ArticleGenetics2016
Bayesian Inference of the Evolution of a Phenotype Distribution on a Phylogenetic Tree.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
- A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.Nature reviews. Cancer · 2026Review
- AcceleratingProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
- Amylase-associated genetic pattern inApplied and environmental microbiology · 2024Article
- Analysis ofMicrobial genomics · 2023Article
- Distribution and origins ofMicrobial genomics · 2023Article
- Repeated evolution of similar phenotypes: Integrating comparative methods with developmental pathways.Genetics and molecular biology · 2023Article
- Phylogenetic Analysis of Bacterial Pathogen Genomes.Methods in molecular biology (Clifton, N.J.) · 2023Article
- A scalable analytical approach from bacterial genomes to epidemiology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Bayesian Inference of Clonal Expansions in a Dated Phylogeny.Systematic biology · 2022Article
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- Viral genome wide association study identifies novel hepatitis C virus polymorphisms associated with sofosbuvir treatment failure.Nature communications · 2021Article
- Machine learning to predict the source of campylobacteriosis using whole genome data.PLoS genetics · 2021Article
- Global prevalence and phylogeny of hepatitis B virus (HBV) drug and vaccine resistance mutations.Journal of viral hepatitis · 2021Article
- An Unbiased Molecular Approach Using 3'-UTRs Resolves the Avian Family-Level Tree of Life.Molecular biology and evolution · 2021Article
- Phylogenetic Methods for Genome-Wide Association Studies in Bacteria.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- Testing for dependence on tree structures.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- The impact of antimicrobials on gonococcal evolution.Nature microbiology · 2019Article
- Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realization from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is, however, often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated data sets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications.
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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.