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Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distribut...

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Detalles Bibliográficos
Autores principales: Davis, N A, Crowe, J E, Pajewski, N M, McKinney, B A
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001955/
https://www.ncbi.nlm.nih.gov/pubmed/20613780
http://dx.doi.org/10.1038/gene.2010.37
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author Davis, N A
Crowe, J E
Pajewski, N M
McKinney, B A
author_facet Davis, N A
Crowe, J E
Pajewski, N M
McKinney, B A
author_sort Davis, N A
collection PubMed
description The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene–gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene–gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor α (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response.
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spelling pubmed-30019552010-12-17 Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine Davis, N A Crowe, J E Pajewski, N M McKinney, B A Genes Immun Original Article The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene–gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene–gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor α (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response. Nature Publishing Group 2010-12 2010-07-08 /pmc/articles/PMC3001955/ /pubmed/20613780 http://dx.doi.org/10.1038/gene.2010.37 Text en Copyright © 2010 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under the Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Original Article
Davis, N A
Crowe, J E
Pajewski, N M
McKinney, B A
Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title_full Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title_fullStr Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title_full_unstemmed Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title_short Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
title_sort surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001955/
https://www.ncbi.nlm.nih.gov/pubmed/20613780
http://dx.doi.org/10.1038/gene.2010.37
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