Models for Genetic Diversity Generated by Negative Binomial Point Processes

TL;DR

This paper introduces a flexible new model based on negative binomial point processes for analyzing genetic diversity, demonstrated on quoll microsatellite data, with implications for conservation efforts.

Contribution

It generalizes the Poisson-Dirichlet model using a negative binomial process, providing enhanced flexibility and a new sampling formula for genetic diversity analysis.

Findings

The new model fits quoll data better than standard models.

Parameter r improves interpretability of genetic diversity.

Generalized Ewens' sampling formula derived.

Abstract

We develop a model based on a generalised Poisson-Dirichlet distribution for the analysis of genetic diversity, and illustrate its use on microsatellite data for the genus Dasyurus (the quoll, a marsupial carnivore listed as near-threatened in Australia). Our class of distributions, termed $PD_\alpha^{(r)}$, is constructed from a negative binomial point process, generalizing the usual one-parameter $PD_\alpha$ model, which is constructed from a Poisson point process. Both models have at their heart a Stable$(\alpha)$ process, but in $PD_\alpha^{(r)}$, an extra parameter $r>0$ adds flexibility, analogous to the way the negative binomial distribution allows for "overdispersion" in the analysis of count data. A key result obtained is a generalised version of Ewens' sampling formula for $PD_\alpha^{(r)}$. We outline the theoretical basis for the model, and, for the quolls data, estimate the parameters $\alpha$ and r by least squares, showing how the extra parameter r aids in the interpretability of the data by comparison with the standard $PD_\alpha$ model. The methods potentially have implications for the management and conservation of threatened populations.