دورية أكاديمية
On the cross-population generalizability of gene expression prediction models.
| العنوان: | On the cross-population generalizability of gene expression prediction models. |
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| المؤلفون: | Kevin L Keys, Angel C Y Mak, Marquitta J White, Walter L Eckalbar, Andrew W Dahl, Joel Mefford, Anna V Mikhaylova, María G Contreras, Jennifer R Elhawary, Celeste Eng, Donglei Hu, Scott Huntsman, Sam S Oh, Sandra Salazar, Michael A Lenoir, Jimmie C Ye, Timothy A Thornton, Noah Zaitlen, Esteban G Burchard, Christopher R Gignoux |
| المصدر: | PLoS Genetics, Vol 16, Iss 8, p e1008927 (2020) |
| بيانات النشر: | Public Library of Science (PLoS), 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Genetics |
| مصطلحات موضوعية: | Genetics, QH426-470 |
| الوصف: | The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1553-7390 1553-7404 |
| العلاقة: | https://doaj.org/toc/1553-7390Test; https://doaj.org/toc/1553-7404Test |
| DOI: | 10.1371/journal.pgen.1008927 |
| الوصول الحر: | https://doaj.org/article/22c150d8426343a09ce557fa67ba7215Test |
| رقم الانضمام: | edsdoj.22c150d8426343a09ce557fa67ba7215 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 15537390 15537404 |
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| DOI: | 10.1371/journal.pgen.1008927 |