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

NIH RePORTER · NIH · R35 · $401,904 · view on reporter.nih.gov ↗

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
UNIVERSITY OF CHICAGO
Principal Investigator
John Novembre
Activity code
R35
Funding institute
NIH
Fiscal year
2023
Award amount
$401,904
Award type
1
Project period
2023-06-01 → 2028-05-31