An estimator for the recombination rate from a continuously observed diffusion of haplotype frequencies

TL;DR

This paper derives a maximum likelihood estimator for recombination rate from a continuously observed haplotype frequency diffusion, revealing unique properties and robustness to selection, with implications for understanding evolutionary dynamics.

Contribution

It introduces a novel estimator for recombination rate based on continuous observation of haplotype frequencies, providing theoretical insights and simulation-based analysis.

Findings

Estimator's information matrix can explode in finite time.

Estimator is robust to the presence of selection.

Distribution sensitive to mutation rates.

Abstract

Recombination is a fundamental evolutionary force, but it is difficult to quantify because the effect of a recombination event on patterns of variation in a sample of genetic data can be hard to discern. Estimators for the recombination rate, which are usually based on the idea of integrating over the unobserved possible evolutionary histories of a sample, can therefore be noisy. Here we consider a related question: how would an estimator behave if the evolutionary history actually was observed? This would offer an upper bound on the performance of estimators used in practice. In this paper we derive an expression for the maximum likelihood estimator for the recombination rate based on a continuously observed, multi-locus, Wright--Fisher diffusion of haplotype frequencies, complementing existing work for an estimator of selection. We show that, contrary to selection, the estimator has unusual properties because the observed information matrix can explode in finite time whereupon the recombination parameter is learned without error. We also show that the recombination estimator is robust to the presence of selection in the sense that incorporating selection into the model leaves the estimator unchanged. We study the properties of the estimator by simulation and show that its distribution can be quite sensitive to the underlying mutation rates.