key: cord-0980451-87gn2ohz
authors: Yu, Xueping; Ho, Kuoting; Shen, Zhongliang; Fu, Xiaoying; Huang, Hongbo; Wu, Delun; Lin, Yancheng; Lin, Yijian; Chen, Wenhuang; Su, Milong; Qiu, Chao; Zhuang, Xibin; Su, Zhijun
title: The Association of Human Leukocyte Antigen and COVID-19 in Southern China
date: 2021-08-06
journal: Open Forum Infect Dis
DOI: 10.1093/ofid/ofab410
sha: e412e4c1cc570e70aff5f09c3b507050b3647546
doc_id: 980451
cord_uid: 87gn2ohz

HLA polymorphism is hypothesized to be associated with diverse immune responses towards infectious diseases. Herein, by comparing against multiple subpopulation groups as control, we confirmed that HLA-B*15:27 and HLA-DRB1*04:06 were associated with COVID-19 susceptibility in China. Both alleles were predicted to have weak binding affinities towards viral proteins.

M a n u s c r i p t 4 Severe respiratory syndrome coronavirus 2 (SARS-CoV-2) has contributed to nearly 4 million deaths worldwide (1) . Delayed or inadequate treatment contributes to a large portion of COVID-19 mortality. Improving risk stratification and clinical management is crucial to identify the potentially vulnerable population. Several clinical features and genetic factors have been suggested to escalate risks of SARS-CoV-2 infection, the occurrence of severe conditions, and mortality (2) .

Human leukocyte antigen (HLA), the most polymorphic genetic locus, plays a pivotal role in viral antigen recognition and presentation (3) . After decades of study, several HLA alleles had been identified to be responsible for individual differences in host response, such as viral clearance and disease prognosis (4) (5) (6) (7) (8) . Thus far, a few HLA alleles have been reported to be predisposed to COVID-19 infection (9) (10) (11) (12) . However, these findings are difficult to replicate (13) . Beyond the reasons such as small sample size and HLA being population dependent, the leading cause is that these case-control studies are extra sensitive to population stratification.

A previous study pointed out that, even within the same ethnic group, the choice of controls would drastically affect the perceived associations (9) . On top of that, complicated issues are specifically present in the COVID-19 study. In the case of investigating patients recruited during an early stage of the pandemic in southern China, it could be inappropriate to use the local population as control, since most cases were made up of people who fled from Wuhan.

To better elucidate these situations, the use of multiple control groups was being suggested (14) .

Herein, we performed an empirical study in 399 unrelated patients to explore the effect of HLA on SARS-CoV-2 infection. We highlighted the influence of subpopulation by comparing it against multiple controls. Our findings provide deeper insights into the pathogenetic mechanism of COVID-19 and lay the valuable groundwork for future HLA research.

A c c e p t e d M a n u s c r i p t 5

Patients with confirmed COVID-19 diagnosis in Quanzhou, Fujian (n=42) during early 2020 were enrolled. Their demographic information, clinical characteristics, and peripheral blood samples were collected with informed consent. The HLA alleles were identified via NGSbased typing, using NanoWES Human Exome V1.0 (Berrygenomics, Beijing, China), according to the manufacturer's instructions. Class I and class II HLA alleles were estimated with OptiType v1.3.3 (15) and ATHLATES v1.0 (16), respectively. HLA allele frequencies and clinical information for patients in the other two centers (Shenzhen n=332 and Zhejiang n=82) were retrieved from prior studies (9, 17) .

Aiming to make our findings more plausible, we performed analyses against multiple sets of control. Firstly, we chose three controls representing the general population in China, we assessed the binding affinities from peptides derived from the receptor binding domain (RBD) region. We later labeled these bindings as the strong binder (IC50 < 50nM) and the weak binder (IC50 < 500nM), following the recommendations from the previous studies (24, 25) . The numbers of strong binders and weak binders were calculated per HLA allele.

Prior to analyses, several quality control procedures were taken. Closely related individuals were excluded through kinship estimation in PLINK v1.9 (26) . Hardy-Weinberg equilibrium (HWE) was assessed in Arlequin v3.5 (27) . Cochran's rule was applied to eliminate HLA alleles with a frequency less than five. The following statistical analyses were performed in R A c c e p t e d M a n u s c r i p t 7 v4.0.2. HLA heterogeneity among three centers was measured by pairwise comparisons using Fisher's exact test. The homogeneities among each control group were examined using the chi-square test. In addition, a t-test was performed to compare allele frequency between controls representing the general and the southern populations. Subsequently, to estimate the effect of HLA on disease susceptibility, we utilized Fisher's exact test to compare the HLA allele frequencies between COVID-19 patients and the selected controls. The corrected Pvalue (P c ) was then calculated with Benjamini-Hochberg method to adjust for multiple comparisons. Couples of alleles appeared to have significant heterogeneity among intra-group controls (full list presented in Supplementary Table 1 and Supplementary Table 2 ). Besides, ten alleles showed significant allele frequency differences between the general and the southern populations, as detailed in A c c e p t e d M a n u s c r i p t 8 We estimated the relationship between HLA and COVID-19 susceptibility by comparing cases against three controls representing the general population in China. We determined an HLA allele to have strong evidence for the association if the associations were observed to be significant against two or more controls. As illustrated in Table 2 

Our study confirmed that HLA-B*15:27 and HLA-DRB1*04:06, which appeared uniquely in Asia, according to AFND, were risk alleles for COVID-19 susceptibility. In spite that HLA-DRB1*04:06 was not significant after multiple comparison correction, both alleles had been mentioned as risk alleles in a prior study (17) . It is interesting that, while we suspect the association of HLA-A*11:01, HLA-A*11:02, and HLA-B*40:01 may be driven by population stratification, both HLA-A*11 and HLA-B*40 had been previously linked with COVID-19 infection (11, 12, 28) . Despite that HLA-B*40 did show weak peptide binding capability, HLA-A*11 exhibited moderate binding affinities in bioinformatic prediction.

Taken together, a comprehensive investigation of the impact of the subpopulation is much needed.

Additionally, DRB1*04:06 had been identified as a risk factor for autoimmune diseases (29, 30) and drug-induced maculopapular exanthema (31) . This finding is consistent with previous observations that autoimmune diseases correlating with COVID-19 severity and mortality (12, 32) . One popular explanation is that the two conditions share the same etiologies such as extensive inflammation. However, recent findings also suggested that the locus between DRB1 and DQA1 was related to circulating IL-6 levels (33) which often accompanying cytokine storms. Taken together, these findings indicate that individual vulnerability to infection may go beyond sole HLA affinity.

The major limitations of this study include the partial availability of haplotype data and the # cell counts smaller than 5; * significant A c c e p t e d M a n u s c r i p t Figure 1 

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We would like to acknowledge all the medical staff in the Department of Infectious Diseases and COVID-19 special ward, First Hospital of Quanzhou, affiliated with Fujian Medical University, for their work of clinical specimen collection during these trying times.

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