A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data

TL;DR

This paper introduces a unified statistical model that integrates genetic, phenotypic, and spatial data to improve the analysis of evolutionary units, demonstrating its effectiveness through simulations and real-world vole data.

Contribution

It presents a novel model that combines spatial, genetic, and phenotypic data within a single inference framework, enhancing analysis accuracy and comparability.

Findings

The method accurately estimates parameters in simulated data.

It outperforms alternative approaches in various scenarios.

Applied to vole data, it reveals complex differentiation patterns.

Abstract

Recognition of evolutionary units (species, populations) requires integrating several kinds of data such as genetic or phenotypic markers or spatial information, in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double original advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography; and (ii) it allows one to analyze genetic and phenotypic data within a unified model and inference framework, thus opening the way to robust comparisons between markers and possibly combined analyzes. We show from simulated data as well are real data from the literature that our method estimates parameters accurately and improves alternative approaches in many situations. The interest of this method is exemplified using an intricate case of inter- and intra-species differentiation based on an original data-set of georeferenced genetic and morphometric markers obtained on {\em Myodes} voles from Sweden. A computer program is made available as an extension of the R package Geneland.