RubricOE: a learning framework for genetic epidemiology

Genetic epidemiology is a growing area of interest in the past years due to the availability of genetic data with the decreasing cost of sequencing. Machine learning (ML) algorithms can be a very useful tool to study the genetic factors on disease incidence or on different traits characterizing a population. There are many challenges that plagues the field of genetic epidemiology including the unbalanced case-control data sets, fallibility of standard genome wide association studies with single marker analysis, heavily underdetermined systems with millions of markers in contrast of a few thousands of samples, to name a few. Ensemble ML methods can be a very useful tool to tackle many of these challenges and thus we propose RubricOE, a pipeline of ML algorithms with error bar computations to obtain interpretable genetic and non-genetic features from genomic or transcriptomic data combined with clinical factors in the form of electronic health records. RubricOE is shown to be robust in simulation studies, detecting true associations with traits of interest in arbitrarily structured multi-ethnic populations.