You are here

Eur J Hum Genet DOI:10.1038/ejhg.2013.308

Accuracy of imputation to infer unobserved APOE epsilon alleles in genome-wide genotyping data.

Publication TypeJournal Article
Year of Publication2014
AuthorsRadmanesh, F, Devan, WJ, Anderson, CD, Rosand, J, Falcone, GJ
Corporate AuthorsAlzheimer’s Disease Neuroimaging Initiative (ADNI)
JournalEur J Hum Genet
Volume22
Issue10
Pages1239-42
Date Published2014 Oct
ISSN1476-5438
KeywordsAlleles, Alzheimer Disease, Apolipoproteins E, Case-Control Studies, Cerebral Hemorrhage, European Continental Ancestry Group, Gene Frequency, Genome, Human, Genome-Wide Association Study, Genotype, Genotyping Techniques, HapMap Project, Humans, Logistic Models, Longitudinal Studies, Polymorphism, Single Nucleotide, Principal Component Analysis, Prospective Studies, Quality Control
Abstract

Apolipoprotein E, encoded by APOE, is the main apoprotein for catabolism of chylomicrons and very low density lipoprotein. Two common single-nucleotide polymorphisms (SNPs) in APOE, rs429358 and rs7412, determine the three epsilon alleles that are established genetic risk factors for late-onset Alzheimer's disease (AD), cerebral amyloid angiopathy, and intracerebral hemorrhage (ICH). These two SNPs are not present in most commercially available genome-wide genotyping arrays and cannot be inferred through imputation using HapMap reference panels. Therefore, these SNPs are often separately genotyped. Introduction of reference panels compiled from the 1000 Genomes project has made imputation of these variants possible. We compared the directly genotyped and imputed SNPs that define the APOE epsilon alleles to determine the accuracy of imputation for inference of unobserved epsilon alleles. We utilized genome-wide genotype data obtained from two cohorts of ICH and AD constituting subjects of European ancestry. Our data suggest that imputation is highly accurate, yields an acceptable proportion of missing data that is non-differentially distributed across case and control groups, and generates comparable results to genotyped data for hypothesis testing. Further, we explored the effect of imputation algorithm parameters and demonstrated that customization of these parameters yields an improved balance between accuracy and missing data for inferred genotypes.

URLhttp://dx.doi.org/10.1038/ejhg.2013.308
DOI10.1038/ejhg.2013.308
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/24448547?dopt=Abstract

Alternate JournalEur. J. Hum. Genet.
PubMed ID24448547
PubMed Central IDPMC4169533
Grant ListK01 AG030514 / AG / NIA NIH HHS / United States
K23 NS086873 / NS / NINDS NIH HHS / United States
U24 AG021886 / AG / NIA NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
P50NS061343 / NS / NINDS NIH HHS / United States
P50 NS051343 / NS / NINDS NIH HHS / United States
/ / Canadian Institutes of Health Research / Canada
R01 NS059727 / NS / NINDS NIH HHS / United States
R01NS059727 / NS / NINDS NIH HHS / United States
M01 RR000042 / RR / NCRR NIH HHS / United States
P30 AG010129 / AG / NIA NIH HHS / United States