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

Vânia Tavares^1,2, Joana Monteiro^1,3, Evangelos Vassos^4,5

¹Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

|October 23, 2021

Summary

PrediXcan, a gene expression prediction method, outperforms eGenScore for brain tissue analysis. Training PrediXcan models with GTEx data yielded better results for frontal cortex gene expression prediction than using brain-specific data.

Keywords:

expression quantitative trait loci gene expression genome wide association study polygenic score transcriptome

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Area of Science:

Genetics
Bioinformatics
Neuroscience

Background:

Predicting gene expression from genotype data is crucial for studying tissues like the brain.
Existing methods like eGenScore and PrediXcan aim to predict gene expression using genetic information.

Purpose of the Study:

To compare the performance of eGenScore and PrediXcan for predicting gene expression in the frontal cortex.
To evaluate the impact of different training datasets (brain-specific vs. multi-tissue) on prediction accuracy.

Main Methods:

Development and comparison of eGenScore (polygenic/poly-variation) and PrediXcan (regularized linear regression with elastic nets).
Training and validation of expression quantitative trait loci (eQTL) models using frontal cortex data.
Internal cross-validation and external validation using the CommonMind Consortium database.

Main Results:

PrediXcan demonstrated superior performance over eGenScore across all tested training datasets.
eQTL models trained with GTEx (multi-tissue) data showed higher prediction accuracy in the frontal cortex compared to those trained with BrainEAC (brain-specific) data when using PrediXcan.

Conclusions:

PrediXcan is a more effective method for predicting gene expression in brain tissues compared to eGenScore.
Multi-tissue gene expression datasets like GTEx can improve the accuracy of predicting gene expression in specific brain regions.