title: Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease.
abstract: Complex genetic disorders are a result of a combination of genetic and nongenetic factors, all potentially interacting. Machine learning methods hold the potential to identify multilocus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of single nucleotide polymorphisms (SNPs) of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges. We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Decision Trees (ADTrees). The algorithm is order nk(2), where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7,822 SNPs in 3,548 individuals). Our results suggest that BADTrees hold promise as a low computational order algorithm for detecting complex combinations of SNP and environmental factors associated with disease.
authors.affiliation: Department of Biostatistical Sciences, Division of Public Health Sciences, Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, NC, USA.
authors.forename: Richard T
authors.initials: RT
authors.lastname: Guy
authors.forename: Peter
authors.initials: P
authors.lastname: Santago
authors.forename: Carl D
authors.initials: CD
authors.lastname: Langefeld
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003663:Decision Trees; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D006680:HLA Antigens / Q000235:genetics; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D017720:Molecular Epidemiology / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012984:Software
chemical_list: D005819:Genetic Markers; D006680:HLA Antigens