Document Type
Article
Publication Date
4-8-2021
Publication Title
HGG Advances
Volume
2
Issue
2
Pages
1-14
Publisher Name
Elsevier
Abstract
Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.
Recommended Citation
Okoro, Paul Chukwuebuka; Schubert, Ryan; Guo, Xiuqing; Johnson, W. Craig; Rotter, Jerome I.; Hoeschele, Ina; Liu, Yongmei; Kyung Im, Hae; Luke, Amy; Dugas, Lara R.; and Wheeler, Heather. Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries. HGG Advances, 2, 2: 1-14, 2021. Retrieved from Loyola eCommons, Biology: Faculty Publications and Other Works, http://dx.doi.org/10.1016/j.xhgg.2020.100019
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
© The Authors, 2021.
Comments
Author Posting © The Authors, 2021. This article was published open access in HGG Advances, Volume 2, Issue 2, April 2021. https://doi.org/10.1016/j.xhgg.2020.100019