title: Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.
abstract: Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.
authors.affiliation: Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, United States of America.
authors.forename: Badri
authors.initials: B
authors.lastname: Padhukasahasram
authors.affiliation: Department of Computer Science, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Chandan K
authors.initials: CK
authors.lastname: Reddy
authors.affiliation: Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, United States of America.
authors.forename: Albert M
authors.initials: AM
authors.lastname: Levin
authors.affiliation: Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Esteban G
authors.initials: EG
authors.lastname: Burchard
authors.affiliation: Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, United States of America.|Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan, United States of America.
authors.forename: L Keoki
authors.initials: LK
authors.lastname: Williams
mesh_terms: D001249:Asthma / Q000235:genetics*; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers