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Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have t...

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Autores principales: Tavares, Vânia, Monteiro, Joana, Vassos, Evangelos, Coleman, Jonathan, Prata, Diana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536060/
https://www.ncbi.nlm.nih.gov/pubmed/34680927
http://dx.doi.org/10.3390/genes12101531
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author Tavares, Vânia
Monteiro, Joana
Vassos, Evangelos
Coleman, Jonathan
Prata, Diana
author_facet Tavares, Vânia
Monteiro, Joana
Vassos, Evangelos
Coleman, Jonathan
Prata, Diana
author_sort Tavares, Vânia
collection PubMed
description Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.
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spelling pubmed-85360602021-10-23 Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study Tavares, Vânia Monteiro, Joana Vassos, Evangelos Coleman, Jonathan Prata, Diana Genes (Basel) Article Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC. MDPI 2021-09-28 /pmc/articles/PMC8536060/ /pubmed/34680927 http://dx.doi.org/10.3390/genes12101531 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tavares, Vânia
Monteiro, Joana
Vassos, Evangelos
Coleman, Jonathan
Prata, Diana
Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title_full Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title_fullStr Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title_full_unstemmed Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title_short Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study
title_sort evaluation of genotype-based gene expression model performance: a cross-framework and cross-dataset study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536060/
https://www.ncbi.nlm.nih.gov/pubmed/34680927
http://dx.doi.org/10.3390/genes12101531
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