Dataset Information

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.

ABSTRACT: Genotype errors are well known to increase type I errors and/or decrease power in related tests of genotype-phenotype association, depending on whether the genotype error mechanism is associated with the phenotype. These relationships hold for both single and multimarker tests of genotype-phenotype association. To assess the potential for genotype errors in Genetic Analysis Workshop 18 (GAW18) data, where no gold standard genotype calls are available, we explored concordance rates between sequencing, imputation, and microarray genotype calls. Our analysis shows that missing data rates for sequenced individuals are high and that there is a modest amount of called genotype discordance between the 2 platforms, with discordance most common for lower minor allele frequency (MAF) single-nucleotide polymorphisms (SNPs). Some evidence for discordance rates that were different between phenotypes was observed, and we identified a number of cases where different technologies identified different bases at the variant site. Type I errors and power loss is possible as a result of missing genotypes and errors in called genotypes in downstream analysis of GAW18 data.

SUBMITTER: Rogers A

PROVIDER: S-EPMC4143748 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.

Rogers Ally A Beck Andrew A Tintle Nathan L NL

BMC proceedings 20140617 Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo

Genotype errors are well known to increase type I errors and/or decrease power in related tests of genotype-phenotype association, depending on whether the genotype error mechanism is associated with the phenotype. These relationships hold for both single and multimarker tests of genotype-phenotype association. To assess the potential for genotype errors in Genetic Analysis Workshop 18 (GAW18) data, where no gold standard genotype calls are available, we explored concordance rates between sequen ...[more]

PMID: 25519374

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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

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.

Publications

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.

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