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author:("Yu, guoyang")
1.  A Ground Truth Based Comparative Study on Detecting Epistatic SNPs 
Genome-wide association studies (GWAS) have been widely applied to identify informative SNPs associated with common and complex diseases. Besides single-SNP analysis, the interaction between SNPs is believed to play an important role in disease risk due to the complex networking of genetic regulations. While many approaches have been proposed for detecting SNP interactions, the relative performance and merits of these methods in practice are largely unclear. In this paper, a ground-truth based comparative study is reported involving 9 popular SNP detection methods using realistic simulation datasets. The results provide general characteristics and guidelines on these methods that may be informative to the biological investigators.
doi:10.1109/BIBMW.2009.5332132
PMCID: PMC2998769  PMID: 21151836
Genome-wide association study; single-nucleotide polymorphism; SNP interaction
2.  An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions 
Bioinformatics  2009;25(19):2478-2485.
Motivation: 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.
Availability: http://www.cbil.ece.vt.edu/ResearchOngoingSNP.htm
Contact: djmiller@engr.psu.edu
Supplementary information:Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp435
PMCID: PMC3140808  PMID: 19608708

Results 1-2 (2)