title: An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions.
abstract: BACKGROUND
  In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models.
  
  
  RESULTS
  We address these objectives via maximum entropy conditional probability modeling (MECPM), coupled with a novel model structure search. Unlike neural networks and support vector machines (SVMs), MECPM makes explicit and is determined by the interactions that confer phenotype-predictive power. Our method identifies both a marker subset and the multiple k-way interactions between these markers. Additional key aspects are: (i) evaluation of a select subset of up to five-way interactions while retaining relatively low complexity; (ii) flexible single nucleotide polymorphism (SNP) coding (dominant, recessive) within each interaction; (iii) no mathematical interaction form assumed; (iv) model structure and order selection based on the Bayesian Information Criterion, which fairly compares interactions at different orders and automatically sets the experiment-wide significance level; (v) MECPM directly yields a phenotype-predictive model. MECPM was compared with a panel of methods on datasets with up to 1000 SNPs and up to eight embedded penetrance function (i.e. ground-truth) interactions, including a five-way, involving less than 20 SNPs. MECPM achieved improved sensitivity and specificity for detecting both ground-truth markers and interactions, compared with previous methods.
  
  
  BACKGROUND
  http://www.cbil.ece.vt.edu/ResearchOngoingSNP.htm
authors.affiliation: Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu
authors.forename: David J
authors.initials: DJ
authors.lastname: Miller
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Zhang
authors.forename: Guoqiang
authors.initials: G
authors.lastname: Yu
authors.forename: Yongmei
authors.initials: Y
authors.lastname: Liu
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.forename: Carl D
authors.initials: CD
authors.lastname: Langefeld
authors.forename: David
authors.initials: D
authors.lastname: Herrington
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019277:Entropy; D020022:Genetic Predisposition to Disease; D016678:Genome; D055106:Genome-Wide Association Study; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide