Forward-backward algorithms with a biallelic mutation-drift model: Orthogonal polynomials, and a coalescent/urn-model based approach

TL;DR

This paper introduces a novel approach using orthogonal polynomials within forward-backward algorithms to efficiently infer ancestral allele configurations and frequencies under a biallelic mutation-drift model, connecting coalescent and diffusion models.

Contribution

It develops a new method employing orthogonal polynomials for forward-backward algorithms to analyze allele configurations and frequencies in population genetics.

Findings

Efficient calculation of ancestral allele configurations using orthogonal polynomials.

Demonstrated equivalence of marginal likelihood calculations across models.

Full description of genealogy via backward polynomial expansion.

Abstract

Inference of the marginal likelihood of sample allele configurations using backward algorithms yields identical results with the Kingman coalescent, the Moran model, and the diffusion model (up to a scaling of time). For inference of probabilities of ancestral population allele frequencies at any given point in the past - either of discrete ancestral allele configurations as in the coalescent, or of ancestral allele proportions as in the backward diffusion - backward approaches need to be combined with corresponding forward ones. This is done in so-called forward-backward algorithms. In this article, we utilize orthogonal polynomials in forward-backward algorithms. They enable efficient calculation of past allele configurations of an extant sample and probabilities of ancestral population allele frequencies in equilibrium and in non-equilibrium. We show that the genealogy of a sample is fully described by the backward polynomial expansion of the marginal likelihood of its allele configuration.