# Population genetics of human evolutionary history and natural selection

> **NIH NIH R35** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $375,430

## Abstract

Project Summary
This proposed research addresses unresolved and fundamental questions in human evolutionary history and
the formation of genetic diversity. It contains three related research themes: (1) uncovering key features of
human population history and structure, both in the recent and ancient past, (2) determining factors influencing
selection across the genome and the limits to cross-population phenotypic prediction due to population history
and natural selection, and (3) modeling and predicting the genetic basis of complex traits under realistic
models of human evolutionary history, population structure and admixture. This work combines ambitious but
feasible methodological developments with new data from diverse worldwide populations.
Human evolutionary history is imprinted in the DNA of people living today. Learning this history involves the
combined efforts of theoretical, statistical, and empirical analyses. As the volume of genome sequencing data
grows, we have the opportunity to infer new details from key periods in our species’ evolution and clarify how
population biology and natural selection shape phenotypic and genetic variation. This requires developing
population genetic theory and computational methods that can handle data from many populations and large
sample sizes, and that simultaneously incorporate these many different factors that impact genetic diversity.
Over the next five years, my group will develop theoretical and methodological innovations that enable new
discoveries from human population genomic data, and we will uncover details of human history that resolve
important questions about our species’ evolution. This work builds on my lab’s expertise in population genetic
theory and simulation methods. Recent work shows that our newly developed approaches for modeling
diversity and linkage disequilibrium statistics are powerful to learn complex multi-population history. Combined
with newly sequenced high-quality genomic data, we will unravel early human evolution and reconstruct recent
admixture and demographic histories of Latin American populations. These models build genomic resources
for understudied populations and are essential to downstream studies to understand genetic components of
health and disease susceptibility. Our theoretical advances will enable new analyses of the combined effects of
selection and recombination in multiple populations, which we will use to understand fundamental limits to the
portability of phenotypic prediction across populations and in recently admixed groups. Finally, we will develop
approaches to predict the architecture of polygenic traits in structured and admixed populations, settings that
are most relevant for human genomic studies, but current methods fail to address. The mathematical and
statistical advances from this work will be developed into open source, maintained computational resources
that will facilitate genomic discoveries in our own research and that of others in...

## Key facts

- **NIH application ID:** 10937634
- **Project number:** 1R35GM154962-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Aaron Ragsdale
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $375,430
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10937634

## Citation

> US National Institutes of Health, RePORTER application 10937634, Population genetics of human evolutionary history and natural selection (1R35GM154962-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10937634. Licensed CC0.

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