Biology subjects

Jenkins, P. A.

Publications and source records attributed to Jenkins, P. A..

4 recordsLinked to original sources

bioRxiv2025

EWF 2.0: Exact sampling from the Wright-Fisher diffusion with time-varying demography

Accurate modelling of allele frequency trajectories requires incorporation of both genetic mechanisms such as selection and mutation, as well as realistic population demography. Accounting for a non-constant demography within a Wright-Fisher diffusion framework induces a time-inhomogenous drift coefficient, a regime falling outside the scope of existing exact simulation routines. To address this gap, we introduce EWF 2.0, an exact simulation algorithm that accommodates time-varying demography within Wright-Fisher diffusions whilst retaining all the functionality of previous EWF versions. We validate correctness using distributional tests (Kolmogorov-Smirnov, QQ plots), confirming agreement with theoretical expectations. In spite of its greater generality, EWF 2.0 retains the same runtime as in previous versions, ensuring computational efficiency and scalability. All software is available at https://github.com/JaroSant/EWF. EWF 2.0 is particularly valuable for bridge simulation, where existing methods cannot handle time-varying mutation and selection rates. For a specified demographic history, mutation parameters, selection function and sampling times, EWF 2.0 generates exact draws from the law of the corresponding Wright-Fisher diffusion or diffusion bridge.

genetics↗

bioRxiv2021

Evidence of ongoing recombination in SARS-CoV-2 through genealogical reconstruction

AO_SCPLOWBSTRACTC_SCPLOWThe evolutionary process of genetic recombination has the potential to rapidly change the properties of a viral pathogen, and its presence is a crucial factor to consider in the development of treatments and vaccines. It can also significantly affect the results of phylogenetic analyses and the inference of evolutionary rates. The detection of recombination from samples of sequencing data is a very challenging problem, and is further complicated for SARS-CoV-2 by its relatively slow accumulation of genetic diversity. The extent to which recombination is ongoing for SARS-CoV-2 is not yet resolved. To address this, we use a parsimony-based method to reconstruct possible genealogical histories for samples of SARS-CoV-2 sequences, which enables us to pinpoint specific recombination events that could have generated the data. We propose a statistical framework for disentangling the effects of recurrent mutation from recombination in the history of a sample, and hence provide a way of estimating the probability that ongoing recombination is present. We apply this to samples of sequencing data collected in England and South Africa, and find evidence of ongoing recombination.

evolutionary biology↗

bioRxiv2020

KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation

AO_SCPLOWBSTRACTC_SCPLOWThe reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of cost parameters. We demonstrate that the algorithm performs well when compared against existing methods. The software is made available on GitHub.

evolutionary biology↗

bioRxiv2018

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our frame-work can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.

evolutionary biology↗