Document Detail


Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.
MedLine Citation:
PMID:  23334152     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Eric O Johnson; Dana B Hancock; Joshua L Levy; Nathan C Gaddis; Nancy L Saccone; Laura J Bierut; Grier P Page
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2013-01-22
Journal Detail:
Title:  Human genetics     Volume:  132     ISSN:  1432-1203     ISO Abbreviation:  Hum. Genet.     Publication Date:  2013 May 
Date Detail:
Created Date:  2013-04-15     Completed Date:  2013-06-18     Revised Date:  2014-05-07    
Medline Journal Info:
Nlm Unique ID:  7613873     Medline TA:  Hum Genet     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  509-22     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
African Americans / genetics*
Algorithms
Bias (Epidemiology)
European Continental Ancestry Group / genetics*
Female
Gene Frequency
Genome, Human / genetics*
Genome-Wide Association Study*
Genotype
HapMap Project
Haplotypes
Humans
Male
Models, Statistical
Oligonucleotide Array Sequence Analysis
Phenotype
Polymorphism, Single Nucleotide / genetics*
Grant Support
ID/Acronym/Agency:
P01 CA089392/CA/NCI NIH HHS; R01 DA013423/DA/NIDA NIH HHS; R01 DA025888/DA/NIDA NIH HHS; R01 DA026141/DA/NIDA NIH HHS; R01DA025888/DA/NIDA NIH HHS; R01DA026141/DA/NIDA NIH HHS; R33 DA027486/DA/NIDA NIH HHS; R33DA027486/DA/NIDA NIH HHS; U01 HG004422/HG/NHGRI NIH HHS; U01 HG004446/HG/NHGRI NIH HHS; U10 AA008401/AA/NIAAA NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Direct detection of sulfide ions [S(2-)] in aqueous media based on fluorescence quenching of functio...
Next Document:  Toll-like receptor signaling and regulation of intestinal immunity.