Predictive rule inference for epistatic interaction detection in genome-wide association studies.
BACKGROUND
Under the current era of genome-wide association study (GWAS), finding epistatic interactions in the large volume of SNP data is a challenging and unsolved issue. Few of previous studies could handle genome-wide data due to the difficulties in searching the combinatorially explosive search space and statistically evaluating high-order epistatic interactions given the limited number of samples. In this work, we propose a novel learning approach (SNPRuler) based on the predictive rule inference to find disease-associated epistatic interactions.


RESULTS
Our extensive experiments on both simulated data and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC) show that SNPRuler significantly outperforms its recent competitor. To our knowledge, SNPRuler is the first method that guarantees to find the epistatic interactions without exhaustive search. Our results indicate that finding epistatic interactions in GWAS is computationally attainable in practice.


BACKGROUND
http://bioinformatics.ust.hk/SNPRuler.zip
Wan
Xiang
X
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk
Yang
Can
C
Yang
Qiang
Q
Xue
Hong
H
Tang
Nelson L S
NL
Yu
Weichuan
W
D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D004843:Epistasis, Genetic / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*