Wednesday, April 2, 2025 4/2/2025

Population genetic methods to detect population structure and adaptation using modern and ancient genomic datasets

Award Number: R01GM146051
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Population genetic methods to detect population structure and adaptation using modern and ancient genomic datasets - ABSTRACT Detecting adaptive genetic variation in population genomic datasets is important for understanding the genetic architecture underlying complex genetic diseases. Humans and other natural populations have been evolving under complex demographic histories, including divergence of ancestral populations, migration in structured populations, and past population size changes. Adaptive genetic variation and variation subject to complex demographic histories can result in similar observable genomic patterns, and distinguishing the evolutionary forces underlying genetic variation observed in natural population remains challenging. It is thus of importance to unravel the complex demographic histories underlying natural populations, and develop methods that detect adaptive genetic variation while properly accounting for these histories. In addition to contemporary genomic data, researchers have been gathering genetic data from ancient human remains in recent years. Including such datasets into the analyses has the potential to vastly improve our ability to detect population structure and genetic variation adapting to selective pressure. Thus, we will develop several tools for the analysis of contemporary and ancient genomic datasets to unravel the migration histories underlying the population expansion of humans and to detect adaptive genetic variation while accounting for these histories. To this end, we will develop a novel Coalescent Hidden Markov Model method to characterize complex migration histories. Our novel approach will use more efficient representations of local genealogies then previous approaches, which increases the accuracy of the inference and is more robust to noise in the data. Moreover, this framework will allow us to analyze population genomic data from large public databases to identify adaptive genetic variation. The local genealogies will be highly skewed in regions with adaptive genetic variation, as compared to genomic regions evolving under neutrality. The novel framework can be used to compute the posterior distribution of genealogical summaries at different locations in the genome to identify regions with skewed genealogies. In addition, we will implement approaches to detect adaptive genetic variation based on forward-in-time solutions of the dynamics of beneficial genetic variation and linked neutral regions. Based on a previously developed numerical approach, we will develop composite likelihood frameworks of observed genomic sequence variation under this model to detect adaptive genetic variation, while accounting for the underlying complex demographic history. Moreover, we will develop a method that aims at detecting polygenic adaptation from ancient DNA. This approach will be based on explicit likelihood models of the underlying allele frequency dynamics and allow us to detect and quantify directional and, unlike previous approaches, stabilizing selection on complex traits. Lastly, we will collaborate with colleagues to apply these methods and other appropriate tools to ancient DNA datasets to unravel the genetic response of medieval European populations to the Black Death pandemic.


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: 2025 ( Subtotal = $299,703 )
2025	2025	UNIVERSITY OF CHICAGO	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	000	4	2/20/2025	NON-COMPETING CONTINUATION	$299,703
														Subtotal = $299,703

Issue Date FY: 2024 ( Subtotal = $333,003 )
2024	2024	UNIVERSITY OF CHICAGO	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	001	3	7/23/2024	NON-COMPETING CONTINUATION	$33,300
2024	2024	UNIVERSITY OF CHICAGO	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	000	3	2/23/2024	NON-COMPETING CONTINUATION	$299,703
														Subtotal = $333,003

Issue Date FY: 2023 ( Subtotal = $333,003 )
2023	2023	UNIVERSITY OF CHICAGO	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	000	2	2/27/2023	NON-COMPETING CONTINUATION	$333,003
														Subtotal = $333,003

Issue Date FY: 2022 ( Subtotal = $333,003 )
2022	2022	UNIVERSITY OF CHICAGO, THE	5801 S ELLIS AVE	CHICAGO	IL	60637	COOK	USA	Biomedical Research and Research Training	000	1	4/7/2022	NEW	$333,003
														Subtotal = $333,003

Grand Total All Awards = $1,298,712

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Population genetic methods to detect population structure and adaptation using modern and ancient genomic datasets

Award Number: R01GM146051

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

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