title: A forest-based feature screening approach for large-scale genome data with complex structures.
abstract: BACKGROUND
  Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. Feature screening reduces the feature space from millions to hundreds by removing non-informative noise. However, the univariate measures used to rank features are mainly based on individual effect without considering the mutual interactions with other features. In this article, we explore the performance of a random forest (RF) based feature screening procedure to emphasize the SNPs that have complex effects for a continuous phenotype.
  
  
  RESULTS
  Both simulation and real data analysis are conducted to examine the power of the forest-based feature screening. We compare it with five other popular feature screening approaches via simulation and conclude that RF can serve as a decent feature screening tool to accommodate complex genetic effects such as nonlinear, interactive, correlative, and joint effects. Unlike the traditional p-value based Manhattan plot, we use the Permutation Variable Importance Measure (PVIM) to display the relative significance and believe that it will provide as much useful information as the traditional plot.
  
  
  CONCLUSIONS
  Most complex traits are found to be regulated by epistatic and polygenic variants. The forest-based feature screening is proven to be an efficient, easily implemented, and accurate approach to cope whole genome data with complex structures. Our explorations should add to a growing body of enlargement of feature screening better serving the demands of contemporary genome data.
authors.affiliation: Department of Mathematics and Statistics, Utah State University, 3900 Old Main, Logan, 84322, UT, USA. gang.wang@aggiemail.usu.edu.
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Mathematics and Statistics, Utah State University, 3900 Old Main, Logan, 84322, UT, USA. guifang.fu@usu.edu.
authors.forename: Guifang
authors.initials: G
authors.lastname: Fu
authors.affiliation: Department of Mathematics and Statistics, Utah State University, 3900 Old Main, Logan, 84322, UT, USA. chris.corcoran@usu.edu.
authors.forename: Christopher
authors.initials: C
authors.lastname: Corcoran
mesh_terms: D000818:Animals; D008076:Cholesterol, HDL / Q000097:blood / Q000235:genetics*; D003198:Computer Simulation; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D006937:Hypercholesterolemia; D051379:Mice; D008957:Models, Genetic; D020412:Multifactorial Inheritance
chemical_list: D008076:Cholesterol, HDL