70,724名个体的自我报告健康史揭示了HLA与过敏及其他常被低报疾病的新关联

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中文摘要

背景： 位于人类6号染色体6p的HLA基因座变异与数百种疾病和状况相关。然而，HLA系统高度多态性，加之大多数表型效应量通常较小，需要相对较大的样本量才能为关联研究提供足够把握度；同时，高分辨率HLA基因分型仍然相对耗费资源。这些限制阻碍了新关联的发现。尽管在拥有电子健康记录（EHR）的大型登记队列中开展的全表型关联研究（PheWAS）已揭示HLA在疾病中作用的新见解，但许多常见健康状况因其发生的时间特性或普遍低报而在EHR中代表性不足。此外，这些研究通常使用从微阵列数据推断的HLA基因分型数据，而非直接测量高分辨率基因型。
目的： 为克服这些限制并揭示新的HLA关联，我们在许多此前研究不足的健康状况中开展了PheWAS。
方法： 我们在70,724名登记于NMDP（原国家骨髓捐献者计划）且具有高分辨率HLA基因分型（HLA-A、HLA-B、HLA-C、HLA-DRB1和HLA-DQB1）的受试者中，查询了超过290种状况、疾病和特征。在根据自我报告的地理血统进行分层后，我们进行了校正性别和年龄的logistic回归分析，以评估HLA-表型关联。
结果： 我们在自我报告的地理血统群体中识别出48个显著的HLA关联，证实了若干已知关联，并发现了14个新关联。大多数新关联涉及在EHR中常被低报的常见感染性或过敏性表型。我们发现HLA-DRB1*04:01与自我报告的、机制未经证实的头孢克洛不良反应标签之间存在一个此前未被充分认识但非常强的关联（OR = 3.74，p值 5.10E-28），在排除报告多种药物超敏反应的个体后该关联仍高度显著（OR = 3.13，p值 = 5.60E-15）。探索性计算共折叠结构与亲和力预测识别出头孢克洛与HLA-DRB1*04:01的P4口袋之间可能存在相互作用，提供了产生假设的结构学依据，但需要功能验证。若在具有临床特征化表型的独立队列和功能研究中得到验证，该关联可能有助于药物基因组学风险分层和药物不良反应的靶向预防。其他新关联，如对生殖器疣（HPV）和过敏性鼻炎的易感性，揭示了对特定HLA等位基因在免疫介导疾病中作用的新见解。许多这些新关联在独立的All of Us队列（AoU）中得到重复，而其他关联则显示相同的关联趋势，证实了该方法的有效性。
结论： 总体而言，我们的发现证明了将群体规模的高分辨率HLA基因型与超越EHR的表型分析相结合，以揭示免疫遗传因素对常见健康结局影响的价值。这些发现推动未来开展功能研究并在临床特征化队列中验证，以完善机制理解并确定潜在的临床效用。

英文摘要

BACKGROUND: Variation in the HLA loci, located on human chromosome 6p, has been associated with hundreds of diseases and conditions. However, high levels of polymorphism that characterize the HLA system, coupled with generally modest effect sizes for most phenotypes, necessitate relatively large sample sizes to power association studies; meanwhile, high resolution HLA genotyping remains relatively resource intensive. These constraints limit identification of novel associations. While phenome-wide association studies (PheWAS) in the context of large registries with available electronic health records (EHR) have revealed new insights into the role of HLA in disease, many common health conditions are poorly represented in EHR due to the temporal nature of their occurrence or general underreporting. Further, these studies have generally been conducted with HLA genotyping data imputed from microarrays, rather than direct measurement of high-resolution genotypes.
OBJECTIVE: To overcome these limitations and reveal novel HLA associations we undertook a PheWAS in many previously understudied health conditions.
METHODS: We queried over 290 conditions, diseases and traits from 70,724 subjects registered with NMDP (formerly known as the National Marrow Donor Program) with available high-resolution HLA genotyping (HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1). After stratifying according to self-reported geographic ancestry, we performed a logistic regression analysis adjusting for sex and age for HLA-phenotype association.
RESULTS: We identified 48 significant HLA associations across self-reported geographic ancestry groups, confirming several known associations and uncovered fourteen novel associations. Most novel associations pertained to common infectious or allergic phenotypes that often go under-reported in the EHR. We identified a previously underappreciated yet very strong association between HLA-DRB1*04:01 and self-reported cefaclor adverse-reaction label of unverified mechanism (OR = 3.74, p-value 5.10E-28), which remained highly significant after excluding individuals reporting multiple drug hypersensitivities (OR = 3.13, p-value = 5.60E-15). Exploratory computational co-folding structure-and-affinity prediction identified a potential interaction between cefaclor and the P4 pocket of HLA-DRB1*04:01, providing a hypothesis-generating structural rationale that requires functional validation. If validated in independent cohorts with clinically characterized phenotypes and functional studies, this association may inform pharmacogenomic risk stratification and targeted prevention of adverse drug reactions. Other novel associations found, such as susceptibility to genital warts (HPV) and allergic rhinitis, reveal new insights into the role of specific HLA alleles in immune-mediated disease. Many of these novel associations were replicated in the independent All of Us cohort (AoU), while others showed the same trend for association, confirming the validity of this approach.
CONCLUSION: Collectively, our findings demonstrate the value of integrating population-scale, high-resolution HLA genotypes with phenotyping beyond the EHR to reveal immunogenetic influences on common health outcomes. These findings motivate future functional studies and validation in clinically characterized cohorts to refine mechanistic understanding and determine potential clinical utility.