Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Computational Population Genetics

Award Number: R35GM148253
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 02/07/2023
PERIOD OF PERFORMANCE END DATE: 12/31/2027

Group Awards By:

View Award Description

Computational Population Genetics - Project Summary The revolution in genome sequencing technologies over the past 15 years has created an explosion of population genomic data but has left in its wake a gap in our ability to make sense of data at this scale. This is in part because traditional population genetic models do not reflect the genomic and geographic processes producing the tremendously diverse data now being collected. To capitalize on this flood of information we need new methods and modes of analysis. In recent years our group has made great strides in using supervised machine learning for population genomic analysis (reviewed in part in Schrider and Kern 2018). In particular, we have pioneered the development and application of deep learning techniques for a wide variety of tasks including detecting selection (Kern and Schrider 2018, Xue et al., 2021), localizing introgression tracts in the genome (Schrider et al. 2018), characterizing the landscape of recombination (Adrion et al. 2020), predicting geographic origin (Battey et al. 2020), as well as visualizing population genetic data (Battey et al. 2021). A particular focus of our efforts during the last funding period has been understanding evolution of Anopheles gambiae in response to vector control efforts underway in sub-Saharan Africa, thus we have been and continue to develop statistical methods with these important data in mind. Our work on Anopheles has reinforced in us the importance of studying spatial variation, particular in the context of adaption. In this proposal we build upon ideas we have been developing during the previous funding period and discuss three facets of our ongoing research program. The proposal has three sections: 1) we will continue our work on spatial population genetics, and propose to develop methods for inferring dispersal parameters directly from population genomic data, as well as to improve our understanding of the ways in which spatial structure can impact GWAS and related techniques. 2) To develop methods to further characterize the population genomics of adaptation. In this section we are particularly interested in developing deep learning methods that account for the geographic spread of an allele relative to its surrounding pattern of genomic variation to discover beneficial alleles. In addition we will develop methods for discovering selection that build upon recent improvements in our ability to infer population-scale genealogies (Kelleher et al., 2019, Speidel et al., 2019). Finally, 3) we propose new avenues of development of a community resource project which our group has been leading, the stdpopsim project, which aims to provide an open source, highly reproducible and accessible method for doing population genetic simulation in a number of common study systems. We outline plans towards more realistic simulation of genomes under selection, with a particular focus on implementing previously published estimates of selective parameters. Moreover we will use the stdpopsim library to benchmark commonly used methods in demographic and selective inference


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $359,926 )
2026	2026	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	000	4	12/15/2025	NON-COMPETING CONTINUATION	$323,934
2026	2026	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	001	4	3/11/2026	NON-COMPETING CONTINUATION	$35,992
														Subtotal = $359,926

Issue Date FY: 2025 ( Subtotal = $359,926 )
2025	2025	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	001	3	12/9/2024	NON-COMPETING CONTINUATION	$323,934
2025	2025	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	002	3	8/13/2025	NON-COMPETING CONTINUATION	$35,992
2025	2024	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	000	2	11/11/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $359,926

Issue Date FY: 2024 ( Subtotal = $359,926 )
2024	2024	UNIVERSITY OF OREGON	1585 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	000	2	12/12/2023	NON-COMPETING CONTINUATION	$323,934
2024	2024	UNIVERSITY OF OREGON	1776 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	001	2	6/24/2024	NON-COMPETING CONTINUATION	$35,992
														Subtotal = $359,926

Issue Date FY: 2023 ( Subtotal = $439,926 )
2023	2023	UNIVERSITY OF OREGON	1585 E 13TH AVE	EUGENE	OR	97403	LANE	USA	Biomedical Research and Research Training	000	1	2/7/2023	NEW	$439,926
														Subtotal = $439,926

Grand Total All Awards = $1,519,704

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Computational Population Genetics

Award Number: R35GM148253

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 02/07/2023

PERIOD OF PERFORMANCE END DATE: 12/31/2027

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer