Automated machine learning for genome wide association studies.

ABSTRACT

MOTIVATION Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice. RESULTS: We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures. AVAILABILITY AND IMPLEMENTATION Code for this study is available at https//github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at https//jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https//ega-archive.org/). PRS data are found at https//www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at https//easygwas.ethz.ch/data/public/dataset/view/1/.