key: cord-0998665-4ykns94q
authors: Ferreira de Araújo, João Locke; Menezes, Diego; Saraiva‐Duarte, Julia Maria; de Lima Ferreira, Luciana; Santana de Aguiar, Renato; Pedra de Souza, Renan
title: Systematic review of host genetic association with Covid‐19 prognosis and susceptibility: What have we learned in 2020?
date: 2021-08-02
journal: Rev Med Virol
DOI: 10.1002/rmv.2283
sha: 9bdcfd025f2679cfa160200f409e7ef5247439ac
doc_id: 998665
cord_uid: 4ykns94q

Biomarker identification may provide strategic opportunities to understand disease pathophysiology, predict outcomes, improve human health, and reduce healthcare costs. The highly heterogeneous Covid‐19 clinical manifestation suggests a complex interaction of several different human, viral and environmental factors. Here, we systematically reviewed genetic association studies evaluating Covid‐19 severity or susceptibility to SARS‐CoV‐2 infection following PRISMA recommendations. Our research comprised papers published until December 31(st), 2020, in PubMed and BioRXiv databases focusing on genetic association studies with Covid‐19 prognosis or susceptibility. We found 20 eligible genetic association studies, of which 11 assessed Covid‐19 outcome and 14 evaluated infection susceptibility (five analyzed both effects). Q‐genie assessment indicated moderate quality. Five large‐scale association studies (GWAS, whole‐genome, or exome sequencing) were reported with no consistent replication to date. Promising hits were found on the 3p21.31 region and ABO locus. Candidate gene studies examined ACE1, ACE2, TMPRSS2, IFITM3, APOE, Furin, IFNL3, IFNL4, HLA, TNF‐ɑ genes, and ABO system. The most evaluated single locus was the ABO, and the most sampled region was the HLA with three and five candidate gene studies, respectively. Meta‐analysis could not be performed. Available data showed the need for further reports to replicate claimed associations.

Coronavirus disease pandemic remains overwhelming healthcare systems and damaging economies. People infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) present a wide range of severity of illness, from asymptomatic or mild to severe disease and death. Recent results estimate over 20.5 million life-years have been lost due to Covid-19 globally. 1 The highly heterogeneous Covid-19 clinical manifestations suggest a complex interaction of several different human, viral and environmental factors playing a role in Covid-19 prognosis. 2-4 Understanding mechanisms leading to severe cases is of great importance for therapeutic development and pandemic control. Furthermore, infection susceptibility has been associated with several factors. 5, 6 As to social-environmental aspects, the pandemic exposed preexisting health and social differences between historically vulnerable populations. A remarkable contrast between the mortality rate from Covid-19 in minority groups exists compared to privileged social stratum. 7 As to the pathogen aspects, the SARS-CoV-2 genome has almost 30,000 base pairs with structural genes (spike, nucleocapsid, membrane, and envelope) and non-structural proteins (involved in replication). 8 SARS-CoV-2 genome research has demonstrated viral diversity 9,10 may be related to pathogenicity, transmissibility, and, more recently, mortality. 11 Variability on the S viral gene seems relevant since it codes for the spike protein that interacts with two crucial cell entry factors: the human angiotensinconverting enzyme-2 (ACE2) receptor and the cellular serine protease TMPRSS2. Recent results indicate other possible human targets (e.g., cathepsin L). 12 As to the host aspects, structural data analysis proposed that ACE2 gene variants can alter host-virus interaction and Covid-19 susceptibility. 13 Apart from ACE2, several other proteins have been associated with Covid-19 pathogenesis and immune response.

Immunomodulatory molecules seem to play a crucial role (e.g., cytokine storm). It would be possible to hypothesize that polymorphisms in their genes could contribute to Covid-19 prognosis. 14 Here, we systematically reviewed genetic association studies evaluating Covid-19 severity or susceptibility to SARS-CoV-2 infection.

We registered a study protocol on PROSPERO (CRD42020187270).

Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) was adopted as a guideline for reporting this systematic review. 15 Study selection was carried out in three phases: identification, screening, and eligibility. Identification was performed by searching on two databases: PubMed and BioRXiv. The bibliographic search included all studies published untill 31 December 2020, using the search arguments listed in the supplementary material (supplementary material I).

Two independent researchers conducted the screening of the articles. Inclusion criteria were primary articles covering human genetics association with Covid-19 susceptibility and/or prognosis, while exclusion criteria were review articles or primary articles not covering genetic association with Covid-19 susceptibility and/or prognosis. A systematic review flowchart was prepared following PRISMA specifications.

We assessed study quality using the Q-Genie tool 16 performed by two independent researchers. This instrument contains 11 questions to be marked on a seven-point Likert scale examing several aspects of a genetic association study: scientific basis for the development of the research question, ascertainment of comparison groups (e.g., cases and controls), technical and non-technical classification of tested genetic variants (e.g., genotyping call rates, blinded experiments), classification of the outcome (e.g., sampling strategy, definition criteria), discussion of sources of bias, appropriateness of sample size, description of planned statistical analyses, statistical methods applied, test of assumptions in the genetic studies (e.g., Hardy-Weinberg equilibrium) and appropriate interpretation of the results. 16, 17 Since all studies used a case-control design, cut-offs were ≤35 for poor, >35 for moderate, and ≤45 for good quality, according to Sohabi et al. 17 (total sum may vary from 7 to 77 points).

Our literature search returned 1633 records from the two databases ( Figure 1 ). Three additional articles were added from other sources (e.g., cross-referencing), leading to 1636 records. We excluded 1587 articles after reading titles and abstracts. We removed another 29 manuscripts following full-text analysis (supplementary material II).

In the end, 20 studies were eligible for the qualitative synthesis.

We found 11 studies addressing genetic influence on the prognosis of Covid-19 (Table 1 ) and 14 studies exploring the susceptibility to Sars-CoV-2 infection (Table 2) . Five studies worked with both approaches. [18] [19] [20] [21] [22] Two studies proposed to work with prognosis, but the outcome was susceptibility. 23, 24 Study quality assessment resulted in six studies with poor quality, seven moderate, and seven classified as good. The mean quality score reached moderate classification (mean 41.56; standard deviation 9.05). One of the most valuable pieces of information from the Q-Genie usage is evaluating quality dimensions across studies, thus identifying systematic issues.

We consistently observed inadequate description of the genotyping process leading Q-Genie item number five to have the lowest mean score. We report that most studies failed to inform whether researchers performed genotyping blinded from case-control information or whether any randomization occurred across cases and controls to avoid batch effects. On the other hand, we found that the most successful quality aspect was presenting the rationale to conduct a genetic association study (Q-Genie item number onerationale for analysis).

Evidence of genetic association was reported in six of the 11 studies addressing the Covid-19 clinical outcome. [19] [20] [21] [25] [26] [27] [28] Three of these studies were large-scale association with either whole-genome sequencing or GWAS approach. 21, 26, 27 Hu et al. 27 reported GWAS significant signals on the DNAH7 CLUAP1, DES, SPEG, STXBP5, TOMM7, PCDH15, and WSB1 genes. Zhang et al. 26 focused on rare variants associated with a monogenic contribution to life-threatening Covid-19 and found 10 variants in the TLR3 and IRF7 genetic pathways. Still, their results were not replicated by Povysil et al. 29 Wang et al. found an association with severity on the TMEM189-UBE2V1 gene locus (rs6020298) using whole-genome sequencing. 21 Another three candidate gene studies indicated statistically significant loci, each evaluating genes related to immune response Wang et al., 28 and HLA-C*04:01 in Littera et al. 19 ) and in genes associated with the viral cell cycle (rs61735794 and rs61735792 located in TMPRSS2 31 and APOE allele e4 26 ). Genetic variance of the ABO blood system was also significant: while A-type subjected showed increased susceptibility, O-type individuals were less likely to be infected. 32,33

Biomarker identification may provide a strategic opportunity to understand disease pathophysiology and predict outcomes, therefore improving human health and reducing healthcare costs. Thus far, the most promising prognosis predictors are age, 34 sex, 35 comorbidities, 36, 37 and viral load at the moment of infection. 38 Level 1 (1); level 2 (2); level 3 (5); level 4 (4); level 5 (7); level 6 (6); level 7 (7); level 8 (6); level 9 (2); level 10 (1) Not reported Throughout last year, 20 genetic association studies were conducted. The most evaluated single locus was the ABO, and the most sampled region was the HLA with three 23,32,33 and five 18, 19, 22, 28, 30 candidate gene studies, respectively. We did not perform a metaanalysis because there was no replication for the same genetic variant or divergence on phenotype definition. Zhang et al. 26 and Povysil et al. 29 studies were the closest studies regarding experimental design and genetic variants examined, with both aiming to find rare variants associated with disease severity in the interferon I response pathway. They reached divergent results, but different control definitions and confounder variant treatment, such as age, may have contributed. 44 The need for replication studies has been extensively discussed to assess the credibility of the initial association, therefore, avoiding the winner's curse phenomenon. 45 and RIMBP3 rs200584390 (OR = 9.29) required molecular testing while others didn't, that is, only clinical symptomatology was assessed. 22 It is also relevant to point out that several studies were not transparent regarding their criteria, as indicated by unclear definition in Table 2 . Thirdly, most studies lack basic technical information (e.g., blinded genotyping, randomization, or the number of batches in which samples were processed) that may be different sources of relevant bias. A powerful tool to avoid further inadequate reporting of genetic association studies is the "strengthening the reporting of genetic association studies" (STREGA) report. 51 It includes a detailed checklist with elements that should be presented in a genetic association publication. While the STREGA recommendations do not aim to influence how a genetic association study should be designed, it seeks to enhance reporting transparency, thus also improving reproducibility.

While this review has highlighted many genes that may be potentially associated with Covid-19 prognosis and infection susceptibility, limitations such as lack of reproducibility, quality of reporting, and quality of assessment remain a significant concern.

Therefore, results should be taken with caution. Future studies are also warranted in underrepresented ancestries since the allelic frequency, and linkage disequilibrium may vary across different populations. 52

None. All authors revised and approved the final manuscript version.

All data is available upon request. 

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