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Understanding HLA associations from SNP summary association statistics
Jiwoo Lim1, Sang-Cheol Bae2, Kwangwoo Kim3
1Department of Biology, Kyung Hee University, Seoul, Republic of Korea.
Scientific Reports
|February 6, 2019
Summary
A new method, DISH, imputes human leukocyte antigen (HLA) variant associations from SNP summary statistics. This approach aids in identifying disease-associated HLA variants while protecting individual genetic data.
Area of Science:
- Genetics
- Immunology
- Statistical Genetics
Background:
- Human leukocyte antigen (HLA) genes show strong associations with immune disorders.
- Current imputation methods for HLA variants require individual-level SNP genotype or reference haplotype data, posing accessibility challenges.
Purpose of the Study:
- To develop and evaluate DISH (direct imputing summary association statistics of HLA variants), a novel method for imputing HLA variant associations from SNP summary statistics.
- To assess the utility of DISH in identifying disease-associated HLA variants while preserving data privacy.
Main Methods:
- DISH utilizes linkage disequilibria to impute summary association statistics of HLA variants from SNP summary association statistics.
- The method was evaluated using null model datasets and applied to genome-wide association study (GWAS) datasets for systemic lupus erythematosus and rheumatoid arthritis.
Main Results:
- DISH demonstrated high correlations between its imputed Z scores and those from traditional HLA genotype imputations in both Asian (r=0.934) and European (r=0.960) populations.
- Application to GWAS datasets confirmed high correlation and identified consistent disease-susceptible and protective alleles compared to HLA genotype imputations.
Conclusions:
- DISH is a valuable tool for imputing HLA variant associations from SNP summary statistics, overcoming data accessibility limitations.
- The method effectively identifies disease-associated HLA variants and aids in understanding their role in human immune disorders without compromising individual data security.
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