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Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples.

ABSTRACT: The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.

SUBMITTER: Hong H

PROVIDER: S-EPMC2928027 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples.

Hong H H Shi L L Su Z Z Ge W W Jones W D WD Czika W W Miclaus K K Lambert C G CG Vega S C SC Zhang J J Ning B B Liu J J Green B B Xu L L Fang H H Perkins R R Lin S M SM Jafari N N Park K K Ahn T T Chierici M M Furlanello C C Zhang L L Wolfinger R D RD Goodsaid F F Tong W W

The pharmacogenomics journal 20100406 4

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the s ...[more]

PMID: 20368714

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Project description:With the completion of the HapMap project, a variety of computational algorithms and tools have been proposed for haplotype inference, tag SNP selection and genome-wide association studies. Simulated data are commonly used in evaluating these new developed approaches. In addition to simulations based on population models, empirical data generated by perturbing real data, has also been used because it may inherit specific properties from real data. However, there is no tool that is publicly available to generate large scale simulated variation data by taking into account knowledge from the HapMap project.A computer program (gs) was developed to quickly generate a large number of samples based on real data that are useful for a variety of purposes, including evaluating methods for haplotype inference, tag SNP selection and association studies. Two approaches have been implemented to generate dense SNP haplotype/genotype data that share similar local linkage disequilibrium (LD) patterns as those in human populations. The first approach takes haplotype pairs from samples as inputs, and the second approach takes patterns of haplotype block structures as inputs. Both quantitative and qualitative traits have been incorporated in the program. Phenotypes are generated based on a disease model, or based on the effect of a quantitative trait nucleotide, both of which can be specified by users. In addition to single-locus disease models, two-locus disease models have also been implemented that can incorporate any degree of epistasis. Users are allowed to specify all nine parameters in a 3 x 3 penetrance table. For several commonly used two-locus disease models, the program can automatically calculate penetrances based on the population prevalence and marginal effects of a disease that users can conveniently specify.The program gs can effectively generate large scale genetic and phenotypic variation data that can be used for evaluating new developed approaches. It is freely available from the authors' web site at http://www.eecs.case.edu/~jxl175/gs.html.

Dataset Information

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples.

Publications

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples.

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