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SNPxE: SNP-environment interaction pattern identifier

BACKGROUND: Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluatin...

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Autores principales: Lin, Hui-Yi, Huang, Po-Yu, Tseng, Tung-Sung, Park, Jong Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425112/
https://www.ncbi.nlm.nih.gov/pubmed/34493206
http://dx.doi.org/10.1186/s12859-021-04326-x
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author Lin, Hui-Yi
Huang, Po-Yu
Tseng, Tung-Sung
Park, Jong Y.
author_facet Lin, Hui-Yi
Huang, Po-Yu
Tseng, Tung-Sung
Park, Jong Y.
author_sort Lin, Hui-Yi
collection PubMed
description BACKGROUND: Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. RESULTS: SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. CONCLUSIONS: SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04326-x.
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spelling pubmed-84251122021-09-10 SNPxE: SNP-environment interaction pattern identifier Lin, Hui-Yi Huang, Po-Yu Tseng, Tung-Sung Park, Jong Y. BMC Bioinformatics Software BACKGROUND: Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. RESULTS: SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. CONCLUSIONS: SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04326-x. BioMed Central 2021-09-07 /pmc/articles/PMC8425112/ /pubmed/34493206 http://dx.doi.org/10.1186/s12859-021-04326-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Lin, Hui-Yi
Huang, Po-Yu
Tseng, Tung-Sung
Park, Jong Y.
SNPxE: SNP-environment interaction pattern identifier
title SNPxE: SNP-environment interaction pattern identifier
title_full SNPxE: SNP-environment interaction pattern identifier
title_fullStr SNPxE: SNP-environment interaction pattern identifier
title_full_unstemmed SNPxE: SNP-environment interaction pattern identifier
title_short SNPxE: SNP-environment interaction pattern identifier
title_sort snpxe: snp-environment interaction pattern identifier
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425112/
https://www.ncbi.nlm.nih.gov/pubmed/34493206
http://dx.doi.org/10.1186/s12859-021-04326-x
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AT tsengtungsung snpxesnpenvironmentinteractionpatternidentifier
AT parkjongy snpxesnpenvironmentinteractionpatternidentifier