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Direct inference and control of genetic population structure from RNA sequencing data
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...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397182/ https://www.ncbi.nlm.nih.gov/pubmed/37532769 http://dx.doi.org/10.1038/s42003-023-05171-9 |
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author | Fachrul, Muhamad Karkey, Abhilasha Shakya, Mila Judd, Louise M. Harshegyi, Taylor Sim, Kar Seng Tonks, Susan Dongol, Sabina Shrestha, Rajendra Salim, Agus Baker, Stephen Pollard, Andrew J. Khor, Chiea Chuen Dolecek, Christiane Basnyat, Buddha Dunstan, Sarah J. Holt, Kathryn E. Inouye, Michael |
author_facet | Fachrul, Muhamad Karkey, Abhilasha Shakya, Mila Judd, Louise M. Harshegyi, Taylor Sim, Kar Seng Tonks, Susan Dongol, Sabina Shrestha, Rajendra Salim, Agus Baker, Stephen Pollard, Andrew J. Khor, Chiea Chuen Dolecek, Christiane Basnyat, Buddha Dunstan, Sarah J. Holt, Kathryn E. Inouye, Michael |
author_sort | Fachrul, Muhamad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10397182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103971822023-08-04 Direct inference and control of genetic population structure from RNA sequencing data Fachrul, Muhamad Karkey, Abhilasha Shakya, Mila Judd, Louise M. Harshegyi, Taylor Sim, Kar Seng Tonks, Susan Dongol, Sabina Shrestha, Rajendra Salim, Agus Baker, Stephen Pollard, Andrew J. Khor, Chiea Chuen Dolecek, Christiane Basnyat, Buddha Dunstan, Sarah J. Holt, Kathryn E. Inouye, Michael Commun Biol Article 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. Nature Publishing Group UK 2023-08-02 /pmc/articles/PMC10397182/ /pubmed/37532769 http://dx.doi.org/10.1038/s42003-023-05171-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fachrul, Muhamad Karkey, Abhilasha Shakya, Mila Judd, Louise M. Harshegyi, Taylor Sim, Kar Seng Tonks, Susan Dongol, Sabina Shrestha, Rajendra Salim, Agus Baker, Stephen Pollard, Andrew J. Khor, Chiea Chuen Dolecek, Christiane Basnyat, Buddha Dunstan, Sarah J. Holt, Kathryn E. Inouye, Michael Direct inference and control of genetic population structure from RNA sequencing data |
title | Direct inference and control of genetic population structure from RNA sequencing data |
title_full | Direct inference and control of genetic population structure from RNA sequencing data |
title_fullStr | Direct inference and control of genetic population structure from RNA sequencing data |
title_full_unstemmed | Direct inference and control of genetic population structure from RNA sequencing data |
title_short | Direct inference and control of genetic population structure from RNA sequencing data |
title_sort | direct inference and control of genetic population structure from rna sequencing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397182/ https://www.ncbi.nlm.nih.gov/pubmed/37532769 http://dx.doi.org/10.1038/s42003-023-05171-9 |
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