# A comparative population genomic approach for high-resolution inference of natural selection in fruit flies

> **NIH NIH F32** · STANFORD UNIVERSITY · 2021 · $66,390

## Abstract

Project Summary/Abstract
The number of sequenced organisms continues to grow exponentially, providing evolutionary biologists with an
unprecedented resolution for mapping natural selection in the genome. Comparative genomic methods identify
function by searching for genomic elements that are constrained by natural selection. Modern comparative
datasets are saturated with substitutions accumulated over many millions of years of evolution, allowing
functional elements to be identified at the resolution of a few base pairs. Since comparative methods rely on
sequence conservation as evidence of natural selection, substitutions caused by fluctuations in the strength of
selection or rare adaptive events can be mistaken for a lack of function. Population genomic data are robust to
these issues and are the best way to measure constraint in principle, but suffer from low per-site densities,
limiting the resolution at which function can be studied. Here, we propose a comparative population genomics
approach for addressing these limitations by combining polymorphism data across multiple species.
Specifically, we will create an unprecedented dataset of genome assemblies of and population polymorphism
data of up to 100 individuals from each of 100 species from the model system of fruit flies (family
Drosophilidae). In Aim 1, we will map selective constraint at the resolution of less than 3 base pairs and use
these maps to test how constraint evolves across a clade. In Aim 2, we will develop new a test for adaptive
evolution that jointly utilizes substitution and polymorphism data from multiple species and test whether the
same genes are utilized by adaptation in drosophilids. Successful completion of the project will contribute
significantly to the emerging field of comparative population genomics by providing an important publicly
available genomic dataset for the scientific community, new ways to design and analyze large sequencing
experiments, and test fundamental assumptions about the relationship between evolution in populations and
evolution over macro-evolutionary time scales.
 The primary goal of this NRSA F32 fellowship is to prepare me with the scientific and professional
foundation to become a leader in the new field of comparative population genomics as an independent
researcher. My long-term scientific goal is to lead an independent research group that utilizes comparative
genomics and population genetics tools to bridge micro and macroevolutionary processes. As a postdoctoral
fellow in the Petrov Lab at Stanford, I will receive new scientific training in wet lab skills, designing sequencing
experiments, and comparative genomics tools by generating and analyzing a genomic dataset that will be the
first of its kind. I will receive substantial training in professional leadership and network building by leading the
effort to build this large genomic resource for the scientific community.

## Key facts

- **NIH application ID:** 10229346
- **Project number:** 5F32GM135998-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Bernard Youngsoo Kim
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $66,390
- **Award type:** 5
- **Project period:** 2020-07-20 → 2022-07-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10229346

## Citation

> US National Institutes of Health, RePORTER application 10229346, A comparative population genomic approach for high-resolution inference of natural selection in fruit flies (5F32GM135998-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10229346. Licensed CC0.

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