A probabilistic approach to visualize the effect of missing data on PCA in ancient human genomics.

ABSTRACT:

Background

Principal Component Analysis (PCA) is widely used in population genetics to visualize genetic relationships and population structures. In ancient genomics, genotype information may in parts remain unresolved due to the low abundance and degraded quality of ancient DNA. While methods like SmartPCA allow the projection of ancient samples despite missing data, they do not quantify projection uncertainty. The reliability of PCA projections for often very sparse ancient genotype samples is not well understood. Ignoring this uncertainty may lead to overconfident conclusions about the observed genetic relationships and population structure.

Results

This study systematically investigates the impact of missing loci on PCA projections using both simulated and real ancient h

SUBMITTER: Zabel S 

PROVIDER: S-EPMC12108005 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature