Dataset Information

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

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.

SUBMITTER: Radmanesh F

PROVIDER: S-EPMC4169533 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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

Radmanesh Farid F Devan William J WJ Anderson Christopher D CD Rosand Jonathan J Falcone Guido J GJ

European journal of human genetics : EJHG 20140122 10

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 inferre ...[more]

PMID: 24448547

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Project description:Well-powered genomic studies require genome-wide marker coverage across many individuals. For non-model species with few genomic resources, high-throughput sequencing (HTS) methods, such as Genotyping-By-Sequencing (GBS), offer an inexpensive alternative to array-based genotyping. Although affordable, datasets derived from HTS methods suffer from sequencing error, alignment errors, and missing data, all of which introduce noise and uncertainty to variant discovery and genotype calling. Under such circumstances, meaningful analysis of the data is difficult. Our primary interest lies in the issue of how one can accurately infer or impute missing genotypes in HTS-derived datasets. Many of the existing genotype imputation algorithms and software packages were primarily developed by and optimized for the human genetics community, a field where a complete and accurate reference genome has been constructed and SNP arrays have, in large part, been the common genotyping platform. We set out to answer two questions: 1) can we use existing imputation methods developed by the human genetics community to impute missing genotypes in datasets derived from non-human species and 2) are these methods, which were developed and optimized to impute ascertained variants, amenable for imputation of missing genotypes at HTS-derived variants? We selected Beagle v.4, a widely used algorithm within the human genetics community with reportedly high accuracy, to serve as our imputation contender. We performed a series of cross-validation experiments, using GBS data collected from the species Manihot esculenta by the Next Generation (NEXTGEN) Cassava Breeding Project. NEXTGEN currently imputes missing genotypes in their datasets using a LASSO-penalized, linear regression method (denoted 'glmnet'). We selected glmnet to serve as a benchmark imputation method for this reason. We obtained estimates of imputation accuracy by masking a subset of observed genotypes, imputing, and calculating the sample Pearson correlation between observed and imputed genotype dosages at the site and individual level; computation time served as a second metric for comparison. We then set out to examine factors affecting imputation accuracy, such as levels of missing data, read depth, minor allele frequency (MAF), and reference panel composition.

Dataset Information

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

Publications

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

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