# Local ancestry inference for complex admixtures

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $459,116

## Abstract

Summary
All humans are admixtures of various historical source populations. This admixture has occurred
across a range of time-scales, from recent admixture such as the intercontinental admixture in
African Americans or Hispanics, to ancient admixture such as the admixture with Neanderthals
that occurred when modern humans migrated out of Africa around 50,000 years ago.
Local ancestry is the population-of-origin of an individual’s chromosomes at each point in the
genome. Local ancestry is essential for many applications, including admixture mapping and
inferring demographic history. Local ancestry is not directly observed, but must be inferred
from an individual’s genotype data.
Existing methods for inferring local ancestry are inadequate for untangling complex admixtures
in human populations. Existing methods struggle when reference data for the source
populations are limited or poorly-matched. These methods are unable to handle genetically
similar ancestral populations, divergent admixture times, or more than a few ancestral
populations.
We propose to develop new methods and computational tools to address these gaps. Our
methods will utilize a state-of-the-art haplotype frequency model and new computational
methods to greatly improve the accuracy and computational efficiency of local ancestry
inference. Our methods will overcome current limitations by flexible modelling of admixture
times, by enabling local ancestry inference when reference panels are from closely-related
populations, and by creating new reference panels from admixed data when no existing
reference population is well-matched to the ancestral population. Our methods will be
implemented in user-friendly, computationally efficient, open-source software that scales to
analysis of very large samples of sequenced individuals.
We will call fine-scale local ancestry in sequence data from diverse African populations. We will
use local ancestry calls to detect past migration within Africa, to detect post-admixture
selection, and to perform admixture mapping for a broad spectrum of traits including heart,
lung, and blood traits, and infectious disease status.

## Key facts

- **NIH application ID:** 10317031
- **Project number:** 5R01HG010869-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Sharon Browning
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $459,116
- **Award type:** 5
- **Project period:** 2020-01-06 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10317031

## Citation

> US National Institutes of Health, RePORTER application 10317031, Local ancestry inference for complex admixtures (5R01HG010869-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10317031. Licensed CC0.

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