# Incorporating geography into statistical methods for analysis of population genomic DNA

> **NIH NIH R35** · MICHIGAN STATE UNIVERSITY · 2021 · $376,311

## Abstract

Project Summary
In humans, genetic variation is distributed geographically, reflecting the history of human movements across
the continents. Understanding these spatial patterns is crucial for many fields in human population genomics,
including the study of human evolutionary history and linking genotypes and phenotypes. Historically, limi-
tations in the size and scope of empirical datasets have allowed researchers to employ models that ignore
geography, but modern genomic datasets demand population genetic methods that incorporate geographic
space. The proposed research will generate novel statistical methods that incorporate geography into the
study of population genetic structure, admixture, demography, and natural selection. These methods will be
developed and implemented as open-source software, validated using state-of-the-art forward-time simula-
tions, and applied to publicly available human genomic datasets.
We will develop tests for population admixture that explicitly account for geographic patterns due to isolation
by distance. These tests will be used to analyze densely sampled Eurasian human genomic datasets to
identify admixed samples, and will also be applied in sliding windows along the genome to highlight genomic
regions that may have been transferred between populations via adaptive introgression. We will also develop
a spatiotemporal population clustering method that can jointly analyze ancient and modern samples. Neutral
genetic processes are expected to generate population differentiation between samples separated in space or
time, so this clustering method will account for both when determining whether two samples share ancestry in
the same discrete population. This method will be extended to detect selection on polygenic traits by testing for
an aggregate increase in the frequency of alleles involved in a particular trait relative to the neutral expectation.
We will apply this method to test for selection through time on human height across Eurasia. Finally, we will
model the lengths of shared genomic segments between individuals, which are informative about genealogical
overlap at different points in the past, to learn about how population density and dispersal patterns have
changed across geographic space through time.
The proposed work represents advances in a number of fields in statistical population genetics, including the
detection of population admixture, adaptive introgression, population replacement and the joint analysis of
DNA from ancient and modern samples, detecting selection on polygenic traits, and modeling heterogeneity
in demographic processes through time. Taken together, this work will offer empirical researchers a valuable
toolkit for the analysis of modern genomic datasets, which require spatially explicit methods, and will shed light
on both human evolutionary history and the mechanisms by which humans have adapted to their environment
across space and time.

## Key facts

- **NIH application ID:** 10200099
- **Project number:** 5R35GM137919-02
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Gideon Bradburd
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $376,311
- **Award type:** 5
- **Project period:** 2020-07-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200099

## Citation

> US National Institutes of Health, RePORTER application 10200099, Incorporating geography into statistical methods for analysis of population genomic DNA (5R35GM137919-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10200099. Licensed CC0.

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