Direct inference and control of genetic population structure from RNA sequencing data
Muhamad Fachrul, Abhilasha Karkey, Mila Shakya, Louise M. Judd, Taylor Harshegyi, Kar Seng Sim, Susan Tonks, Sabina Dongol, Rajendra Shrestha, Agus Salim, STRATAA study group, Anup Adhikari, Happy Banda, Christoph J. Blohmke, Thomas C. Darton, Yama F Mujadidi, Maheshwar Ghimire, Jennifer Hill, Tran Do Hoang Nhu, Tikhala Makhaza Jere, Moses Kamzati, Yu-Han Kao, Clemens Masesa, Maurice Mbewe, Harrison Msuku, Patrick Munthali, Tran Vu Thieu Nga, Rose Nkhata, Neil J. Saad, Trinh Van Tan, Deus Thindwa, Farhana Khanam, James Meiring, John D. Clemens, Gordon Dougan, Virginia E. Pitzer, Firdausi Qadri, Robert S. Heyderman, Melita A. Gordon, Merryn Voysey, Stephen Baker, Andrew J. Pollard, Chiea Chuen Khor, Christiane Dolecek, Buddha Basnyat, Sarah J. Dunstan, Kathryn E. Holt, Michael Inouye
Abstract
RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.