# Theory, Methods, and Resources for Understanding and Leveraging Spatial Variation in Population Genetic Data

> **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2023 · $401,904

## Abstract

PROJECT SUMMARY
 Across many species, the evolutionary processes that underlie genetic variation are structured by the
geographical distribution of individuals and how geography impacts patterns of reproduction and dispersal.
In human genetics, geographic patterning in allele frequencies has important practical consequences for
genome-wide association studies, as it can produce a type of confounding with other spatially varying factors
impacting traits. It also has relevance for the discovery of rare loss-of-function variant carriers. In infectious
disease, the arrival and spread of novel adaptive variants is mediated by geographic dispersal patterns.
 In this project, we will develop theory, methods, and resources that incorporate an explicitly geographic
component. In the first research area, we will develop new theoretical models for investigating the impacts
of varying spatial sampling strategies on the detection of deleterious alleles, such as loss-of-function alleles.
We will also study the spread of advantageous alleles in populations with super-spreaders and long-distance
dispersal, as well as graph-based dispersal dynamics, in a series of analyses that is relevant for
understanding the spread of adaptive variants in human-dispersed pathogens. In the second research area,
we will expand a set of methods for understanding spatial structure in genetic data. Specifically, we hope to
build models that more accurately capture directional and long-distance migration. In the third research area,
we will continue to maintain and develop resources for visualizing geographic distributions in allele
frequencies. These three research areas are synergistic and ideally will help advance our understanding of
genetic variation in numerous species, including humans.

## Key facts

- **NIH application ID:** 10623985
- **Project number:** 1R35GM149521-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** John Novembre
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $401,904
- **Award type:** 1
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10623985

## Citation

> US National Institutes of Health, RePORTER application 10623985, Theory, Methods, and Resources for Understanding and Leveraging Spatial Variation in Population Genetic Data (1R35GM149521-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10623985. Licensed CC0.

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