The method builds on earlier work on inferring transmission rates and genetic drift from Covid time series, where much larger sample sizes were needed.
www.pnas.org/doi/abs/10.1...

There are many open questions, but it’s remarkable what time-series data from recombining genomes can reveal. Here: Each panel shows, for one focal region, the fraction of ancestry imported from other regions in 300-year bins; titles = recipient region, colors = source regions.

Allele-frequency time series as a window into gene flow and transmission in COVID / influenza is now out in PNAS

www.pnas.org/doi/10.1073/...

Huge thanks to three reviewers altruistically improving our paper!

Below is the thread from the preprint.

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Frequency-dependent fitness effects are ubiquitous In simple microbial populations, the fitness effects of most selected mutations are generally taken to be constant, independent of genotype frequency. This assumption underpins predictions about evolutionary dynamics, epistatic interactions, and the maintenance of genetic diversity in populations. Here, we systematically test this assumption using beneficial mutations from early generations of the Escherichia coli Long-Term Evolution Experiment (LTEE). Using flow cytometry-based competition assays, we find that frequency-dependent fitness effects are the norm rather than the exception, occurring in approximately 80\% of strain pairs tested. Most competitions exhibit negative frequency-dependence, where fitness advantages decline as mutant frequency increases. Furthermore, we demonstrate that the strength of frequency-dependence is predictable from invasion fitness measurements, with invasion fitness explaining approximately half of the biological variation in frequency-dependent slopes. Additionally, we observe violations of fitness transitivity in several strain combinations, indicating that competitive relationships cannot always be predicted from fitness relative to a single reference strain alone. Through high-resolution measurements of within-growth cycle dynamics, we show that simple resource competition explains a substantial portion of the frequency-dependence: when faster-growing genotypes dominate populations, they deplete shared resources more rapidly, reducing the time available for fitness differences to accumulate. Our results demonstrate that even in a simple model system designed to minimize ecological complexity, subtle ecological interactions between closely related genotypes create frequency-dependent selection that can fundamentally alter evolutionary dynamics. ### Competing Interest Statement The authors have declared no competing interest.

How common are frequency dependent fitness effects?

New preprint out today 👇
doi.org/10.1101/2025...

Uncovering heterogeneous inter-community disease transmission from neutral allele frequency time series The COVID-19 pandemic has underscored the critical need for accurate epidemic forecasting to predict pathogen spread and evolution, to anticipate healthcare challenges, and to evaluate intervention st...

This work owes its existence to the incredible Takashi Okada, with support from
@qinqinyu.bsky.social, @giulioisac.bsky.social, and invaluable input from friends and experts. Read more: www.medrxiv.org/content/10.1...

With NIH (esp. NIAID) funding under threat, this work underscores the importance of genomic epidemiology for global health. Supporting such analyses is vital—any suggestions on alternative funding sources?

Why is this important? Knowing these networks can enhance epidemic forecasting, inform targeted interventions like vaccination campaigns, explain why some regions contribute more to pathogen evolution.

Applied to SARS-CoV-2 data from 🇬🇧England & 🇺🇸the USA, our method revealed: Networks mirror geography / Long-range interactions have greater impact than expected based on mobility data alone / Importation networks shift across variant waves

The massive rise in genome surveillance during the pandemic, allowed lead author Takashi Okada to infer entire importation networks, using an HMM to filter out genetic drift and sampling noise.

Neutral allele frequency time series can tell. Consider two communities (A & B) under transient travel restrictions: Allele frequencies X_A(t), X_B(t) drift independently during isolation but converge post-lockdown - the convergence rate precisely measures the importation rate.

The pandemic showed us that disease doesn’t respect boundaries. But how do we map hidden transmission pathways, especially the crucial rare ones between distant communities? 🌍

After a long and winding odyssey, excited to finally drop anchor in open-access waters. This preprint shows how neutral allele frequency time series can illuminate disease transmission rates between communities— key for epidemic fore- & backcasting. medrxiv.org/content/10.1... 🧵