Inference of Ancestral Recombination Graphs Using ARGweaver

Area of Science:

• Population Genetics
• Bioinformatics
• Computational Biology

Background:

• Understanding genetic variation and evolutionary history requires detailed models of coalescence and recombination.
• The ancestral recombination graph (ARG) provides a comprehensive framework for these evolutionary events among sampled sequences.

Purpose of the Study:

• To describe the usage and application of the ARGweaver program for estimating ARGs.
• To guide users in data preparation, program execution, and result interpretation for population genetic analyses.

Main Methods:

• Utilizes the ARGweaver program to infer the ancestral recombination graph from sequence data.
• Details data preparation steps and key considerations for running ARGweaver analyses.
• Demonstrates ARG estimation and interpretation using the DARC (Duffy) gene as a case study.

Main Results:

• ARGweaver successfully estimates ARGs for up to approximately 100 genome sequences.
• The analysis of the DARC gene revealed signatures of natural selection and Neandertal introgression.
• Mutation event dates were estimated, showcasing the program's utility in evolutionary inference.

Conclusions:

• ARGweaver is a powerful tool for detailed population genetic analyses, offering insights into evolutionary processes.
• The program facilitates the detection of selection, introgression, and the dating of genetic events.
• This chapter provides practical guidance for new users to effectively employ ARGweaver.