Allele-specific concordance rate.

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.人类白细胞抗原（Human Leukocyte Antigen, HLA）区域在自身免疫病与感染性疾病中发挥重要作用。HLA区域具有高度多态性，因此基因型填充难度较高。鉴于西非人群研究不足且遗传多样性丰富，本研究旨在评估该人群的HLA基因型填充准确性。本研究的研究队列选自冈比亚基因组变异项目（Gambian Genome Variation Project, GGVP）全基因组测序数据集的315名冈比亚个体。本研究评估了两款基因分型芯片——Illumina Omni 2.5芯片与非洲人类遗传与健康计划（Human Hereditary and Health in Africa, H3Africa）芯片的标记适配性，并使用二者测试了多款基因型填充参考面板与分析工具。参考面板选自千人基因组计划（1kg-All）、千人基因组非洲亚组（1kg-Afr）、千人基因组冈比亚亚组（1kg-Gwd）、H3Africa数据集以及HLA多族群数据集。使用HIBAG、SNP2HLA、CookHLA及Minimac4四款工具对HLA-A、HLA-B及HLA-C等位基因进行基因型填充，以一致性率作为评估指标。研究结果显示，表现最优的工具为HIBAG，一致性率达0.84；表现最优的参考面板为H3Africa面板，一致性率达0.62。相较于HIBAG（0.71）、SNP2HLA（0.51）与CookHLA（0.17），Minimac4（0.75）可提升HLA-B等位基因的基因型填充准确性。H3Africa与Illumina Omni 2.5芯片的分型表现相当，提示在西非人群中，基因分型芯片对HLA基因型填充的影响较小。本研究结果表明，采用更大规模的人群特异性参考面板搭配HIBAG工具，可提升西非人群的HLA基因型填充准确性。