# Novel Coalescent Approaches for Studying Evolutionary Processes

> **NIH NIH R35** · STANFORD UNIVERSITY · 2023 · $386,750

## Abstract

Project Summary/Abstract:
My laboratory research program in stochastic modeling and inference of evolutionary processes focuses on
developing efficient methods for inference of evolutionary parameters from molecular data, and statistical
tests for assessing evolutionary hypotheses. This proposal will focus on answering three fundamental
questions in the study of evolutionary processes: Are the observed patters of genetic diversity the result of
adaptive or non-adaptive evolution? What is the mode and strength of selection? How can we identify
genomic regions undergoing selection? Whether adaptation, demography or local patterns of mutations are
the sources of variation across populations, these forces influence the shape of the underlying genealogies
and phylogenetic networks. Hence, assessing differences among genealogies provide information about
differences in these forces, particularly among genealogies of different individuals, possibly living in
different environments and times. We propose to approach these questions by defining new coalescent
models of selection and exploiting a metric on the space of genealogies to define statistical tests. The
computational advantage and the ease of biological interpretation, together with the mathematical properties
of the proposed models and metric spaces, open the door to novel approaches for studying adaptation. Over
the next five years, the Palacios laboratory will combine tools from combinatorial optimization, Bayesian
inference, and coalescent theory to develop new coalescent models and tests applicable to studying the
evolution of pathogens and other organisms.

## Key facts

- **NIH application ID:** 10552480
- **Project number:** 1R35GM148338-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Julia Palacios
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $386,750
- **Award type:** 1
- **Project period:** 2023-05-17 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10552480

## Citation

> US National Institutes of Health, RePORTER application 10552480, Novel Coalescent Approaches for Studying Evolutionary Processes (1R35GM148338-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10552480. Licensed CC0.

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