ArticleThe pharmacogenomics journal2010
An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm.
Article in The pharmacogenomics journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A genomic data archive from the Network for Pancreatic Organ donors with Diabetes.Scientific data · 2023Article
- The FDA's Experience with Emerging Genomics Technologies-Past, Present, and Future.The AAPS journal · 2016Review
- M(3)-S: a genotype calling method incorporating information from samples with known genotypes.BMC bioinformatics · 2015Article
- Getting DNA copy numbers without control samples.Algorithms for molecular biology : AMB · 2012Article
- M(3): an improved SNP calling algorithm for Illumina BeadArray data.Bioinformatics (Oxford, England) · 2012Article
- Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine.PloS one · 2011Article
- A machine learning pipeline for quantitative phenotype prediction from genotype data.BMC bioinformatics · 2010Article
- Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies.The pharmacogenomics journal · 2010Article
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4 authors.
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Abstract
The discordance in results between independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. To identify the causes of variability underlying lack of reproducibility, here we present the results of a repeatability experiment on GWAS on a cohort of 1991 coronary artery disease individuals and 1500 controls (National Blood Service) provided by the Wellcome Trust Case Control Consortium. As part of the MicroArray Quality Control project, we identified quality control (QC) and association analysis steps with a major impact on the identification of candidate markers for possible classifiers. Different experimental conditions were used with the CHIAMO calling algorithm to assess the effects of batch size and case-control composition on downstream association analysis. Results showed that both composition and size create discordant single-nucleotide polymorphism (SNP) results for QC and statistical analysis and may contribute to the lack of reproducibility in GWAS. An interactive effect of batch size and composition contributes to discordant results in significantly associated loci. About 800 significant SNPs (Cochran-Armitage trend test, P<5.0 x 10(-7)) were found for batches of 2000 samples with separated cases and controls, whereas only 14 significant markers were found with one batch of all samples.
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