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Transcriptome prediction performance across machine learning models and diverse ancestries
2021
Human Genetics and Genomics Advances
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
doi:10.1016/j.xhgg.2020.100019
pmid:33937878
pmcid:PMC8087249
fatcat:mzgznh6gfvhqfcrtp65b446fla