Population stratification using a statistical model on hypergraphs

TL;DR

This paper introduces a hypergraph-based statistical model for population stratification, effectively capturing complex associations among elements and identifying minimal stratifications that retain most population structure information.

Contribution

It presents a novel hypergraph modeling approach and the concept of stratification representativeness for analyzing population structures.

Findings

Successfully stratified animal and human populations using phenotypic and genotypic data.

Demonstrated the model's ability to identify minimal yet informative stratifications.

Showed the framework's effectiveness in capturing complex population associations.

Abstract

Population stratification is a problem encountered in several areas of biology and public health. We tackle this problem by mapping a population and its elements attributes into a hypergraph, a natural extension of the concept of graph or network to encode associations among any number of elements. On this hypergraph, we construct a statistical model reflecting our intuition about how the elements attributes can emerge from a postulated population structure. Finally, we introduce the concept of stratification representativeness as a mean to identify the simplest stratification already containing most of the information about the population structure. We demonstrate the power of this framework stratifying an animal and a human population based on phenotypic and genotypic properties, respectively.