Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.02.11.579856

How to validate a Bayesian evolutionary model

Mendes, F. K.·Bouckaert, R.·Carvalho, L. M.·Drummond, A. J.
View original source ↗Export metadata ↓

Abstract

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe and illustrate good practices for assessing the correctness of a model implementation, with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

Source connections

Explore related subjects

evolutionary biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mendes, F. K., Bouckaert, R., Carvalho, L. M., Drummond, A. J.. 2024-02-12. How to validate a Bayesian evolutionary model. https://doi.org/10.1101/2024.02.11.579856

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Geometry of antigenic evolution improves influenza vaccine selection

Anticipating antigenic evolution is essential for selecting effective seasonal influenza A/H3N2 vaccine strains. To this end, we integrated hemagglutination-inhibition and neutralization titers spanning 2002 to 2025 into a unified Bayesian antigenic map. The map resolves twelve antigenic clusters advancing in discrete steps, with several clusters co-circulating in most seasons. In 15 of 21 seasons, the WHO-recommended vaccine belonged to an earlier cluster than the dominant circulating cluster. The direction of each vaccine update relative to recent viral drift predicted vaccine effectiveness one season ahead in out-of-sample forecasts. Antigenic distance, the conventional measure of vaccine-virus match, was weakly associated with effectiveness until update direction was accounted for. Retrospectively ranking candidate strains by predicted effectiveness would have selected a strain predicted to outperform the WHO recommendation in every season, raising mean predicted effectiveness by 10 percentage points.

evolutionary biology↗
bioRxiv2026

Evolutionary replay of duplicate-gene retention across independent whole-genome duplications

Whole-genome duplications repeatedly expose ancestral gene lineages to the same broad evolutionary outcome-retention or loss of duplicated copies-but it remains unclear whether this history replays similarly across evolutionary scales. We placed duplicate retention in shared hierarchical orthologous-group coordinates and compared percentile ranks defined within each event-wide mapped universe. Three independent angiosperm whole-genome duplications showed reproducible replay (global rank effect T-replay = 0.210, bootstrap 95% confidence interval 0.172-0.248; permutation P = 1/100,001). A plant reference-panel score specified before target outcomes were examined predicted retention after the Apple/Pear duplication ({rho} = 0.169, n = 373). Deep transfer was heterogeneous: the teleost-genome-duplication estimate was positive but unresolved ({rho} = 0.107, n = 151, 95% confidence interval -0.050 to 0.260), whereas transfer to the ancient budding-yeast whole-genome duplication (yeast WGD) was supported ({rho} = 0.280, n = 186). Independently reconstructed animal outcomes also replayed between teleost and Stylommatophora duplications (r = 0.226, n = 146, P = 0.00326), although the effect remained below a prespecified strong-effect threshold. A strict plant-animal comparison was limited to 25 deeply one-to-one lineages and was unresolved (r = 0.033, 95% confidence interval -0.303 to 0.340). Thus, ancestral gene-lineage identity contributes reproducibly to duplicate retention after independent whole-genome duplications, but replay is structured by evolutionary lineage and modified by event-specific history rather than governed by one universal gene-fate ranking.

evolutionary biology↗
bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗