ArticleJournal of the American Statistical Association2022
Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 4 citations in OpenAlex.
- Recent advances in the inference of deep viral evolutionary history.Journal of virology · 2025Review
- BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference.Nature methods · 2025Article
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Evolutionary shift detection with ensemble variable selection.BMC ecology and evolution · 2024Article
- Body size and life history shape the historical biogeography of tetrapods.Nature ecology & evolution · 2023Article
- A Novel Phylogenetic Negative Binomial Regression Model for Count-Dependent Variables.Biology · 2023Article
- Modeling the Evolution of Rates of Continuous Trait Evolution.Systematic biology · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
- Scalable Bayesian phylogenetics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis.Methods in ecology and evolution · 2022Article
- Relax, Keep Walking - A Practical Guide to Continuous Phylogeographic Inference with BEAST.Molecular biology and evolution · 2021Article
- Relaxed Random Walks at Scale.Systematic biology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
Abstract
Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or non-heritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.