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Jackknife-based gene-gene interactiontests for untyped SNPs

BACKGROUND: Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test fo...

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Autor principal: Song, Minsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506584/
https://www.ncbi.nlm.nih.gov/pubmed/26187382
http://dx.doi.org/10.1186/s12863-015-0225-9
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author Song, Minsun
author_facet Song, Minsun
author_sort Song, Minsun
collection PubMed
description BACKGROUND: Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy. RESULTS: In this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest. CONCLUSIONS: As demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0225-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-45065842015-07-19 Jackknife-based gene-gene interactiontests for untyped SNPs Song, Minsun BMC Genet Methodology Article BACKGROUND: Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy. RESULTS: In this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest. CONCLUSIONS: As demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0225-9) contains supplementary material, which is available to authorized users. BioMed Central 2015-07-18 /pmc/articles/PMC4506584/ /pubmed/26187382 http://dx.doi.org/10.1186/s12863-015-0225-9 Text en © Song. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Song, Minsun
Jackknife-based gene-gene interactiontests for untyped SNPs
title Jackknife-based gene-gene interactiontests for untyped SNPs
title_full Jackknife-based gene-gene interactiontests for untyped SNPs
title_fullStr Jackknife-based gene-gene interactiontests for untyped SNPs
title_full_unstemmed Jackknife-based gene-gene interactiontests for untyped SNPs
title_short Jackknife-based gene-gene interactiontests for untyped SNPs
title_sort jackknife-based gene-gene interactiontests for untyped snps
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506584/
https://www.ncbi.nlm.nih.gov/pubmed/26187382
http://dx.doi.org/10.1186/s12863-015-0225-9
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