Understanding HLA associations from SNP summary association statistics

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...

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Bibliographic Details
Published in:Scientific reports 2019-02, Vol.9 (1), p.1337-1337, Article 1337
Main Authors: Lim, Jiwoo, Bae, Sang-Cheol, Kim, Kwangwoo
Format: Article
Language:English
Subjects:
Disease
Gene polymorphism
Haplotypes
Histocompatibility antigen HLA
Rheumatoid arthritis
Single-nucleotide polymorphism
Statistics
Systemic lupus erythematosus
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Summary: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 null 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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-37840-9