title: Computational methods for single-point and multipoint analysis of genetic variants associated with a simulated complex disorder in a general population.
abstract: Several techniques for association analysis have been applied to simulated genetic data for a general population. We describe and compare the performance of three single-point methods and two multipoint approaches rooted in machine learning and data mining.
authors.affiliation: Department of Computer Science, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.
authors.forename: C A
authors.initials: CA
authors.lastname: Shoemaker
authors.forename: M
authors.initials: M
authors.lastname: Pungliya
authors.forename: M A
authors.initials: MA
authors.lastname: Sao Pedro
authors.forename: C
authors.initials: C
authors.lastname: Ruiz
authors.forename: S A
authors.initials: SA
authors.lastname: Alvarez
authors.forename: M
authors.initials: M
authors.lastname: Ward
authors.forename: E F
authors.initials: EF
authors.lastname: Ryder
authors.forename: J
authors.initials: J
authors.lastname: Krushkal
mesh_terms: D000483:Alleles; D001185:Artificial Intelligence; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D014644:Genetic Variation; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015999:Multivariate Analysis; D016571:Neural Networks, Computer