Inference with selection, varying population size, and evolving population structure: application of ABC to a...

Area of Science:

• Evolutionary biology
• Population genetics
• Ecological modeling

Background:

• Genetic data analysis commonly relies on models with restrictive assumptions about demography, population structure, and selection.
• Existing methods often overlook the interplay between genetic evolution, demographic changes, and ecological interactions.

Purpose of the Study:

• To develop a novel stochastic birth-death model incorporating competitive interactions and asexual reproduction for inferring ecological, demographic, and genetic parameters.
• To establish a framework for analyzing genetic diversity and inferring population history under more realistic conditions.
• To investigate the relationship between genetic diversity, birth-death rates, and inter/intra-strain competition.

Main Methods:

• Proposed an original model of phylogenies with trait structure, interactions, and multiple merging capabilities.
• Developed an Approximate Bayesian Computation (ABC) framework for model-based inference from genetic data.
• Applied the procedure to simulated data and real Y-chromosome microsatellite data from Central Asian human populations.

Main Results:

• Demonstrated the relationship between genetic diversity, genealogies, birth/death rates, and individual competition.
• Successfully applied the ABC framework to analyze complex genetic datasets.
• Identified potential differences in fertility rates linked to distinct social organizations in Central Asian populations.

Conclusions:

• The developed birth-death model provides a more comprehensive approach to inferring demographic and ecological parameters from genetic data.
• The study highlights the importance of incorporating ecological interactions into population genetic models.
• The analysis of Central Asian Y-chromosome data suggests a link between social organization and human fertility, warranting further investigation.