Predictive rule inference for epistatic interaction detection in genome-wide association studies Export

@article{citeulike:6055156, abstract = {Motivation: 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. Availability: http://bioinformatics.ust.hk/SNPRuler.zip Contact: eexiangw@ust.hk, eeyu@ust.hk 10.1093/bioinformatics/btp622}, author = {Wan, Xiang and Yang, Can and Yang, Qiang and Xue, Hong and Tang, Nelson L. S. and Yu, Weichuan}, citeulike-article-id = {6055156}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp622}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/btp622v1/.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/btp622v1/.full.pdf}, day = {30}, doi = {10.1093/bioinformatics/btp622}, journal = {Bioinformatics}, keywords = {epistasis, gwa, interaction, prediction, snp}, month = {October}, pages = {btp622+}, posted-at = {2009-11-02 04:16:37}, priority = {2}, title = {Predictive rule inference for epistatic interaction detection in genome-wide association studies}, url = {http://dx.doi.org/10.1093/bioinformatics/btp622}, year = {2009} }

TY - JOUR ID - citeulike:6055156 L3 - citeulike-article-id:6055156 N2 - Motivation: 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. Availability: http://bioinformatics.ust.hk/SNPRuler.zip Contact: eexiangw@ust.hk, eeyu@ust.hk 10.1093/bioinformatics/btp622 JF - Bioinformatics TI - Predictive rule inference for epistatic interaction detection in genome-wide association studies SP - btp622 KW - epistasis KW - gwa KW - interaction KW - prediction KW - snp AU - Wan, Xiang AU - Yang, Can AU - Yang, Qiang AU - Xue, Hong AU - Tang, Nelson AU - Yu, Weichuan PY - 2009/10/30/ UR - http://dx.doi.org/10.1093/bioinformatics/btp622 ER -