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Lookup NU author(s): James Fryett, Professor Heather Cordell
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data, and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods - LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model and Random Forests - by performing cross-validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries, and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.
Author(s): Fryett JJ, Morris AP, Cordell HJ
Publication type: Article
Publication status: Published
Journal: Genetic Epidemiology
Year: 2020
Volume: 44
Issue: 5
Pages: 425-441
Print publication date: 01/07/2020
Online publication date: 19/03/2020
Acceptance date: 06/03/2020
Date deposited: 07/03/2020
ISSN (print): 0741-0395
ISSN (electronic): 1098-2272
Publisher: John Wiley & Sons, Inc.
URL: https://doi.org/10.1002/gepi.22290
DOI: 10.1002/gepi.22290
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