# Large-scale phylodynamics under non-neutral and non-treelike models of evolution

> **NIH NIH R35** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $373,871

## Abstract

Project Summary
Technological breakthroughs such as next-generation sequencing have recently led to the creation of immense
“BioBanks” featuring genomic information collected from hundreds of thousands of people, and the ongoing
pandemic has resulted in an even more extreme repository containing over 10 million SARS-CoV-2 genomes.
Unfortunately, existing techniques for inferring evolutionary models can, in most cases, only analyze a tiny
fraction of the information contained in these datasets. At a time when we should be able to use vast quantities
of data to answer increasingly nuanced evolutionary questions, lack of adequate methods has limited our
opportunities for discovery and hampered our ability to respond to the ongoing pandemic.
The proposed research addresses this problem through the creation of novel statistical and computational
methods designed to study targeted evolutionary hypotheses using BioBank- and pandemic-scale datasets.
First, we will develop new phylodynamic methods for epidemiological inference using tens of thousands of
sampled pathogen genomes. Apart from being more scalable, these methods will innovate over previous work
by being more biologically realistic and making fewer simplifying assumptions about the data. In particular, we
will study systems where multiple strains co-circulate and have differential fitness, and we will use this model to
improve our understanding of the role that natural selection has played in shaping the pandemic. We will further
extend this method to integrate non-genetic sources of information such as case count data, which will enable
public health researchers to partition case counts into different variants and estimate variant-specific effective
reproduction numbers. Second, we will develop improved methods for inferring phylogenetic networks, and use
them to understand the role that recombination has played in the evolution of the coronavirus, as well as its role
in confounding earlier studies that incorrectly assumed that SARS-CoV-2 evolution could be represented by a
single tree. All of these advances will be implemented and released as easy to use open source software
packages.
In summary, this work represents advances in several areas of statistical genetics including phylodynamic
modeling, genetic epidemiology, inference of natural selection and phylogenetic network analysis, and will
provide empirical researchers with modern tools needed to propel the next generation of discoveries in these
fields.

## Key facts

- **NIH application ID:** 10892687
- **Project number:** 5R35GM151145-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jonathan G Terhorst
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $373,871
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10892687

## Citation

> US National Institutes of Health, RePORTER application 10892687, Large-scale phylodynamics under non-neutral and non-treelike models of evolution (5R35GM151145-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10892687. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*