Assessing HLA imputation accuracy in a West African population

Abstract

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We, therefore, sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from 315 Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes (1kg-All), 1000 Genomes African (1kg-Afr), 1000 Genomes Gambian (1kg-Gwd), H3Africa, and the HLA Multi-ethnic datasets. HLA-A, HLA-B, and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA, and Minimac4, and concordance rate was used as an assessment metric. The best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel, with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in a West African population.

Fig 1. Diagrammatic representation of the HLA region [3].

Fig 2. A summary of the materials and methods used.

Fig 3. Concordance rate based on reference panels.

Fig 4. Allele frequency vs. accuracy of HIBAG.

Accuracy tended to decrease with increasing frequency, especially for HLA-B alleles.

Fig 5. Imputation accuracy comparison based on error rates.

Results from HIBAG (Fig 5B) showed that HLA-B had a higher error rate, followed by HLA-A and HLA-C. HLA-B imputation was less accurate for SNP2HLA (Fig 5C), followed by HLA-C and HLA-A. HLA-A had higher error rates for Minimac4 (Fig 5D) and CookHLA (Fig 5A), followed by HLA-B and HLA-C alleles.