Bayesian Hyperparameter Optimization for Machine Learning Based eQTL Analysis

Machine learning methods are being applied to a wide range of problems in biology and bioinformatics. These methods often rely on configuring high level parameters, or hyperparameters, such as regularization hyperparameters in sparse learning models like graph-guided multitask Lasso methods. Different choices for these hyperparameters will lead to different results, which makes finding good hyperparameter combinations an important task when using these hyperparameter dependent methods. There are several different ways to tune hyperparameters including manual tuning, grid search, random search, and Bayesian optimization. In this paper, we apply three hyperparameter tuning strategies to eQTL analysis including grid and random search in addition to Bayesian optimization. Experiments show that the Bayesian optimization strategy outperforms the other strategies in modeling eQTL associations. Applying this strategy to assess eQTL associations using the 1000 Genomes structural variation genotypes and RNAseq data in gEUVADIS, we identify a set of new SVs associated with gene expression changes in a human population.