Peripheral Blood Nascent RNA Sequencing Captures Ancestry-Linked Differential Gene Expression

Genetic ancestry reflects inherited population structure that can influence gene regulation and disease susceptibility, yet biomedical studies continue to rely on self-identified race as a proxy for genetic variation. We developed a streamlined framework to infer local and global genetic ancestry directly from nascent RNA sequencing data and tested whether ancestry-based stratification improves detection of transcriptional differences compared with race. Peripheral blood samples from 50 donors were profiled using Peripheral Blood Chromatin Run-On sequencing (pChRO). Ancestry-informative single nucleotide polymorphisms were identified from aligned reads and compared with 1000 Genomes reference populations representing populations in the U.S. using a Bayesian model to infer ancestry locally. Global ancestry proportions were assigned by unsupervised clustering methods. Differential gene expression was analyzed by controlling for age and sex, and the number of true differentially expressed genes (DEGs) was estimated using a q-value framework. Ancestry inference from sparse nascent transcription data recovered heterogeneous local ancestry patterns and separated individuals into two major ancestry clusters, revealing multiple discordances with self-identified race. Grouping by genetically inferred ancestry increased power to detect transcriptional differences relative to to self-identified race, including genes involved in dermatologic and neurodegenerative pathways. These results demonstrate that nascent RNA sequencing enables ancestry inference and that ancestry-based stratification captures transcriptional variation not resolved by race, supporting its integration into population scale functional genomics study.