Transcriptome prediction performance across machine learning models and diverse ancestries.

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 Mo

SUBMITTER: Okoro PC 

PROVIDER: S-EPMC8087249 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature