Point cloud local ancestry inference (PCLAI): continuous coordinate-based ancestry along the genome

Abstract Local ancestry inference (LAI) predicts a discrete ancestry label for each segment of an individual’s genome and has become integral to studying population history, genetic variation, and polygenic trait association. We present a new local ancestry paradigm that eschews discrete categorical labels and instead performs inference in a continuous coordinate space. We call this method “point cloud local ancestry inference” (PCLAI), since it represents an individual’s genetic ancestry as a point cloud with each point corresponding to a small haplotypic segment in their genome. This formulation works in any co-ordinate space (for instance, geographic or principal components) permitting the representation of continuous genetic variation at the haplotypic-segment level without resorting to artificially constructed discrete labels. We illustrate PCLAI by training on ancient samples from multiple time periods separately, yielding chromosome paintings based on geography that are time-stratified and provide insight into how individuals’ genomic segments moved across space and time.