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; Anup Adhikari; Happy Chimphako Banda; Christoph Blohmke; Thomas C. Darton; Yama Farooq; Maheshwar Ghimire; Jennifer Hill; Nhu Tran Hoang; 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

doi:10.1038/s42003-023-05171-9

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, Anup Adhikari, Happy Chimphako Banda, Christoph Blohmke, Thomas C. Darton, Yama Farooq, Maheshwar Ghimire, Jennifer Hill, Nhu Tran Hoang, Tikhala Makhaza Jere, Moses KamzatiYu 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

Liverpool School of Tropical Medicine

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	804
Journal	Communications Biology
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s42003-023-05171-9
Publication status	Published - 2 Aug 2023

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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., ... Inouye, M. (2023). Direct inference and control of genetic population structure from RNA sequencing data. Communications Biology, 6(1), Article 804. https://doi.org/10.1038/s42003-023-05171-9

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title = "Direct inference and control of genetic population structure from RNA sequencing data",

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.",

author = "Muhamad Fachrul and Abhilasha Karkey and Mila Shakya and Judd, \{Louise M.\} and Taylor Harshegyi and Sim, \{Kar Seng\} and Susan Tonks and Sabina Dongol and Rajendra Shrestha and Agus Salim and Anup Adhikari and Banda, \{Happy Chimphako\} and Christoph Blohmke and Darton, \{Thomas C.\} and Yama Farooq and Maheshwar Ghimire and Jennifer Hill and Hoang, \{Nhu Tran\} and Jere, \{Tikhala Makhaza\} and Moses Kamzati and Kao, \{Yu Han\} and Clemens Masesa and Maurice Mbewe and Harrison Msuku and Patrick Munthali and Nga, \{Tran Vu Thieu\} and Rose Nkhata and Saad, \{Neil J.\} and \{Van Tan\}, Trinh and Deus Thindwa and Farhana Khanam and James Meiring and Clemens, \{John D.\} and Gordon Dougan and Pitzer, \{Virginia E.\} and Firdausi Qadri and Heyderman, \{Robert S.\} and Gordon, \{Melita A.\} and Merryn Voysey and Stephen Baker and Pollard, \{Andrew J.\} and Khor, \{Chiea Chuen\} and Christiane Dolecek and Buddha Basnyat and Dunstan, \{Sarah J.\} and Holt, \{Kathryn E.\} and Michael Inouye",

year = "2023",

month = aug,

day = "2",

doi = "10.1038/s42003-023-05171-9",

language = "English",

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Fachrul, M, Karkey, A, Shakya, M, Judd, LM, Harshegyi, T, Sim, KS, Tonks, S, Dongol, S, Shrestha, R, Salim, A, Adhikari, A, Banda, HC, Blohmke, C, Darton, TC, Farooq, Y, Ghimire, M, Hill, J, Hoang, NT, Jere, TM, Kamzati, M, Kao, YH, Masesa, C, Mbewe, M, Msuku, H, Munthali, P, Nga, TVT, Nkhata, R, Saad, NJ, Van Tan, T, Thindwa, D, Khanam, F, Meiring, J, Clemens, JD, Dougan, G, Pitzer, VE, Qadri, F, Heyderman, RS, Gordon, MA, Voysey, M, Baker, S, Pollard, AJ, Khor, CC, Dolecek, C, Basnyat, B, Dunstan, SJ, Holt, KE & Inouye, M 2023, 'Direct inference and control of genetic population structure from RNA sequencing data', Communications Biology, vol. 6, no. 1, 804. https://doi.org/10.1038/s42003-023-05171-9

TY - JOUR

T1 - Direct inference and control of genetic population structure from RNA sequencing data

AU - Fachrul, Muhamad

AU - Karkey, Abhilasha

AU - Shakya, Mila

AU - Judd, Louise M.

AU - Harshegyi, Taylor

AU - Sim, Kar Seng

AU - Tonks, Susan

AU - Dongol, Sabina

AU - Shrestha, Rajendra

AU - Salim, Agus

AU - Adhikari, Anup

AU - Banda, Happy Chimphako

AU - Blohmke, Christoph

AU - Darton, Thomas C.

AU - Farooq, Yama

AU - Ghimire, Maheshwar

AU - Hill, Jennifer

AU - Hoang, Nhu Tran

AU - Jere, Tikhala Makhaza

AU - Kamzati, Moses

AU - Kao, Yu Han

AU - Masesa, Clemens

AU - Mbewe, Maurice

AU - Msuku, Harrison

AU - Munthali, Patrick

AU - Nga, Tran Vu Thieu

AU - Nkhata, Rose

AU - Saad, Neil J.

AU - Van Tan, Trinh

AU - Thindwa, Deus

AU - Khanam, Farhana

AU - Meiring, James

AU - Clemens, John D.

AU - Dougan, Gordon

AU - Pitzer, Virginia E.

AU - Qadri, Firdausi

AU - Heyderman, Robert S.

AU - Gordon, Melita A.

AU - Voysey, Merryn

AU - Baker, Stephen

AU - Pollard, Andrew J.

AU - Khor, Chiea Chuen

AU - Dolecek, Christiane

AU - Basnyat, Buddha

AU - Dunstan, Sarah J.

AU - Holt, Kathryn E.

AU - Inouye, Michael

PY - 2023/8/2

Y1 - 2023/8/2

N2 - 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.

AB - 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.

U2 - 10.1038/s42003-023-05171-9

DO - 10.1038/s42003-023-05171-9

M3 - Article

SN - 2399-3642

VL - 6

JO - Communications Biology

JF - Communications Biology

IS - 1

M1 - 804

ER -

Direct inference and control of genetic population structure from RNA sequencing data

Abstract

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