Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes. Export

@article{citeulike:1930856, abstract = {MOTIVATION: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity. RESULTS: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.}, author = {English, Sangeeta B. and Butte, Atul J.}, citeulike-article-id = {1930856}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btm483}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/23/21/2910}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17921495}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17921495}, day = {1}, doi = {10.1093/bioinformatics/btm483}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {drug-targets, identification}, month = {November}, number = {21}, pages = {2910--2917}, posted-at = {2008-09-08 15:51:29}, priority = {2}, title = {Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes.}, url = {http://dx.doi.org/10.1093/bioinformatics/btm483}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1930856 L3 - citeulike-article-id:1930856 N2 - MOTIVATION: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity. RESULTS: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well. IS - 21 JF - Bioinformatics (Oxford, England) SN - 1460-2059 EP - 2917 TI - Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes. VL - 23 SP - 2910 KW - drug-targets KW - identification AU - English, Sangeeta AU - Butte, Atul PY - 2007/11/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btm483 ER -