Optimal designs for two-stage genome-wide association studies
dc.contributor.author | Skol, Andrew D. | en_US |
dc.contributor.author | Scott, Laura J. | en_US |
dc.contributor.author | Abecasis, Gonçalo R. | en_US |
dc.contributor.author | Boehnke, Michael | en_US |
dc.date.accessioned | 2007-12-04T18:31:35Z | |
dc.date.available | 2008-11-05T15:05:43Z | en_US |
dc.date.issued | 2007-11 | en_US |
dc.identifier.citation | Skol, Andrew D.; Scott, Laura J.; Abecasis, GonÇalo R.; Boehnke, Michael (2007). "Optimal designs for two-stage genome-wide association studies." Genetic Epidemiology 31(7): 776-788. <http://hdl.handle.net/2027.42/57367> | en_US |
dc.identifier.issn | 0741-0395 | en_US |
dc.identifier.issn | 1098-2272 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/57367 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=17549752&dopt=citation | |
dc.description.abstract | Genome-wide association (GWA) studies require genotyping hundreds of thousands of markers on thousands of subjects, and are expensive at current genotyping costs. To conserve resources, many GWA studies are adopting a staged design in which a proportion of the available samples are genotyped on all markers in stage 1, and a proportion of these markers are genotyped on the remaining samples in stage 2. We describe a strategy for designing cost-effective two-stage GWA studies. Our strategy preserves much of the power of the corresponding one-stage design and minimizes the genotyping cost of the study while allowing for differences in per genotyping cost between stages 1 and 2. We show that the ratio of stage 2 to stage 1 per genotype cost can strongly influence both the optimal design and the genotyping cost of the study. Increasing the stage 2 per genotype cost shifts more of the genotyping and study cost to stage 1, and increases the cost of the study. This higher cost can be partially mitigated by adopting a design with reduced power while preserving the false positive rate or by increasing the false positive rate while preserving power. For example, reducing the power preserved in the two-stage design from 99 to 95% that of the one-stage design decreases the two-stage study cost by ∼15%. Alternatively, the same cost savings can be had by relaxing the false positive rate by 2.5-fold, for example from 1/300,000 to 2.5/300,000, while retaining the same power. Genet. Epidemiol . 2007. © 2007 Wiley-Liss, Inc. | en_US |
dc.format.extent | 326246 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Life and Medical Sciences | en_US |
dc.subject.other | Genetics | en_US |
dc.title | Optimal designs for two-stage genome-wide association studies | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Biological Chemistry | en_US |
dc.subject.hlbsecondlevel | Genetics | en_US |
dc.subject.hlbsecondlevel | Molecular, Cellular and Developmental Biology | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan ; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, Illinois ; Department of Medicine, Section of Genetic Medicine, University of Chicago, 5841 South Maryland Avenue, W611A – MC6091, Chicago, Illinois 60637 | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan | en_US |
dc.identifier.pmid | 17549752 | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/57367/1/20240_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/gepi.20240 | en_US |
dc.identifier.source | Genetic Epidemiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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