# Identifying complex modes of adaptation from population-genomic data

> **NIH NIH R35** · FLORIDA ATLANTIC UNIVERSITY · 2020 · $341,823

## Abstract

Project Summary
Low-cost DNA sequencing has provided researchers with abundant genomic data in which to search for the
unique footprints left by natural selection. However, a number of non-adaptive forces can obscure these signals,
making it important to develop statistical methods that can account for multiple factors that influence genetic
variation. My research in this area has focused on the design and application of statistical approaches for
identifying regions undergoing balancing selection, which maintains the frequency of alleles in a population, and
positive selection, which increases the frequency of beneficial alleles in a population. Specifically, we contributed
to a number of advances in this area, including developing the first model-based methods for detecting balancing
selection, the first likelihood approach for identifying positive selection while accounting for the confounding
effects of negative selection, the first likelihood method for detecting adaptive introgression within a single
population, and a computationally-efficient statistic tailored for identifying signals of ancestral positive selection.
Our applications of these and other methods to human genomic data have uncovered novel candidates for high-
altitude adaptation in Ethiopians and adaptation to European-borne pathogens in Native Americans, as well as
for balancing selection via segregation distortion. During the next five years, I propose to develop novel statistical
methods that leverage information about how different evolutionary forces shape the spatial distribution of
genetic diversity around adaptive sites to identify genomic targets affected by complex modes of natural selection.
These methods will be applied to whole-genome sequencing data from primates to answer questions about the
role of adaptation in ancient and recent evolutionary history. In particular, our future research will be subdivided
into several interrelated goals: designing statistical techniques for identifying positive selection in admixed
populations, and using these techniques to identify genomic regions undergoing positive selection in admixed
human populations; developing methods for identifying regions that underwent complex ancient balancing
selection, and applying these methods to multiple primate species to investigate the prevalence of ancient
balancing selection in this lineage; constructing statistics for uncovering adaptive footprints that integrate data
from ancient and modern samples, and using these statistics to understand past adaptive history in European
human populations; and building novel functional data analysis procedures for classifying modes of selection
acting across the genome, and using these procedures to better understand the relative roles of hard sweeps,
soft sweeps, adaptive introgression, and recent and ancient balancing selection in human evolutionary history.
Advantages of these studies are two-fold, in that they will both yield powerful new approac...

## Key facts

- **NIH application ID:** 9975871
- **Project number:** 5R35GM128590-03
- **Recipient organization:** FLORIDA ATLANTIC UNIVERSITY
- **Principal Investigator:** Michael DeGiorgio
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $341,823
- **Award type:** 5
- **Project period:** 2019-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975871

## Citation

> US National Institutes of Health, RePORTER application 9975871, Identifying complex modes of adaptation from population-genomic data (5R35GM128590-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9975871. Licensed CC0.

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