| For this project, we develop, optimize, and implement a novel analytical pipeline that combines a tree-based variable selection method with an evolutionary computation modeling method. The purpose of this pipeline is to integrate high-throughput data from different levels of biological regulation to identify meta-dimensional models that predict a given outcome. We hypothesize that by integrating different types of data we will identify aspects of the genetic architecture that would have been missed by single variable and/or single data type study designs.;The development process consisted of rigorous performance testing, method comparisons, and parameter optimizations using in silico and biological data sets. Next, we applied the analysis pipeline to a data set with SNP genotypes, gene expression variables, and quantitative low-density lipoprotein cholesterol (LDL-C) trait outcomes.;Using our meta-dimensional analysis pipeline, we were able to generate multi-variable models that explain a proportion of the inter-individual variation in LDL-C traits. Additionally, we were able to map these genetic variants to biological units and pathways that would not have been identified with single data type analysis. |
