Gene expression prediction: A machine learning approach

Tremendous progress in understanding and unravelling genetic predictors of complex traits have been made through genome-wide association studies (GWAS) and transcriptome association methods like PrediXcan. However, these genomic successes were largely achieved in populations of European ancestry; thereby creating disparity in the applicability of these results in other ancestry populations. We have shown that genetic predictors of gene expression built in one continental population do not perform as well when applied to another. Therefore our goal is to use machine learning algorithms to build gene expression prediction models that are specially optimized for African-origin populations and thus broaden the applicability of PrediXcan to diverse populations. The models will be useful to researchers carrying out genomic studies in African populations and will enhance our knowledge of biological mechanisms associated with disease in all populations.