• Existing methods struggle with polyclonal infections and diverse genetic data (SNPs, microsatellites).

Purpose of the Study:

• To develop a method for calculating genetic distance between complex, polyclonal infections.
• To provide a general solution accommodating unphased data and various genetic markers.

Main Methods:

• Introduced Dcifer (Distance for complex infections: fast estimation of relatedness).
• Employs an identity by descent (IBD) framework accounting for population allele frequencies and infection complexity.
• Uses combinatorics to handle unobserved haplotypes for fast processing.

Main Results:

• Dcifer provides reliable inference and estimates pairwise relatedness with uncertainty.
• Achieves 2-4 times greater statistical power in detecting related infections compared to identity by state methods.
• Demonstrates accurate and interpretable results, with relatedness aligning with geographic data.

Conclusions:

• Dcifer fills a methodological gap for analyzing genetic distance in polyclonal infections.
• The method is applicable to diverse genetic data and unphased samples.
• Dcifer is available as a public software package for pathogen transmission studies.