Show simple item record

dc.contributor.authorSebastiani, Paolaen_US
dc.contributor.authorZhao, Zhenmingen_US
dc.contributor.authorAbad-Grau, María M.en_US
dc.contributor.authorRiva, Albertoen_US
dc.contributor.authorHartley, Stephen W.en_US
dc.contributor.authorSedgewick, Amanda E.en_US
dc.contributor.authorDoria, Alessandroen_US
dc.contributor.authorMontano, Montyen_US
dc.contributor.authorMelista, Efthymiaen_US
dc.contributor.authorTerry, Dellaraen_US
dc.contributor.authorPerls, Thomas T.en_US
dc.contributor.authorSteinberg, Martin H.en_US
dc.contributor.authorBaldwin, Clinton T.en_US
dc.date.accessioned2011-12-29T20:57:40Z
dc.date.available2011-12-29T20:57:40Z
dc.date.copyright2008
dc.date.issued2008-1-14
dc.identifier.citationSebastiani, Paola, Zhenming Zhao, Maria M Abad-Grau, Alberto Riva, Stephen W Hartley, Amanda E Sedgewick, Alessandro Doria, Monty Montano, Efthymia Melista, Dellara Terry, Thomas T Perls, Martin H Steinberg, Clinton T Baldwin. "A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples." BMC Genetics 9:6. (2008)
dc.identifier.issn1471-2156
dc.identifier.urihttps://hdl.handle.net/2144/2484
dc.description.abstractBACKGROUND: One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. RESULTS. We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. CONCLUSION: Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects.en_US
dc.description.sponsorshipNational Heart, Lung, and Blood Institute (R21 HL080463, R01 HL68970, K-24 AG025727, K23 AG026754)en_US
dc.language.isoen
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2008 Sebastiani et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.titleA Hierarchical and Modular Approach to the Discovery of Robust Associations in Genome-Wide Association Studies from Pooled DNA Samplesen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/1471-2156-9-6
dc.identifier.pmid18194558
dc.identifier.pmcid2248205


This item appears in the following Collection(s)

Show simple item record

Copyright 2008 Sebastiani et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2008 Sebastiani et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.