A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples

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A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples

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dc.contributor.author Sebastiani, Paola
dc.contributor.author Zhao, Zhenming
dc.contributor.author Abad-Grau, Maria M
dc.contributor.author Riva, Alberto
dc.contributor.author Sedgewick, Amanda E
dc.contributor.author Melista, Efthymia
dc.contributor.author Terry, Dellara
dc.contributor.author Perls, Thomas T
dc.contributor.author Steinberg, Martin H
dc.contributor.author Baldwin, Clinton T
dc.contributor.author Hartley, Stephen W.
dc.contributor.author Doria, Alessandro
dc.contributor.author Montano, Monty A.
dc.date.accessioned 2011-04-22T19:35:27Z
dc.date.issued 2008
dc.identifier.citation Sebastiani, Paola, Zhenming Zhao, Maria M. Abad-Grau, Alberto Riva, Stephen W. Hartley, Amanda E. Sedgewick, Alessandro Doria, et al. 2008. A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples. BMC Genetics 9: 6. en_US
dc.identifier.issn 1471-2156 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4874479
dc.description.abstract Background: 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.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1471-2156-9-6 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248205/pdf/ en_US
dash.license LAA
dc.title A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Genetics en_US
dash.depositing.author Doria, Alessandro
dc.date.available 2011-04-22T19:35:27Z
dash.affiliation.other SPH^Epidemiology en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US

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