HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Sungkyoung | - |
dc.contributor.author | Lee, Sungyoung | - |
dc.contributor.author | Kim, Yongkang | - |
dc.contributor.author | Hwang, Heungsun | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2021-06-22T11:21:22Z | - |
dc.date.available | 2021-06-22T11:21:22Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.issn | 0219-7200 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/5092 | - |
dc.description.abstract | Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 x SPOCK1) and (LINGO2 x ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website (http://statgen.snu.ac.kr/software/hiscom-ggi). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Imperial College Press | - |
dc.title | HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Sungkyoung | - |
dc.identifier.doi | 10.1142/S0219720018400267 | - |
dc.identifier.scopusid | 2-s2.0-85058821898 | - |
dc.identifier.wosid | 000455392200005 | - |
dc.identifier.bibliographicCitation | Journal of Bioinformatics and Computational Biology, v.16, no.6(SI), pp.1 - 25 | - |
dc.relation.isPartOf | Journal of Bioinformatics and Computational Biology | - |
dc.citation.title | Journal of Bioinformatics and Computational Biology | - |
dc.citation.volume | 16 | - |
dc.citation.number | 6(SI) | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 25 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | MULTIFACTOR-DIMENSIONALITY REDUCTION | - |
dc.subject.keywordPlus | GENOME-WIDE ASSOCIATION | - |
dc.subject.keywordPlus | SNP-SNP INTERACTIONS | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordPlus | MISSING HERITABILITY | - |
dc.subject.keywordPlus | STRATEGIES | - |
dc.subject.keywordPlus | EPISTASIS | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | LOCI | - |
dc.subject.keywordPlus | POLYMORPHISMS | - |
dc.subject.keywordAuthor | Genome-wide association study | - |
dc.subject.keywordAuthor | gene-gene interactions | - |
dc.subject.keywordAuthor | generalized structured component analysis | - |
dc.subject.keywordAuthor | ridge regression | - |
dc.identifier.url | https://www.worldscientific.com/doi/abs/10.1142/S0219720018400267 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.