| Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy. | |
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MedLine Citation:
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PMID: 23334152 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality. |
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Authors:
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Eric O Johnson; Dana B Hancock; Joshua L Levy; Nathan C Gaddis; Nancy L Saccone; Laura J Bierut; Grier P Page |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2013-1-22 |
Journal Detail:
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Title: Human genetics Volume: - ISSN: 1432-1203 ISO Abbreviation: Hum. Genet. Publication Date: 2013 Jan |
Date Detail:
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Created Date: 2013-1-21 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7613873 Medline TA: Hum Genet Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Behavioral Health Epidemiology Program, RTI International, 3040 Cornwallis Road, PO Box 12194, Research Triangle Park, NC, 27709-12194, USA, ejohnson@rti.org. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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