title: Chapter 10: Mining genome-wide genetic markers.
abstract: Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions.
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, Ohio, United States of America.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.forename: Shunping
authors.initials: S
authors.lastname: Huang
authors.forename: Zhaojun
authors.initials: Z
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D006801:Humans; D016247:Information Storage and Retrieval; D008957:Models, Genetic
chemical_list: D005819:Genetic Markers