# Characterizing modes of natural selection via diverse ancient and modern samples

> **NIH NIH R35** · BROWN UNIVERSITY · 2021 · $379,863

## Abstract

Project Summary
 
With the advent of new sequencing technologies, we now have generated more data
than ever before. These data have greatly improved our knowledge of human history,
human adaptation to different environments and human disease. Genome-wide studies
have highlighted many genes or genomic loci that may play a role in adaptive or
disease related phenotypes of biological importance. Now that we have access to
thousands of human genomes from a diverse set of populations around the globe, we
can zoom in at the local scale (e.g. within a gene) and leverage information from
multiple populations to understand the observed patterns of genetic variation, without
the ascertainment bias associated with the older array-based technologies. In addition,
thanks to advances in DNA extraction and library preparation, we now are beginning to
have access to DNA sequence data from ancient human samples.
We propose to leverage these collections of modern and ancient sequence data to
address some key questions in the field, and we have identified opportunities for
methods development and biological discovery. The common theme in the proposal is
to understand the different modes of natural selection in human populations. We plan to
develop methods to detect shared selective events across populations by means of
extending current statistical summaries, and methods for detecting admixture-facilitated
adaptation which we believe is likely a common mode of natural selection based on our
earlier published work. We will apply these tools to new datasets to characterize the
interplay of natural selection, archaic and modern admixture in populations in the
Americas and make a comparative analysis of modern and ancient European samples
to understand the changing profile of medically important risk alleles for disease. As a
result our work will reveal evolutionary processes that have played an important role in
human evolution and disease.

## Key facts

- **NIH application ID:** 10241443
- **Project number:** 5R35GM128946-04
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Emilia Huerta-Sanchez
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $379,863
- **Award type:** 5
- **Project period:** 2018-09-19 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241443

## Citation

> US National Institutes of Health, RePORTER application 10241443, Characterizing modes of natural selection via diverse ancient and modern samples (5R35GM128946-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241443. Licensed CC0.

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