title: Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest.
abstract: We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.
authors.affiliation: Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA. usman@cs.njit.edu
authors.forename: Usman
authors.initials: U
authors.lastname: Roshan
authors.forename: Satish
authors.initials: S
authors.lastname: Chikkagoudar
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wei
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Hakon
authors.initials: H
authors.lastname: Hakonarson
mesh_terms: D001185:Artificial Intelligence; D016009:Chi-Square Distribution; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide