In:
HLA, Wiley, Vol. 103, No. 6 ( 2024-06)
Abstract:
The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome‐wide association studies have identified numerous disease‐associated SNPs within this region. However, these associations do not fully capture the immune‐biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi‐ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole‐genome sequencing data. Using HIBAG, we created three reference panels: 1KG ( n = 2504), SABE ( n = 1171), and the full model ( n = 3675) encompassing all samples. In extensive cross‐validation of these reference panels, the multi‐ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non‐classical, MICA , MICB and HLA‐H genes, previously unavailable for multi‐ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA‐B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.
Type of Medium:
Online Resource
ISSN:
2059-2302
,
2059-2310
Language:
English
Publisher:
Wiley
Publication Date:
2024
detail.hit.zdb_id:
2844321-4
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