Direct inference and control of genetic population structure from RNA sequencing data
- Fachrul, M.
- Karkey, A.
- Shakya, M.
- Judd, L.M.
- Harshegyi, T.
- Sim, K.S.
- Tonks, S.
- Dongol, S.
- Shrestha, R.
- Salim, A.
- Adhikari, A.
- Banda, H.C.
- Blohmke, C.
- Darton, T.C.
- Farooq, Y.
- Ghimire, M.
- Hill, J.
- Hoang, N.T.
- Jere, T.M.
- Kamzati, M.
- Kao, Y.-H.
- Masesa, C.
- Mbewe, M.
- Msuku, H.
- Munthali, P.
- Nga, T.V.T.
- Nkhata, R.
- Saad, N.J.
- Van Tan, T.
- Thindwa, D.
- Khanam, F.
- Meiring, J.
- Clemens, J.D.
- Dougan, G.
- Pitzer, V.E.
- Qadri, F.
- Heyderman, R.S.
- Gordon, M.A.
- Voysey, M.
- Baker, S.
- Pollard, A.J.
- Khor, C.C.
- Dolecek, C.
- Basnyat, B.
- Dunstan, S.J.
- Holt, K.E.
- Inouye, M.
DOIAbstract
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 r...
Add to ORKG