Populations in statistical genetic modelling and inference

TL;DR

This review explores how populations are defined in statistical genetic modeling, contrasting generative and inference models, and discusses practical tools and the theoretical versus real-world applicability of population concepts.

Contribution

It provides a comprehensive comparison of generative and statistical models for populations, clarifies their relationships, and evaluates current software tools in genetic inference.

Findings

Populations can be well theoretically defined and approximately exist in practice.

Generative models include drift, admixture, and spatial structure.

Statistical inference methods like PCA offer non-parametric insights.

Abstract

What is a population? This review considers how a population may be defined in terms of understanding the structure of the underlying genetics of the individuals involved. The main approach is to consider statistically identifiable groups of randomly mating individuals, which is well defined in theory for any type of (sexual) organism. We discuss generative models using drift, admixture and spatial structure, and the ancestral recombination graph. These are contrasted with statistical models for inference, principle component analysis and other `non-parametric' methods. The relationships between these approaches are explored with both simulated and real-data examples. The state-of-the-art practical software tools are discussed and contrasted. We conclude that populations are a useful theoretical construct that can be well defined in theory and often approximately exist in practice.