Cited 10 times in
HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최성경 | - |
dc.date.accessioned | 2019-05-29T05:14:06Z | - |
dc.date.available | 2019-05-29T05:14:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0219-7200 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/169475 | - |
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 × SPOCK1) and (LINGO2 × 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 | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | World Scientific Publishing Europe | - |
dc.relation.isPartOf | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Yonsei Biomedical Research Center (연세의생명연구원) | - |
dc.contributor.googleauthor | Sungkyoung Choi | - |
dc.contributor.googleauthor | Sungyoung Lee | - |
dc.contributor.googleauthor | Yongkang Kim | - |
dc.contributor.googleauthor | Heungsun Hwang | - |
dc.contributor.googleauthor | Taesung Park | - |
dc.identifier.doi | 10.1142/S0219720018400267 | - |
dc.contributor.localId | A05717 | - |
dc.relation.journalcode | J03605 | - |
dc.identifier.eissn | 1757-6334 | - |
dc.identifier.pmid | 30567476 | - |
dc.identifier.url | https://www.worldscientific.com/doi/abs/10.1142/S0219720018400267 | - |
dc.subject.keyword | Genome-wide association study | - |
dc.subject.keyword | generalized structured component analysis | - |
dc.subject.keyword | gene–gene interactions | - |
dc.subject.keyword | ridge regression | - |
dc.contributor.alternativeName | Choi, Sungkyoung | - |
dc.contributor.affiliatedAuthor | 최성경 | - |
dc.citation.volume | 16 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1840026 | - |
dc.identifier.bibliographicCitation | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY , Vol.16(6) : 1840026, 2018 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.