# Genomic Insights into Human Population Mixture and its Role in Adaptation and Disease

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA BERKELEY · 2022 · $376,839

## Abstract

Project Summary
 Recent studies have shown that population mixture (or `admixture') is pervasive throughout human
evolution and has played a major role in shaping human genetic and phenotypic variation. Despite the
ubiquity and importance of population mixture, we still lack adequate methods to characterize the impact
of admixture on a genomic scale and leverage this information for effective gene mapping. Addressing
these topics is the central focus of research in my lab. In this proposal, our goal is to develop new methods to
reconstruct fine-scale genomic ancestry in admixed groups and leverage this information to identify novel
disease and adaptive mutations and genes. The application of these methods to large genomic surveys will
help to discover novel disease and adaptive variants.
 The first step in characterizing the genomic impact of admixture is to infer the ancestry of each
chromosomal segment, referred to as local ancestry. Towards this goal, we are developing new methods for
local ancestry inference using machine-learning approaches that are ideally suited for classification problems
and computationally tractable for large datasets. Our preliminary results show that our method is highly
accurate and applicable across a range of demographic models. With reliable local ancestry inference, we will
be well placed to study the impact of admixture on disease architecture and evolution of complex traits. We
propose to use Admixture Mapping, a method to identify disease associations by leveraging ancestry
differences across the genome, between cases and controls or among cases alone. By applying Admixture
Mapping to complex admixed groups like South Asians and Latinxs, we aim to discover new population-
specific disease associations and advance our understanding of disease architecture. Further, we will develop
a novel method to leverage the demographic history of admixed groups to identify adaptive variants. By
applying the method to study selection at various timescales in human evolution, we will uncover candidate
genes and pathways related to adaptive gene flow and characterize its role in shaping human genetic
variation. Finally, we will build reference-free ancestral genomes by recovering chromosomal segments of
our lost ancestors hidden in admixed genomes. We will use these genomes to reconstruct the demographic
history of our ancestors, as well as understand the fitness effects of population mixtures and the phenotypic
legacy of our extinct ancestors.
 The successful completion of the proposed project will provide new statistical tools to leverage patterns
of admixture to perform effective disease mapping and evolutionary inference in diverse, admixed groups.
Application of these methods to large-scale genomic datasets will provide insights into the genetic,
evolutionary, and functional impact of admixture during human evolution. Algorithms proposed here will be
implemented in freely available software for use by other researchers.

## Key facts

- **NIH application ID:** 10461145
- **Project number:** 5R35GM142978-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Priya Moorjani
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $376,839
- **Award type:** 5
- **Project period:** 2021-08-03 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10461145

## Citation

> US National Institutes of Health, RePORTER application 10461145, Genomic Insights into Human Population Mixture and its Role in Adaptation and Disease (5R35GM142978-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10461145. Licensed CC0.

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