PHATE-Derived Ancestry Coordinates Capture Continuous Population Structure in Human Genomic Data
- 1Université de Montréal
Abstract
Population genetic data are increasingly visualized using low-dimensional embeddings as ancestry coordinates, yet how these methods represent population structure can depend strongly on sampling and construction choices. Principal component analysis (PCA), when visualized using only leading components, can obscure fine-scale structure, while nonlinear neighbor-based methods such as uniform manifold approximation and projection (UMAP) often fragment geographic and admixture gradients into discrete clusters. Here, we introduce a framework for constructing ancestry coordinates using potential of heat-diffusion for affinity-based trajectory embedding (PHATE), a diffusion-based method that preserves relationships across scales. Across synthetic benchmarks and large genomic datasets, we show that appropriate scale tuning and population balancing allow PHATE to recover geographic organization and admixture gradients. We show that incorrectly tuned neighborhood scale and population imbalance can distort the resulting representations of population structure. These results therefore provide a methodological framework and practical guidance for constructing ancestry visualizations that better reflect continuous human genetic diversity.
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BibTeX
@misc{scicluna2026phatederived,
title = {{PHATE-Derived Ancestry Coordinates Capture Continuous Population Structure in Human Genomic Data}},
author = {M. C. Scicluna and S. Ni and C. M. Valdez Cordova and J.-C. Grenier and R. Poujol and M. E. Kanoun and J. F. Rocha and K. R. Moon and S. Lemieux and G. Wolf and S. Krishnaswamy and J. Hussin},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762525},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762525v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - PHATE-Derived Ancestry Coordinates Capture Continuous Population Structure in Human Genomic Data
AU - M. C. Scicluna
AU - S. Ni
AU - C. M. Valdez Cordova
AU - J.-C. Grenier
AU - R. Poujol
AU - M. E. Kanoun
AU - J. F. Rocha
AU - K. R. Moon
AU - S. Lemieux
AU - G. Wolf
AU - S. Krishnaswamy
AU - J. Hussin
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762525
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762525v1
AB - Population genetic data are increasingly visualized using low-dimensional embeddings as ancestry coordinates, yet how these methods represent population structure can depend strongly on sampling and construction choices. Principal component analysis (PCA), when visualized using only leading components, can obscure fine-scale structure, while nonlinear neighbor-based methods such as uniform manifold approximation and projection (UMAP) often fragment geographic and admixture gradients into discrete clusters. Here, we introduce a framework for constructing ancestry coordinates using potential of heat-diffusion for affinity-based trajectory embedding (PHATE), a diffusion-based method that preserves relationships across scales. Across synthetic benchmarks and large genomic datasets, we show that appropriate scale tuning and population balancing allow PHATE to recover geographic organization and admixture gradients. We show that incorrectly tuned neighborhood scale and population imbalance can distort the resulting representations of population structure. These results therefore provide a methodological framework and practical guidance for constructing ancestry visualizations that better reflect continuous human genetic diversity.
ER -