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Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928027/ https://www.ncbi.nlm.nih.gov/pubmed/20368714 http://dx.doi.org/10.1038/tpj.2010.24 |
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author | Hong, H Shi, L Su, Z Ge, W Jones, W D Czika, W Miclaus, K Lambert, C G Vega, S C Zhang, J Ning, B Liu, J Green, B Xu, L Fang, H Perkins, R Lin, S M Jafari, N Park, K Ahn, T Chierici, M Furlanello, C Zhang, L Wolfinger, R D Goodsaid, F Tong, W |
author_facet | Hong, H Shi, L Su, Z Ge, W Jones, W D Czika, W Miclaus, K Lambert, C G Vega, S C Zhang, J Ning, B Liu, J Green, B Xu, L Fang, H Perkins, R Lin, S M Jafari, N Park, K Ahn, T Chierici, M Furlanello, C Zhang, L Wolfinger, R D Goodsaid, F Tong, W |
author_sort | Hong, H |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2928027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-29280272010-08-25 Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples Hong, H Shi, L Su, Z Ge, W Jones, W D Czika, W Miclaus, K Lambert, C G Vega, S C Zhang, J Ning, B Liu, J Green, B Xu, L Fang, H Perkins, R Lin, S M Jafari, N Park, K Ahn, T Chierici, M Furlanello, C Zhang, L Wolfinger, R D Goodsaid, F Tong, W Pharmacogenomics J Original Article 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. Nature Publishing Group 2010-08 2010-04-06 /pmc/articles/PMC2928027/ /pubmed/20368714 http://dx.doi.org/10.1038/tpj.2010.24 Text en Copyright © 2010 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
spellingShingle | Original Article Hong, H Shi, L Su, Z Ge, W Jones, W D Czika, W Miclaus, K Lambert, C G Vega, S C Zhang, J Ning, B Liu, J Green, B Xu, L Fang, H Perkins, R Lin, S M Jafari, N Park, K Ahn, T Chierici, M Furlanello, C Zhang, L Wolfinger, R D Goodsaid, F Tong, W Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title | Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title_full | Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title_fullStr | Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title_full_unstemmed | Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title_short | Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples |
title_sort | assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with hapmap samples |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928027/ https://www.ncbi.nlm.nih.gov/pubmed/20368714 http://dx.doi.org/10.1038/tpj.2010.24 |
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