Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 배상철 | - |
dc.date.accessioned | 2019-07-17T06:23:52Z | - |
dc.date.available | 2019-07-17T06:23:52Z | - |
dc.date.issued | 2019-02 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v. 9, Page. 1-5 | en_US |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://www.nature.com/articles/s41598-018-37840-9 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/107536 | - |
dc.description.abstract | Strong genetic associations in the region containing human leukocyte antigen (HLA) genes have been well-documented in various human immune disorders. Imputation methods to infer HLA variants from single nucleotide polymorphism (SNP) genotypes are currently used to understand HLA associations with a trait of interest. However, it is challenging for some researchers to obtain individual-level SNP genotype data or reference haplotype data. In this study, we developed and evaluated a new method, DISH (direct imputing summary association statistics of HLA variants), for imputing summary association statistics of HLA variants from SNP summary association statistics based on linkage disequilibria in Asian and European populations. Disease association Z scores in DISH were highly correlated with those from imputed HLA genotypes in model datasets (r = 0.934 in Asians; r = 0.960 in Europeans). We applied DISH to two previous GWAS datasets in Asian systemic lupus erythematosus and European rheumatoid arthritis populations. There was a high correlation between Z scores in the DISH and HLA genotype imputations, showing the same disease-susceptible and protective alleles. This study illustrated the usefulness of the DISH method in understanding and identifying disease-associated HLA variants in human diseases while maintaining individual-level data security. | en_US |
dc.description.sponsorship | This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2017R1E1A1A01076388) and Korea Healthcare Technology R&D Project funded by the Ministry for Health & Welfare (HI15C3182) in the Republic of Korea. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk.Funding for the project was provided by the Wellcome Trust under award 076113, 085475 and 090355. The Consortium and/or Individual Investigators bear no responsibility for the further analysis or interpretation of these data, over and above that published by the Consortium. | en_US |
dc.language.iso | en | en_US |
dc.publisher | NATURE PUBLISHING GROUP | en_US |
dc.subject | DIRECT IMPUTATION | en_US |
dc.subject | MHC | en_US |
dc.subject | ALLELES | en_US |
dc.subject | EXPLAIN | en_US |
dc.title | Understanding HLA associations from SNP summary association statistics | en_US |
dc.type | Article | en_US |
dc.relation.volume | 9 | - |
dc.identifier.doi | 10.1038/s41598-018-37840-9 | - |
dc.relation.page | 1337-1337 | - |
dc.relation.journal | SCIENTIFIC REPORTS | - |
dc.contributor.googleauthor | Lim, Jiwoo | - |
dc.contributor.googleauthor | Bae, Sang-Cheol | - |
dc.contributor.googleauthor | Kim, Kwangwoo | - |
dc.relation.code | 2019002548 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF MEDICINE[S] | - |
dc.sector.department | DEPARTMENT OF MEDICINE | - |
dc.identifier.pid | scbae | - |
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