The estimation of demographic parameters from population genetic data has been the subject of many scientific studies [1]. Among these efforts, legofit was firstly proposed in 2019 as a tool to infer size changes, subdivision and gene flow events from patterns of nucleotidic variation [2]. The first release of legofit used a stochastic algorithm to fit population parameters to the observed data. As it requires simulations to evaluate the fitting of each model, it is computationally intensive and can only be deployed on high-performance computing clusters.

To overcome this issue, Rogers proposes a new implementation of legofit based on a deterministic algorithm that allows the estimation of demographic histories to be computationally faster and more accurate [3]. The new algorithm employs a continuous-time Markov chain that traces the ancestry of each sample into the past. The calculations are now divided into two steps, the first one being solved numerically. To test the hypothesis that the new implementation of legofit produces a more desirable performance, Rogers generated extensive simulations of genomes from African, European, Neanderthal and Denisovan populations with msprime [4]. Additionally, legofit was tested on real genetic data from samples of said populations, following a previously published study [5].

Based on simulations, the new deterministic algorithm is more than 1600 times faster than the previous stochastic model. Notably, the new version of legofit produces smaller residual errors, although the overall accuracy to estimate population parameters is comparable to the one obtained using the stochastic algorithm. When applied to real data, the new implementation of legofit was able to recapitulate previous findings of a complex demographic model with early gene flow from humans to Neanderthal [5]. Notably, the new implementation generates better discrimination between models, therefore leading to a better precision at predicting the population history. Some parameters estimated from real data point towards unrealistic scenarios, suggesting that the initial model could be misspecified.

Further research is needed to fully explore the parameter range that can be evaluated by legofit, and to clarify the source of any associated bias. Additionally, the inclusion of data uncertainty in parameter estimation and model selection may be required to apply legofit to low-coverage high-throughput sequencing data [6]. Nevertheless, legofit is an efficient, accessible and user-friendly software to infer demographic parameters from genetic data and can be widely applied to test hypotheses in evolutionary biology. The new implementation of legofit software is freely available at https://github.com/alanrogers/legofit. 

References

[1] Spence JP, Steinrücken M, Terhorst J, Song YS (2018) Inference of population history using coalescent HMMs: review and outlook. Current Opinion in Genetics & Development, 53, 70–76. https://doi.org/10.1016/j.gde.2018.07.002

[2] Rogers AR (2019) Legofit: estimating population history from genetic data. BMC Bioinformatics, 20, 526. https://doi.org/10.1186/s12859-019-3154-1

[3] Rogers AR (2021) An Efficient Algorithm for Estimating Population History from Genetic Data. bioRxiv, 2021.01.23.427922, ver. 5 peer-reviewed and recommended by Peer community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.01.23.427922

[4] Kelleher J, Etheridge AM, McVean G (2016) Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes. PLOS Computational Biology, 12, e1004842. https://doi.org/10.1371/journal.pcbi.1004842

[5] Rogers AR, Harris NS, Achenbach AA (2020) Neanderthal-Denisovan ancestors interbred with a distantly related hominin. Science Advances, 6, eaay5483. https://doi.org/10.1126/sciadv.aay5483

[6] Soraggi S, Wiuf C, Albrechtsen A (2018) Powerful Inference with the D-Statistic on Low-Coverage Whole-Genome Data. G3 Genes|Genomes|Genetics, 8, 551–566. https://doi.org/10.1534/g3.117.300192

This study [1] used Bayesian models of the number of deaths through time across different regions of France to explore the effects of lockdown and other events (i.e., holding elections) on the dynamics of the SARS-CoV-2 epidemic. The models accurately predicted the number of deaths 2 to 3 weeks in advance, and results were similar to other recent models using different structure and input data. Viral reproduction numbers were not found to be different between weekends and week days, and there was no evidence that holding elections affected the number of deaths directly. However, exploring different scenarios of the timing of the lockdown showed that this had a substantial impact on the number of deaths. This is an interesting and important paper that can inform adaptive management strategies for controlling the spread of this virus, not just in France, but in other geographic areas. For example, the results found that there was a lag period between a change in management strategies (lockdown, social distancing, and the relaxing of controls) and the observed change in mortality. Also, there was a large variation in the impact of mitigation measures on the viral reproduction number depending on region, with lockdown being slightly more effective in denser regions. The authors provide an extensive amount of additional data and code as supplemental material, which increase the value of this contribution to the rapidly growing literature on SARS-CoV-2.

References

[1] Duchemin, L., Veber, P. and Boussau, B. (2020) Bayesian investigation of SARS-CoV-2-related mortality in France. medRxiv 2020.06.09.20126862, ver. 5 peer-reviewed and recommended by PCI Mathematical & Computational Biology. doi: 10.1101/2020.06.09.20126862