# Inferring molecular evolutionary rates and divergence dates

> **NIH NIH R01** · TEMPLE UNIV OF THE COMMONWEALTH · 2020 · $317,000

## Abstract

Knowledge of evolutionary rates and dates is essential for answering fundamental questions in biology and
medicine, including the antiquity of gene duplications, origins of pathogenic strains, and relative tempo of
changes in genes and convergence in species. Despite decades of methodological advances, researchers face
substantial challenges when conducting these analyses. Therefore, we focus on developing a new relative rate
framework (RRF) that will advance beyond the current state-of-the-art to address conceptual and practical
challenges in estimating evolutionary rates and dates. Using RRF we will develop much-needed methods to test
hypotheses of evolutionary rate independence among lineages and to select the statistical distribution that best
fits the given data. No reliable methods currently exist for either of these two purposes, which compels
practitioners to make arbitrary and ad hoc choices, resulting in biases in temporal trends inferred and powerless
tests of evolutionary hypotheses. We will use our newly developed methods to query empirical datasets
regarding fundamental questions of rates and dates, including the hypothesized existence and prevalence of
evolutionary rate correlation in closely- and distantly-related species. The statistical development of RRF will
produce reliable estimates of node dates to establish robust biological patterns, and generate robust 95%
confidence intervals to test hypotheses. RRF framework will be computationally efficient and scalable, with
accuracy surpassing computationally-intensive methods whose usage currently requires ad hoc divide-and-
conquer or data subsampling approaches when applied to larger data sets. We will also create a library of
functions containing the advanced methods developed in this project, which will be directly useable on the
command line and available in a graphical interface through integration with the MEGA software.

## Key facts

- **NIH application ID:** 9857611
- **Project number:** 5R01GM126567-03
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Sudhir Kumar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $317,000
- **Award type:** 5
- **Project period:** 2018-02-01 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9857611

## Citation

> US National Institutes of Health, RePORTER application 9857611, Inferring molecular evolutionary rates and divergence dates (5R01GM126567-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9857611. Licensed CC0.

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