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Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sungyoung | - |
dc.contributor.author | Kim, Sunmee | - |
dc.contributor.author | Kim, Yongkang | - |
dc.contributor.author | Oh, Bermseok | - |
dc.contributor.author | Hwang, Heungsun | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2019-07-16T07:52:48Z | - |
dc.date.available | 2019-07-16T16:55:05Z | - |
dc.date.issued | 2019-07-11 | - |
dc.identifier.citation | BMC Medical Genomics. 12(Suppl 5):100 | ko_KR |
dc.identifier.issn | 1755-8794 | - |
dc.identifier.uri | https://hdl.handle.net/10371/160363 | - |
dc.description.abstract | Backgrounds
Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. Results Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. Conclusion In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/. | ko_KR |
dc.description.sponsorship | Publication costs are funded by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) grant (HI16C2037). Also, this work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) grant (2013M3A9C4078158) and by grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165, HI16C2048). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Generalized estimating equations | ko_KR |
dc.subject | Clustered phenotypes | ko_KR |
dc.subject | Pathway analysis | ko_KR |
dc.subject | Rare variants | ko_KR |
dc.title | Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 이성용 | - |
dc.contributor.AlternativeAuthor | 김선미 | - |
dc.contributor.AlternativeAuthor | 김용강 | - |
dc.contributor.AlternativeAuthor | 오범석 | - |
dc.contributor.AlternativeAuthor | 황흥선 | - |
dc.contributor.AlternativeAuthor | 박태성 | - |
dc.identifier.doi | 10.1186/s12920-019-0517-4 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2019-07-15T13:13:37Z | - |
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