key: cord-0685333-tcn6jc0b
authors: Zhang, Yuan; Yang, Hongxi; Li, Shu; Li, Wei-Dong; Wang, Ju; Wang, Yaogang
title: Association analysis framework of genetic and exposure risks for COVID-19 in middle-aged and elderly adults
date: 2021-01-12
journal: Mech Ageing Dev
DOI: 10.1016/j.mad.2021.111433
sha: f54deae6828b24a2d3dfbc6aee7e40ed3a42d89a
doc_id: 685333
cord_uid: tcn6jc0b

Coronavirus disease 2019 (COVID-19) is a current pandemic, and studies reported that older people have higher rates of infection and more severe cases. Recently, studies have revealed the involvement of both genetic and exposure factors in the susceptibility of COVID-19. However, the correlation between them is still unclear. Thus, we aimed to investigate the correlation between genetic and exposure factors associated with COVID-19. We retrieved the information of 7362 participants with COVID-19 testing results from the UK Biobank. We identified genetic factors for COVID-19 by genome-wide association studies (GWAS) summary analysis. In this study, 21 single-nucleotide polymorphisms (SNPs) and 15 exposure factors [smoking, alcohol intake, daytime dozing, body mass index (BMI), triglyceride, High Density Lipoprotein (HDL), diabetes, chronic kidney disease, chronic liver disease, dementia, atmosphere NO2 concentration, socioeconomic status, education qualification, ethnicity, and income] were found to be potential risk factors of COVID-19. Then, a gene-exposure (G × E) association network was built based on the correlation among and between these genetic factors and exposure factors. rs140092351, a SNP on microRNA miR1202, not only had the most significant association with COVID-19, but also interacted with multiple exposure factors. Dementia, alcohol consumption, daytime dozing, BMI, HDL, and atmosphere NO2 concentration were among most significant G × E interactions with COVID-19 infection (P = 0.001).

We hypothesized the existence of nonrandom correlation among and between the genetic and exposure factors associated with COVID-19, based on which an association network of these factors can be built. Then, by examining whether a person fit into such association network "pattern" could provide us a more comprehensive assessment of the risks, susceptibility, and treatment responses of COVID-19, and improve our understanding on the etiology of the disease. Thus, in the present study, we aimed to investigate the correlation of genetic and exposure factors associated with COVID-19 in middle-aged and elderly adults, as well as the global phenotype-genotype association framework for the disease.

Data related to COVID-19 were obtained from the UK Biobank, a health resource for a population-based study of more than 500,000 participants that attended one of 22 with COVID-19 testing results or with exposure and genetic information were included.

In present study, the exposure factor screening was based on a previously published review(Zhang et al., 2020). Based on the extensive review and analysis of the abovementioned review, we have enriched, improved, integrated, and assembled the literature on the exposure risk factors, methods, and models of COVID-19, and applied UKB data to analyze, verify and expand. In this study, the exposure factors are organized into five hierarchical levels, including behavior risks, metabolic risks, disease risks, environmental risks, and socio-demographic index.

We used 17 indicators for behavior risks. Briefly, smoking status was categorized as never, previous, or current smoking. Regular physical activity was defined as per week ≥150 minutes of moderate activity, or per week ≥ 75 minutes of vigorous activity (Lloyd-Jones et al., 2010). Alcohol intake (including wine, beer, spirits, and fortified wine) was categorized as <1 g/day, 1-7 g/day, 8-15 g/day, and ≥ 16 g/day. All sleep behaviors were self-reported, and we included six sleep factors (chronotype, duration, insomnia, snoring, daytime dozing, and nap during day). All of the UK Biobank participants completed a questionnaire on their usual dietary pattern, most of which J o u r n a l P r e -p r o o f asked about the frequency of consumption of main foods and food groups. The questions used in this manuscript are those that asked about the frequency of consumption of fresh fruit, raw vegetables, cooked vegetables, oily fish, non-oily fish, processed meat, beef, lamb, pork, tea, and coffee.

Nine metabolic risk factors were included in our study. Of them, height, weight, waist circumference, and hip circumference were measured directly during a medical examination from which body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Non-fasting venous blood, available in a subsample, was drawn with assaying conducted at dedicated central laboratory for uric acid, cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and Vitamin D. (Elliott et al., 2008) Eleven disease factors were employed in the study. Vital statuses of each participant were identified chiefly using linkage with hospital admission data. Disease affection statuses were documented, including type 2 diabetes (T2DM), chronic kidney disease, hypertension, depression, dementia, cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), asthma, chronic liver disease, and cancer. Each disease factor was categorized as undiagnosed, diagnosed < 10 years ago, and diagnosed ≥ 10 years ago according to disease duration. 

Genetic risk factors for COVID-19 were identified by the COVID-19 Host Genetics Initiative (https://www.covid19hg.org/), (Initiative, 2020) a global initiative to bring together the human genetics community to generate, share, and analyze data to learn the genetic determinants of COVID-19 susceptibility, severity, and outcomes. In this J o u r n a l P r e -p r o o f study, 102 single-nucleotide polymorphisms (SNPs) reaching a conventional genomewide significance threshold of P-value <1 ×10 -6 were identified (Supplementary Table 1 ).

Provided by Public Health England, data on COVID-19 status downloaded on July 6, 2020 covered the period March 16, 2020 until May 31, 2020. Nose and/or throat swabs were taken from hospitalized patients and detection of SARS-CoV-2 can be reported as positive or negative.

All of the models were adjusted for age, sex, ethnicity (white, mixed, Asian, black, and others), qualifications (College degree, A levels/AS levels, O levels/GCESs, CSEs, NVQ or HND or HNC, other professional qualifications, and none of the above), and socioeconomic status (categories derived from Townsend deprivation index(Guillaume et al., 2016) quintiles 1, 2 to 4, and 5, combining information on social class, employment, car availability, and housing). Cytoscape (version 3.7.1) was used to layout the association network. (Shannon et al., 2003) All statistical analyses were performed using R (version 3.6.1).

In this analysis, after excluding participants without genetic information or without exposure information, 7362 participants were ultimately included For these samples, the mean age was 69.20 ± 8.68 years, and 3647 (49.54%) individuals were male. In total, 1485 (20.17%) participants were positive for COVID-19 infection. The baseline characteristics of the participants are provided in Table 1 . Compared to participants negative for COVID-19 infection, the positive participants were more likely to be male, be of Asian or Black ethnic group, have a higher socioeconomic status and income; they were also more likely to have a history of T2DM or dementia, a higher BMI and a lower level of HDL, whereas less likely to consume alcohol, and less likely to have a university degree.

From the 102 SNPs identified in the GWAS summary analysis, we obtained 21 SNPs that were associated with COVID-19 ( Figure 1A; Supplementary Table 2 ). The rs140092351 locus on microRNA MIR1202 yielded the most significant association.

For the 45 exposure factors examined (Supplementary Table 3) , we found 15 exposure J o u r n a l P r e -p r o o f factors associated with COVID-19, including smoking, alcohol intake, daytime dozing, BMI, TG, HDL, diabetes, chronic kidney disease, chronic liver disease, dementia, atmosphere NO2 concentration, socioeconomic status, education qualification, ethnicity, and income ( Figure 1B) .

We also detected associations between the genetic and exposure factors. Altogether, 247 associations among and between the 21 genetic risks and 15 exposure factors of COVID-19 infection were identified, based on which a risk factor association network was conducted (Figure 2A) . Figure 2B shows the correlation coefficient of SNPs and exposure factors. Among the exposure factors, ethnicity was associated with sixteen genetic loci of COVID-19, and atmosphere NO2 concentration was associated with ten genetic loci of COVID-19, while alcohol intake was associated with nine gene loci of COVID-19. Furthermore, smoking was associated with eight gene loci of COVID-19, and T2DM was associated with four gene loci of COVID-19.

The significance of gene-exposure interaction was further evaluated using the GMDR model with age, sex, ethnicity, qualification, and socioeconomic status as covariates ( 

We found a significant association framework between and among genetic and exposure factors of COVID-19 infection. The rs140092351 locus on a microRNA MIR1202 not only had the most significant association with COVID-19, but also interacted with multiple exposure factors. Dementia, alcohol consumption, daytime dozing, BMI, HDL, and atmosphere NO2 concentration were among most significant G Our present study also had several limitations. We are unable to assess exposure to SARS-CoV-2 in most UKB participants. This has important implications for casecontrol studies, because we cannot distinguish individuals who have not contracted SARS-CoV-2 following exposure from those who have not been exposed. Furthermore, genetic factors related to exposure factors may not cause COVID-19 by themselves, but likely to increase the susceptibility of the disease by increasing the risk of phenotypic 

We found a significant association framework between and among genetic and 

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We thank the participants of the UK biobank. This research has been conducted using the UK biobank Resource under the project number of 45676. We would like to thank all UK Biobank participants and staff, and all health-care workers involved in the diagnosis and treatment of COVID-19 patients.

This work was supported by the National Natural Science Foundation of China (grant numbers: 71910107004, 91746205).

YW conceived the idea. YW, YZ and HY designed the study. YW, YZ and HY led the analysis with support from SL. YW and YZ drafted the paper, YW, YZ, WL, and JW finalized the paper. All authors contributed to the analysis, intellectual content, critical revisions to the drafts of the paper and approved the final version.

The authors have no conflicts of interest to declare.J o u r n a l P r e -p r o o f