Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes
Abstract Background As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant ana...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2066-9 |
| _version_ | 1818450235486109696 |
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| author | Sungyoung Lee Yongkang Kim Sungkyoung Choi Heungsun Hwang Taesung Park |
| author_facet | Sungyoung Lee Yongkang Kim Sungkyoung Choi Heungsun Hwang Taesung Park |
| author_sort | Sungyoung Lee |
| collection | DOAJ |
| description | Abstract Background As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Results Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. Conclusions Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously. |
| first_indexed | 2024-12-14T20:48:05Z |
| format | Article |
| id | doaj.art-a0e86bd6ef8f4fc3b8239d76231eb2b6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T20:48:05Z |
| publishDate | 2018-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a0e86bd6ef8f4fc3b8239d76231eb2b62022-12-21T22:47:57ZengBMCBMC Bioinformatics1471-21052018-05-0119S4859710.1186/s12859-018-2066-9Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypesSungyoung Lee0Yongkang Kim1Sungkyoung Choi2Heungsun Hwang3Taesung Park4Interdisciplinary Program in Bioinformatics, Seoul National UniversityDepartment of Statistics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityDepartment of Psychology, McGill UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityAbstract Background As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Results Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. Conclusions Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously.http://link.springer.com/article/10.1186/s12859-018-2066-9Pathway-based analysisNext-generation sequencing dataMultivariate analysisGeneralized structured component analysisHierarchical analysis |
| spellingShingle | Sungyoung Lee Yongkang Kim Sungkyoung Choi Heungsun Hwang Taesung Park Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes BMC Bioinformatics Pathway-based analysis Next-generation sequencing data Multivariate analysis Generalized structured component analysis Hierarchical analysis |
| title | Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| title_full | Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| title_fullStr | Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| title_full_unstemmed | Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| title_short | Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| title_sort | pathway based approach using hierarchical components of rare variants to analyze multiple phenotypes |
| topic | Pathway-based analysis Next-generation sequencing data Multivariate analysis Generalized structured component analysis Hierarchical analysis |
| url | http://link.springer.com/article/10.1186/s12859-018-2066-9 |
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