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Gene-Interaction-Sensitive enrichment analysis in congenital heart disease
BACKGROUND: Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived b...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841104/ https://www.ncbi.nlm.nih.gov/pubmed/35151364 http://dx.doi.org/10.1186/s13040-022-00287-w |
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author | Woodward, Alexa A. Taylor, Deanne M. Goldmuntz, Elizabeth Mitchell, Laura E. Agopian, A.J. Moore, Jason H. Urbanowicz, Ryan J. |
author_facet | Woodward, Alexa A. Taylor, Deanne M. Goldmuntz, Elizabeth Mitchell, Laura E. Agopian, A.J. Moore, Jason H. Urbanowicz, Ryan J. |
author_sort | Woodward, Alexa A. |
collection | PubMed |
description | BACKGROUND: Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ(2) rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs). RESULTS: In the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development. CONCLUSIONS: Our results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13040-022-00287-w). |
format | Online Article Text |
id | pubmed-8841104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88411042022-02-16 Gene-Interaction-Sensitive enrichment analysis in congenital heart disease Woodward, Alexa A. Taylor, Deanne M. Goldmuntz, Elizabeth Mitchell, Laura E. Agopian, A.J. Moore, Jason H. Urbanowicz, Ryan J. BioData Min Methodology BACKGROUND: Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ(2) rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs). RESULTS: In the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development. CONCLUSIONS: Our results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13040-022-00287-w). BioMed Central 2022-02-12 /pmc/articles/PMC8841104/ /pubmed/35151364 http://dx.doi.org/10.1186/s13040-022-00287-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Methodology Woodward, Alexa A. Taylor, Deanne M. Goldmuntz, Elizabeth Mitchell, Laura E. Agopian, A.J. Moore, Jason H. Urbanowicz, Ryan J. Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title | Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title_full | Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title_fullStr | Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title_full_unstemmed | Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title_short | Gene-Interaction-Sensitive enrichment analysis in congenital heart disease |
title_sort | gene-interaction-sensitive enrichment analysis in congenital heart disease |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841104/ https://www.ncbi.nlm.nih.gov/pubmed/35151364 http://dx.doi.org/10.1186/s13040-022-00287-w |
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