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High resolution models for understanding admixture, adaptation and disease in humans through the lens of time series genomic data

Award Number: R35GM162367
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: 03/16/2025
PERIOD OF PERFORMANCE END DATE: 02/28/2031

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High resolution models for understanding admixture, adaptation and disease in humans through the lens of time series genomic data - PROJECT SUMMARY Admixture, natural selection, and changes in life history are fundamental biological processes that shape genetic variation, drive the evolution of complex traits, and influence the distribution of disease risk across human populations. The advent of Ancient DNA (aDNA) technology now presents us with an unprecedented opportunity to examine these mechanisms in a manner not previously possible by directly allowing changes in gene frequency over time and space. Over the past decade, the field has grown dramatically, with publicly available aDNA samples expanding more than 100-fold to over 10,000. However, this rapid expansion of data has also led to major increases in complexity and scale that have outgrown existing analytical methods to detect admixture and natural selection. My research program addresses this gap by developing computational and statistical methods that the integrates aDNA with large-scale genome-wide association studies (GWAS) to uncover the mechanisms underlying genetic and phenotypic evolution. First, we will develop highly scalable methods for automatic reconstruction of admixture graphs and large human pedigrees, unlocking large sample size analysis of both population and familial dynamics. These tools will have broad applications beyond just time series data and help enhance association analyses for both rare and common diseases by improving resolution of population structure and relatedness particularly in populations with complex demographic histories. Second, we will extend recent models we have built to study natural selection in ancient populations, including the evolution of complex traits, while addressing challenges posed by population structure. Third, we will expand the scope of evolutionary analysis beyond DNA to include DNA methylation, providing insights into the evolution of life history traits such as age at death over long time scales. Finally, by leveraging the thousands of skeletal samples already analyzed for aDNA and combining this information with climatic and biochemical information, we will develop predictive models for DNA preservation, which will inform future sampling efforts and importantly advance our understanding of why DNA remains stable over long time scales in certain natural environments but not others. These insights will be broadly applicable, from improving studies of ancient pathogens to enhancing study of genetic disease risk while deepening our understanding of fundamental biological processes shaping human genetic variation. The computational tools built will broadly benefit the scientific community across medical domains.


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 = $398,742 )
2026	2026	UNIVERSITY OF TEXAS AT AUSTIN	110 INNER CAMPUS DR	AUSTIN	TX	78712	TRAVIS	USA	Biomedical Research and Research Training	000	1	3/16/2026	NEW	$398,742
														Subtotal = $398,742

Grand Total All Awards = $398,742

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High resolution models for understanding admixture, adaptation and disease in humans through the lens of time series genomic data

Award Number: R35GM162367

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: 03/16/2025

PERIOD OF PERFORMANCE END DATE: 02/28/2031

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