title: A system-level pathway-phenotype association analysis using synthetic feature random forest.
abstract: As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations.
authors.affiliation: Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.
authors.forename: Qinxin
authors.initials: Q
authors.lastname: Pan
authors.forename: Ting
authors.initials: T
authors.lastname: Hu
authors.forename: James D
authors.initials: JD
authors.lastname: Malley
authors.forename: Angeline S
authors.initials: AS
authors.lastname: Andrew
authors.forename: Margaret R
authors.initials: MR
authors.lastname: Karagas
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D004843:Epistasis, Genetic / Q000235:genetics*; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015203:Reproducibility of Results; D001749:Urinary Bladder Neoplasms / Q000235:genetics
keywords: epistasis; interactions; pathway analysis; statistical epistasis network (SEN); synthetic feature random forest (SF-RF)