A model-free method for genealogical inference without phasing and its application for topology weighting

Area of Science:

• Evolutionary biology
• Genomics
• Computational biology

Background:

• Ancestral Recombination Graphs (ARGs) are crucial for understanding evolutionary processes.
• Current ARG inference methods often require fully phased data and rely on demographic models.
• There is a need for robust methods applicable to unphased genotype data.

Purpose of the Study:

• To introduce a novel, model-free method for genealogical inference from unphased genotype data.
• To develop a topology weighting approach using the proposed method, overcoming sample size limitations.
• To provide accurate tools for analyzing relatedness and introgression in diverse species.

Main Methods:

• Developed Sequential Tree Inference by Collecting Compatible Sites (sticcs), a heuristic algorithm based on perfect phylogeny.
• Implemented a "collecting" procedure to integrate information from nearby genomic sites.
• Introduced a "stacking" procedure for topology weighting using multiple sticcs-inferred tree sequences from data subsets.

Main Results:

• sticcs demonstrates accurate ARG inference for small sample sizes.
• Topology weights derived from the stacking procedure using unphased data are more accurate than those from phased data using popular tools.
• The methods show promise for analyzing relatedness and introgression, including in polyploid species.

Conclusions:

• The sticcs and twisst2 packages offer a powerful, flexible approach to ARG inference and topology weighting.
• These methods reduce reliance on phased data and complex demographic models.
• The developed tools facilitate deeper insights into evolutionary history and population genetics across various organisms.