Skip to main content
NCBI home page
CNS Neuroscience & Therapeutics logoLink to CNS Neuroscience & Therapeutics
. 2024 Jul 26;30(7):e14873. doi: 10.1111/cns.14873

Shared genetics and causal association between plasma levels of SARS‐CoV‐2 entry receptor ACE2 and Alzheimer's disease

Yan Zhang 1, Fang Xu 2, Tao Wang 3,4, Zhifa Han 5, Hong Shang 6, Kevin Han 7, Ping Zhu 8, Shan Gao 8, Xiaojie Wang 9, Yanli Xue 10, Chen Huang 11, Yan Chen 12,13,✉, Guiyou Liu 8,12,13,14,15,16,✉
PMCID: PMC11273102  PMID: 39056224

Abstract

Background

Alzheimer's disease (AD) is the highest risk of COVID‐19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID‐19 outcomes. ACE2 is an entry receptor for SARS‐CoV‐2. Circulating ACE2 is a novel biomarker of death and associated with COVID‐19 outcomes.

Methods

Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large‐scale genome‐wide association study, gene expression, expression quantitative trait loci, and high‐throughput plasma proteomic profiling datasets.

Results

We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross‐trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID‐19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID‐19 infection, ventilation, and death.

Conclusion

Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID‐19.

Keywords: ACE2, Alzheimer's disease, COVID‐19, expression quantitative trait loci, gene expression, genome‐wide association study, SARS‐CoV‐2

We found the shared genetics and causal association between plasma levels of SARS‐CoV‐2 entry receptor ACE2 and Alzheimer's disease. We found association between plasma proteins corresponding to the shared genes and COVID‐19 outcomes using high‐throughput plasma proteomic profiling.

graphic file with name CNS-30-e14873-g005.jpg

1. INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) had caused coronavirus disease 2019 (COVID‐19), a devastating global pandemic. Evidence indicates that several medical comorbidities, such as chronic obstructive pulmonary disease, asthma, cardiovascular disease (CVD), diabetes, hypertension, and dementias, increase the risk of COVID‐19 infection, hospitalization, and mortality. 1 , 2 Importantly, the older population with dementia especially Alzheimer's disease (AD) are facing an unprecedented threat from COVID‐19, and have the highest risk of COVID‐19 infection, hospitalization, and mortality. 1 , 2 Meanwhile, COVID‐19 further increases the risk of AD. 3 , 4

Angiotensin‐converting enzyme 2 (ACE2) is a protein on the surface of many cell types. 5 It cuts up the larger protein angiotensinogen into small proteins that then go on to regulate functions in these cells. 5 Importantly, ACE2 has been identified to be an entry receptor for SARS‐CoV‐2. 5 In healthy individuals, ACE2 exists mainly in membrane‐bound form, and circulating ACE2 (lacking the transmembrane domain) is relatively low with very low levels of catalytically active ectodomain. 6 , 7 , 8 It is believed that circulating ACE2 is generated from cell‐membrane expressed ACE2, shed by ADAM‐17 and other proteases. 8 SARS‐CoV‐2 utilizes the catalytic site of full‐length membrane‐bound ACE2 for host cell entry, which is followed by viral internalization together with ACE2 and ACE2 degradation, accelerating the conversion from membrane‐bound ACE2 to circulating ACE2. 5 , 9 Therefore, the full‐length membrane‐bound ACE2 levels were markedly reduced and the circulating ACE2 levels were markedly increased upon SARS‐CoV‐2 infection, which have been widely reported in COVID‐19 patients by observational studies. 10 , 11 Importantly, high circulating ACE2 associated with increased COVID‐19 severity and mortality, and could be used to predict severity and mortality. 10 , 11

In addition to COVID‐19, circulating ACE2 is also a novel biomarker of death and CVD. 12 , 13 In patients with CVD, increased circulating ACE2 levels associate with adverse cardiovascular outcomes. 12 In the general population, high circulating ACE2 levels associate with increased risk of total deaths, incident heart failure, myocardial infarction, stroke, and diabetes independent of age, sex, ancestry, and traditional cardiovascular risk factors. 13 Compared with the well‐established clinical risk factors (smoking, diabetes, blood pressure, lipids, and body‐mass index), circulating ACE2 is the highest ranked predictor of death, and is also a strong predictor of CVD, including heart failure, stroke, and myocardial infarction. 13 Importantly, recent findings further support positive genetic association of circulating ACE2 with severe COVID‐19, CVD, asthma, diabetes, and hypertension, as well as causal effect of circulating ACE2 on COVID‐19 infection, hospitalization, and severity. 14

Collectively, these above findings show that circulating ACE2 shares a genetic basis with COVID‐19 and its established risk factors, and could be a link of COVID‐19 severity and mortality with its established risk factors. Evidence shows that AD pathology might aggravate the consequence of COVID‐19 infection. 15 However, it currently remains unclear about the genetic association between circulating ACE2 levels and the risk of AD. We consider that there may be shared genetic etiology between circulating ACE2 levels and AD, which may contribute to explain the highest risk of COVID‐19 infection, hospitalization, and mortality in preexisting diagnosis of AD, as well as the increased risk of AD in COVID‐19 patients. Here, we explore the shared genetic etiology between AD and plasma ACE2 levels. In stage 1, we examine the causal association between circulating ACE2 and AD using Mendelian randomization (MR). In stage 2, we identify the shared genetic variants using a cross‐trait association analysis. In stage 3, we map the shared genetic variants to their corresponding genes, and conduct tissue‐specific gene expression analysis, tissue‐specific enrichment analysis, and gene set enrichment analysis. In stage 4, we investigate the association of shared genetic variants and their corresponding genes with COVID‐19 outcomes. Figure 1 provides the schematic diagram of the study design in this study.

FIGURE 1.

FIGURE 1

Open in a new tab

The schematic diagram of the study design in this study.

2. MATERIALS AND METHODS

2.1. AD GWAS dataset

International Genomics of Alzheimer's Project (IGAP) conducted the largest clinically diagnosed AD genome‐wide association study (GWAS) in 94,437 individuals of European ancestry (35,274 clinical and autopsy‐documented AD and 59,163 controls) from stage 1 (n = 63,926), stage 2 (n = 18,845), and stage 3A (n = 11,666) or stage 3B (n = 30,511 from stage 2 + stage 3A). 16 AD is diagnosed using the same diagnostic criteria including DSM‐III‐R, DSM‐IV, and NINCDS‐ADRDA across the three stages. 16 There is no clear evidence of cognitive or overall difference across different stages. 16 In IGAP stage 1, a total of 9,456,058 common variants and 2,024,574 rare variants were imputed and selected for analysis. 16 In IGAP stage 2, a total of 11,632 variants were further genotyped in 8362 AD cases and 10,483 controls, and were meta‐analyzed with IGAP stage 1. 16 Here, we selected the IGAP stage 1 for MR analysis as it included the full genetic variants (n = 9,456,058), and IGAP stage 1 + stage 2 for cross‐trait association analysis as it included the largest sample size (n = 82,777), respectively.

2.2. Circulating ACE2 GWAS dataset

In order to understand the genetic basis of the ACE2 protein levels, Yang et al. 14 performed the largest GWAS meta‐analysis of plasma ACE2 levels measured by Olink platform in 28,204 individuals from 14 cohorts in the SCALLOP consortium (Systematic and Combined Analysis of Olink Proteins). Here, we selected the circulating ACE2 GWAS dataset in both LDSC analysis and cross‐trait association analysis. Yang et al. 14 only identified 10 independent genome‐wide significant genetic variants including nine in the autosomes and one in the X chromosome, which together explain 4.1% of the phenotypic variance of plasma ACE2 equivalent to about 30% of the heritability. In order to increase more autosomal genetic variants as the potential instrumental variables in MR analysis, we performed a clumping analysis of the plasma ACE2 GWAS dataset to select independent autosomal genetic variants with p < 1.00E‐05 using TwoSampleMR v0.5.7 and two key parameters including clumping window 250 kb and clumping r 2 cutoff 0.01.

2.3. Mendelian randomization analysis

We selected inverse‐variance weighted (IVW) as the main MR analysis method, and selected the weighted median and MR‐Egger as the sensitivity analysis methods. 17 , 18 , 19 If all genetic variants are valid instrumental variables, in other words, if there is no evidence of pleiotropy, IVW combines the variant‐specific Wald estimators to get the overall causal estimate. 17 , 18 , 19 If some genetic variants are not valid instrumental variables, in other words, if there is clear evidence of pleiotropy, MR‐Egger could test the potential pleiotropy using MR‐Egger intercept test and accounts for the potential pleiotropy. 17 , 18 , 19 The causal estimate from weighted median is still consistent with the overall causal estimate when at least 50% of the weights come from valid instrumental variables. 17 , 18 , 19 All statistical analyses were performed using the R package “Mendelia nRandomization” 19 an d R version 4.0.5.

2.4. Cross‐trait meta‐analysis

We conducted a cross‐trait meta‐analysis to identify the shared genetic variants in both AD and circulating ACE2 using METAL, which is a popular tool for meta‐analysis of GWAS datasets. 20 METAL provides two analysis schemes. One scheme, METAL combines the p values across different studies by fixed‐effects sample size weighted meta‐analysis taking into account the direction of effect. 20 The other scheme, METAL combine effect size estimates and standard errors across different studies by fixed‐effects inverse‐variance weighted meta‐analysis. 20 Here, we selected both analysis schemes to identify the shared genetic variants reaching genome‐wide significance p < 5.00E‐08 for meta‐analysis and suggestive trait‐specific significance p < 0.05 for AD and circulating ACE2.

2.5. Gene mapping

We aim to identify the risk genes corresponding to the shared genetic variants using both positional mapping and expression quantitative trait loci (eQTLs) mapping. For positional mapping, we map the shared genetic variants to the nearest genes using HaploReg v4.1. 21 For eQTLs mapping, we identify risk genes whose expression might be regulated by the shared genetic variants using multiple publicly available eQTLs datasets from human whole blood, brain tissues, microglial cell, and other human tissues. Here, we selected 49 eQTLs datasets in 49 human tissues from Genotype‐Tissue Expression Project (GTEx version 8), 22 1 large‐scale eQTLs meta‐analysis dataset in 1433 brain cortex samples, 23 4 eQTLs dataset in 255 primary human microglial samples isolated at autopsy from four different brain regions of 100 individuals with neurodegenerative, neurological, or neuropsychiatric disorders, as well as unaffected controls, 24 4 eQTL datasets in whole blood, including 31,684 individuals, 25 2765 individuals, 26 2116 individuals, 27 5257 and individuals. 28 The statistically significant association is defined to be p < 1.00E‐04.

2.6. Tissue‐specific gene expression analysis

Using all genes from both positional mapping and eQTLs mapping, we performed a tissue‐specific gene expression analysis by FUMA v1.5.0, which is an online web application to annotate and prioritize genetic associations. 29 FUMA evaluated gene expression and detected tissue‐specific enrichment analysis using expression data from GTEx v8 54 tissue types. 29 The gene expression value TPM (Transcripts Per Million) is an averaged expression value per tissue type per gene following to winsorization at 50 and log 2 transformation with pseudocount 1. 29 This kind of averaged expression allows for comparison across tissues and genes.

2.7. Tissue‐specific enrichment analysis

Using all genes from both positional mapping and eQTLs mapping, we performed a tissue‐specific enrichment analysis by FUMA v1.5.0. 29 Tissue‐specific enrichment analysis is tested using the differentially expressed genes (DEGs) defined for each tissue type of each expression dataset. 29 First, gene expression values were normalized (zero‐mean) following to a log 2 transformation of expression value (TPM). 29 Second, DEGs were calculated by performing two‐sided t‐test for any one of tissue type against all others. Only those genes with Bonferroni corrected p value ≤ 0.05 and absolute log fold change ≥0.58 were defined as DEGs. 29 Third, tissue‐specific enrichment analysis is performed to test if DEGs are overrepresented in any of tissue type against all others using the hypergeometric test. Tissue types with Bonferroni corrected p value < 0.05 are defined to be significant enrichment of DEGs. 29

2.8. Gene set enrichment analysis

We performed a gene set enrichment analysis of all genes from both positional mapping and eQTLs mapping using WebGestalt (WEB‐based Gene SeT AnaLysis Toolkit), a functional enrichment analysis web tool. 30 Here, we focused on the KEGG pathways in WebGestalt functional database. 30 The hypergeometric test was used to detect any overrepresentation of the shared genes among all the genes in a given KEGG pathway. 30 KEGG pathways with Bonferroni corrected p value < 0.05 are defined to be significantly enriched pathways.

2.9. Association between genetic variants and COVID‐19 outcomes

We investigated the potential association between the shared genetic variants and COVID‐19 outcomes using large‐scale GWAS datasets from COVID‐19 Human Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS‐CoV‐2 virus pandemic. 31 We downloaded the GWAS summary statistics from COVID19‐hg GWAS meta‐analyses round 7 for four COVID‐19 outcomes including infection: cases versus population (159,840 cases and 2,782,977 controls), hospitalization: hospitalized cases versus population (44,986 cases and 2,356,386 controls) and hospitalized cases versus not hospitalized cases (16,512 cases and 71,321 controls), and severity: very severe respiratory confirmed cases versus population (18,152 cases and 1,145,546 controls).

2.10. Association between shared genes and COVID‐19 outcomes

We evaluated the potential association between plasma proteins corresponding to the shared genes and COVID‐19 outcomes using high‐throughput plasma proteomic profiling from two large independent cohorts including the discovery stage (332 COVID‐19 patients and 150 controls) and replication stage (297 COVID‐19 patients and 76 controls). 32 Three COVID‐19 outcomes were available in both datasets including COVID‐19 infection (all cases vs. healthy controls that compared all COVID‐19‐positive individuals with samples taken from healthy individuals without COVID‐19), ventilation (cases requiring ventilation vs. cases without ventilation support that compared COVID‐19‐positive individuals whose treatment included ventilation to COVID‐19‐positive individuals who did not), and death (died cases vs. survived cases that contrasted COVID‐19‐related deaths with individuals who had COVID‐19 but did not die of it). 32 We explored the shared genes using COVID‐19 Proteomics Data and Analytics Browser, which consisted of 1449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death). 32

3. RESULTS

3.1. Mendelian randomization analysis

We identified 70 independent autosomal genetic variants with p < 1.00E‐05 by clumping analysis of the plasma ACE2 GWAS dataset using TwoSampleMR v0.5.7 and two key parameters including clumping window 250 kb and clumping r 2 cutoff 0.01, as provided in Table S1. Here, we selected these 70 genetic variants as the potential instrumental variables, and extracted their corresponding AD GWAS summary statistics from IGAP stage 1. Using IVW, we identified a significant causal effect of genetically increased circulating ACE2 level on increased risk of AD (OR = 1.12, 95% CI: 1.05–1.21, p = 0.001). Importantly, MR estimates from weighted median (OR = 1.10, 95% CI: 0.97–1.23, p = 0.134) and MR‐Egger (OR = 1.10, 95% CI: 0.95–1.29, p = 0.203) were consistent with IVW estimate in terms of direction and magnitude although lack of statistically significance. Meanwhile, MR‐Egger intercept test indicates no evidence of pleiotropy with intercept = 0.001 and p = 0.793.

3.2. Cross‐trait meta‐analysis

Using fixed‐effects sample size weighted meta‐analysis, we found 19 genetic variants that were associated with both AD and circulating ACE2 at the genome‐wide significance p < 5.00E‐08 for the cross‐trait meta‐analysis and suggestive trait‐specific significance p < 0.05 for AD and circulating ACE2 with the same directions of effect sizes (Table 1); 4, 1, 1, and 13 genetic variants are located at chromosome 6p21.32, 8p21.2‐p21.1, 17p13.2, and 19q13.32, respectively. These genetic variants are in linkage disequilibrium with each other. Using fixed‐effects inverse‐variance weighted meta‐analysis, these 19 genetic variants were further verified, and three genetic variants rs9269853, rs2395166, and rs10415074 reached the genome‐wide significance p < 5.00E‐08, as provided in Table 1.

TABLE 1.

Shared genetic variants from cross‐trait meta‐analysis of AD and circulating ACE2 with p < 5.00E‐08 and single trait p < 0.05.

SNP Chr Position EA NEA Z score p Value β SE p Value Direction Gene
rs2395166 6 32388275 t c −6.543 6.02E‐11 −0.0428 0.0076 1.51E‐08 − 8.8 kb 5′ of BTNL2
rs3135344 6 32395036 t c −5.987 2.14E‐09 −0.0412 0.0079 1.70E‐07 − 13 kb 5′ of HLA‐DRA
rs9269853 6 32550322 a c 5.969 2.38E‐09 0.0887 0.0149 2.63E‐09 ++ HLA‐DRB1
rs3104412 6 32585967 a g 5.799 6.68E‐09 0.0399 0.0075 8.90E‐08 ++ 10 kb 5′ of HLA‐DQA1
rs17466060 8 27422740 a g 5.539 3.04E‐08 0.0353 0.0073 1.50E‐06 ++ 20 kb 3′ of EPHX2
rs12150370 17 4777634 t c −5.464 4.66E‐08 −0.0629 0.0119 1.16E‐07 − MINK1
rs1551891 19 45231821 a g −6.577 4.80E‐11 −0.0738 0.0142 2.00E‐07 − 2.5 kb 5′ of snoZ6
rs62117161 19 45233385 a g 6.645 3.02E‐11 0.0734 0.0143 2.66E‐07 ++ 4.1 kb 5′ of snoZ6
rs62117162 19 45239536 a c −6.115 9.68E‐10 −0.0682 0.0142 1.46E‐06 − 10 kb 5′ of snoZ6
rs4803750 19 45247627 a g 6.635 3.24E‐11 0.0762 0.0154 7.13E‐07 ++ 3.3 kb 5′ of BCL3
rs10401176 19 45253491 t c −6.683 2.34E‐11 −0.0608 0.0119 3.26E‐07 − BCL3
rs10415074 19 45341759 c g −11.81 3.47E‐32 −0.1351 0.0127 2.99E‐26 − 7.6 kb 5′ of PVRL2
rs141739979 19 45374983 t g −5.974 2.32E‐09 −0.2206 0.0435 4.02E‐07 − PVRL2
rs113345881 19 45431636 a g −5.928 3.08E‐09 −0.0597 0.0133 6.99E‐06 − 9 kb 3′ of APOC1
rs112871012 19 45431897 a g 5.622 1.88E‐08 0.0567 0.0133 1.90E‐05 ++ 9.3 kb 3′ of APOC1
rs7259350 19 45432278 t c −5.829 5.58E‐09 −0.0582 0.0133 1.15E‐05 − 9.7 kb 3′ of APOC1
rs7259004 19 45432557 c g 5.639 1.71E‐08 0.0578 0.0132 1.22E‐05 ++ 10 kb 3′ of APOC1
rs143674704 19 45458466 a g −6.802 1.03E‐11 −0.146 0.0296 8.36E‐07 − CLPTM1
rs76271385 19 45612862 a g 5.525 3.29E‐08 0.1865 0.0419 8.52E‐06 ++ PPP1R37
Open in a new tab

Note: The position is based on GRCh37/hg19.

Abbreviations: Chr, chromosome; EA, effect allele; NEA, non‐effect allele.

3.3. Gene mapping using positional mapping

Using HaploReg v4.1, we mapped these 19 genetic variants to their corresponding nearby genes as provided in Table 1. Four genetic variants at chromosome 6p21.32 are mapped to BTNL2, HLA‐DRA, HLA‐DRB1, and HLA‐DQA1. Genetic variant rs17466060 located at chromosome 8p21.2‐p21.1 is mapped to 20 kb 3′ of EPHX2. Genetic variant rs12150370 located at chromosome 17p13.2 is mapped to MINK1. Thirteen genetic variants located at chromosome 19q13.32 are mapped to snoZ6, BCL3, PVRL2, APOC1, CLPTM1, and PPP1R37 (Table 1).

3.4. Gene mapping using eQTLs analysis

eQTLs analysis not only confirms those findings from positional mapping, but also highlights some novel findings. Four genetic variants at chromosome 6p21.32 are associated with the expression of 65 genes including HLA‐DPB1, HLA‐DMA, HLA‐DMB, HLA‐DOB, HLA‐DPA1, HLA‐DPB2, HLA‐DQA1, HLA‐DQA2, HLA‐DQB1, HLA‐DQB1‐AS1, HLA‐DQB2, HLA‐DRA, HLA‐DRB1, HLA‐DRB4, HLA‐DRB5, HLA‐DRB6, HLA‐DRB9, AGER, ATF6B, PSMB9, TNXB, C2, and LST1. rs17466060 variant located at chromosome 8p21.2‐p21.1 was associated with the expression of three genes including EPHX2, CLU, and GULOP. rs12150370 variant located at chromosome 17p13.2 was associated with the expression of 33 genes such as CAMTA2, CHRNE, INCA1, MINK1, PLD2, CXCL16, KIF1C, SCIMP, GP1BA, ENO3, and ZNF232. Thirteen genetic variants located at chromosome 19q13.32 associated with the expression of 12 genes such as BCL3, PVR, PVRL2, NKPD1, MARK4, CEACAM19, NECTIN2, BCAM, APOC1P1, and CKM. Here, we have provided more detailed eQTLs analysis results in Tables S2–S8.

3.5. Tissue‐specific gene expression analysis

We got a total of 117 unique genes using positional mapping and/or eQTLs mapping. A total of 102 and 94 genes were recognized with recognized Ensembl ID and Ensembl ID in FUMA, respectively. Tissue‐specific gene expression results are provided in Figure 2 as a gene expression heat map, which is clustered by both genes and tissues. The results showed that some genes were highly expressed across GTEx v8 54 tissue types, such as HLA‐DRA, HLA‐DRB1, CLU, KIF1C, BAG6, CLPTM1, MINK1, and ATF6B. Meanwhile, some genes were highly expressed specifically in some tissues, such as LST1 and AGER. LST1 only showed high expression levels in whole blood, spleen, and lung. AGER just showed high expression levels in lung and thyroid.

FIGURE 2.

FIGURE 2

Open in a new tab

Heat map of tissue‐specific gene expression of genes corresponding to shared genetic variants from cross‐trait meta‐analysis of AD and circulating ACE2. The heat map is plotted using FUMA v1.5.0 and gene expression data from GTEx v8 54 tissue types. The heat map was ordered by both gene and tissue clustering. Darker red represent higher expression of that gene compared to darker blue color across genes and tissues.

3.6. Tissue‐specific enrichment analysis

Using GTEx v8 54 tissue ty pes, DEGs are significantly enriched in lung, spleen, and small intestine with Bonferroni corrected p value < 0.05, which are highlighted in red as provided in Figure 3. Interestingly, subgroup analysis using the upregulated DEGs and downregulated DEGs further supports that only upregulated DEGs are significantly enriched in lung, spleen, and small intestine with Bonferroni corrected p value < 0.05. Meanwhile, we found that downregulated DEGs were significantly enriched in multiple brain tissues including putamen (basal ganglia), frontal Cortex (BA9), hippocampus, nucleus accumbens (basal ganglia), anterior cingulate cortex (BA24), cortex, cerebellum, and cerebellar hemisphere. Here, we have provided more detailed results from the tissue specificity test in Table S9.

FIGURE 3.

FIGURE 3

Open in a new tab

Tissue‐specific gene expression enrichment analysis of differentially expressed genes across GTEx v8 54 tissue types. Enrichment of differentially expressed genes was identified using FUMA v1.5.0 and gene expression data from GTEx v8 54 tissue types. Tissue types with Bonferroni corrected p value ≤ 0.05 are defined to be significant enrichment of differentially expressed genes, and are highlighted in red.

3.7. Gene set enrichment analysis

We identified 25 significantly enriched pathways as provided in Figure 4. KEGG pathway classifications shows that most of these pathway are associated with immune system and immune diseases, such as autoimmune thyroid disease, intestinal immune network for IgA production, type I diabetes mellitus, graft‐versus‐host disease, allograft rejection, and asthma. Meanwhile, other pathways are associated with infectious diseases, including staphylococcus aureus infection, leishmaniasis, herpes simplex infection, toxoplasmosis, Epstein–Barr virus infection, Influenza A, Tuberculosis, and Human T‐cell leukemia virus 1 infection. Here, we have provided more detailed results from gene set enrichment analysis in Table S10.

FIGURE 4.

FIGURE 4

Open in a new tab

Gene set enrichment analysis of genes corresponding to shared genetic variants from cross‐trait meta‐analysis of AD and circulating ACE2. Gene set enrichment analysis was performed using WebGestalt (WEB‐based GEne SeT AnaLysis Toolkit). KEGG pathways with Bonferroni corrected p value < 0.05 are defined to be significantly enriched pathways.

3.8. Association between shared genetic variants and COVID‐19 outcomes

Using the GWAS summary statistics from COVID19‐hg GWAS meta‐analyses round 7, we found suggestive (p value < 0.05) or statistically significant (Bonferroni corrected p value < 0.05/(19*4) = 6.58E‐04, as 19 genetic variants and four COVID‐19 outcomes) association of three genetic variants at chromosome 6p21.32 (rs3104412, rs2395166, and rs3135344) and two genetic variants at 19q13.32 (141,739,979, rs143674704) with COVID‐19 outcomes, as provided in Table 2. In brief, three genetic variants including rs3104412, rs141739979, and rs143674704 were associated with COVID‐19 infection, three genetic variants including rs2395166, rs3135344, and rs3104412 were associated with COVID‐19 hospitalization, two genetic variants including rs3135344 and rs3104412 were associated with COVID‐19 severity, as provided in Table 2. Importantly, the directions of the effect alleles from rs3104412, rs2395166, and rs3135344 are consistent across AD, circulating ACE2, and COVID‐19 outcomes. Here, we provided all association results in Table S11.

TABLE 2.

Association between shared genetic variants and COVID‐19 outcomes with p < 0.05.

COVID‐19 outcomes SNP Chr Position EA NEA β SE p Value
COVID‐19 versus population rs3104412 6 32585967 A G 8.31E‐03 4.21E‐03 4.84E‐02
COVID‐19 versus population rs141739979 19 45374983 T G 5.51E‐02 2.68E‐02 3.94E‐02
COVID‐19 versus population rs143674704 19 45458466 A G 3.52E‐02 1.73E‐02 4.22E‐02
Hospitalized COVID‐19 versus population rs2395166 6 32388275 T C −1.78E‐02 8.81E‐03 4.34E‐02
Hospitalized COVID‐19 versus population rs3135344 6 32395036 T C −3.53E‐02 9.70E‐03 2.68E‐04
Hospitalized COVID‐19 versus population rs3104412 6 32585967 A G 3.01E‐02 8.45E‐03 3.65E‐04
Hospitalized COVID‐19 versus not hospitalized COVID‐19 rs3135344 6 32395036 T C −4.97E‐02 1.99E‐02 1.26E‐02
Very severe respiratory confirmed COVID‐19 versus population rs3135344 6 32395036 T C −4.91E‐02 1.48E‐02 8.78E‐04
Very severe respiratory confirmed COVID‐19 versus population rs3104412 6 32585967 A G 5.29E‐02 1.28E‐02 3.47E‐05
Open in a new tab

Note: The position is based on GRCh37/hg19. We define the suggestive association using p value < 0.05, and statistically significant using Bonferroni corrected p value < 0.05/12 = 4.17E‐03, as three genetic variants and four COVID‐19 outcomes.

Abbreviations: Chr, chromosome; EA, effect allele; NEA, non‐effect allele.

3.9. Association between shared genes and COVID‐19 outcomes

Using high‐throughput plasma proteomic profiling from the meta‐analysis of two large independent cohorts including discovery cohorts (332 COVID‐19 patients and 150 controls) and replication cohorts (297 COVID‐19 patients and 76 controls), 32 we found association between plasma proteins corresponding to the shared genes and COVID‐19 outcomes, as provided in Table 2. Using the GWAS summary statistics from COVID19‐hg GWAS meta‐analyses round 7, we found suggestive association (p value < 0.05) or statistically significant association (Bonferroni corrected p value < 0.05/(117*3) = 1.42E‐04, as 117 genes and 3 COVID‐19 outcomes), as provided in Table 3. Fourteen genes are associated with COVID‐19 infection with p < 0.05, and LST1 (p = 3.72E‐48), TNXB (p = 1.23E‐45), APOC1 (p = 7.94E‐29), C2 (p = 9.77E‐16), and SCIMP (p = 3.47E‐10) are the top five significant signals. Eleven genes are associated with COVID‐19 ventilation with p < 0.05, and LST1 (p = 2.82E‐31), PSMB9 (p = 1.26E‐13), KIF1C (p = 1.17E‐10), SCIMP (p = 4.07E‐07), and AGER (p = 3.09E‐05) are the top five significant signals. Eight genes are associated with COVID‐19 death with p < 0.05, and LST1 (p = 2.40E‐16), KIF1C (p = 7.76E‐09), PSMB9 (p = 3.63E‐08), AGER (p = 5.75E‐06), and BCAM (p = 5.62E‐04) are the top five significant signals. LST1 not only is the most significant signal that associates with COVID‐19 infection, ventilation, and death, but also shows the largest effects on COVID‐19 infection (β = 0.3451), ventilation (β = 0.176), and larger effect on death (β = 0.1524). AGER indicates the largest effect on COVID‐19 death (β = 0.1876). Meanwhile, TNXB and APOC1 also have larger effects on COVID‐19 infection with β = −0.2418 and β = −0.1398, respectively. Here, we have provided more detailed results in Table 3. Figure 5 provides the abundance distributions of plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 in different COVID‐19 outcomes.

TABLE 3.

Association between shared genes and COVID‐19 outcomes.

Region Gene COVID‐19 infection COVID‐19 ventilation COVID‐19 death
β p Value β p Value β p Value
6p21.32 AGER 0.0884 2.14E‐02 0.1361 3.09E‐05 0.1876 5.75E‐06
ATF6B −0.0193 2.24E‐01 −0.0154 2.69E‐01 −0.0169 3.31E‐01
C2 0.0982 9.77E‐16 0.0085 3.80E‐01 0.0063 5.89E‐01
CSNK2B 0.0414 2.24E‐05 −0.003 6.92E‐01 −0.0047 6.31E‐01
HLA‐DQA2 −0.0396 3.31E‐05 0.0114 7.41E‐02 0.0079 3.02E‐01
LST1 0.3451 3.72E‐48 0.176 2.82E‐31 0.1524 2.40E‐16
PSMB9 −0.0222 1.00E‐01 −0.0768 1.26E‐13 −0.0714 3.63E‐08
TNXB −0.2418 1.23E‐45 −0.0327 2.40E‐02 −0.0225 2.19E‐01
8p21.2‐p21.1 CLU 0.0541 1.62E‐09 −0.004 6.03E‐01 −0.0133 1.74E‐01
17p13.2 CXCL16 −0.0548 2.14E‐04 0.048 1.58E‐04 0.0433 1.00E‐02
ENO3 0.0818 6.17E‐03 0.0673 5.25E‐03 0.0524 7.76E‐02
GP1BA −0.0225 5.75E‐02 −0.0187 3.09E‐02 0.0033 7.59E‐01
KIF1C 0.0031 7.59E‐01 0.0529 1.17E‐10 0.0588 7.76E‐09
SCIMP −0.0678 3.47E‐10 −0.0312 4.07E‐07 −0.0182 1.17E‐02
19q13.32 BCAM 0.033 3.47E‐03 0.0201 9.55E‐03 0.0338 5.62E‐04
CEACAM19 −0.0353 1.51E‐04 −0.0036 5.50E‐01 −0.0068 3.31E‐01
NECTIN2 −0.0106 3.98E‐01 0.0293 5.01E‐04 0.0337 8.13E‐04
PVR −0.0722 3.02E‐06 0.0115 1.86E‐01 0.0026 7.94E‐01
APOC1 −0.1398 7.94E‐29 −0.0284 2.09E‐03 −0.0216 5.01E‐02
Open in a new tab

Note: We define the suggestive association using p value < 0.05, and statistically significant using Bonferroni corrected p value < 0.05/(117*3) = 1.42E‐04, as 117 genes and three COVID‐19 outcomes.

FIGURE 5.

FIGURE 5

Open in a new tab

Abundance distributions of plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 in different COVID‐19 outcomes. Box plots were plotted using COVID‐19 Proteomics Data and Analytics Browser. (A) Abundance distributions of plasma proteins corresponding to LST1 in COVID‐19 infection (all cases vs. healthy controls) with p value = 3.72E‐48; (B) Abundance distributions of plasma proteins corresponding to LST1 in COVID‐19 ventilation (cases requiring ventilation vs. cases without ventilation support) with p value = 2.82E‐31; (C) Abundance distributions of plasma proteins corresponding to LST1 in COVID‐19 death (died cases vs. survived cases) with p value = 2.40E‐16; (D) Abundance distributions of plasma proteins corresponding to TNXB in COVID‐19 infection (all cases vs. healthy controls) with p value = 1.23E‐45; (E) Abundance distributions of plasma proteins corresponding to APOC1 in COVID‐19 infection (all cases vs. healthy controls) with p value = 7.94E‐29; (F) Abundance distributions of plasma proteins corresponding to AGER in COVID‐19 death (died cases vs. survived cases) with p value = 5.75E‐06.

4. DISCUSSION

Until now, growing evidence showed the involvement of ACE2 and related genes in the serum or plasma of AD or other diseases related to aging. AD cases had decreased ACE2 activity in the serum compared with normal control individuals. 33 Singh et al. 34 found the reduced levels of soluble ACE2 in plasma in stroke‐operated mice compared to sham mice. Parkinson's disease (PD) patients had significantly higher serum levels of ACE2 autoantibodies than controls. 35

Here, we explored the shared genetic etiology between AD and plasma ACE2 levels by a comprehensive analysis. In stage 1, we conducted a causal association analysis. We found a significant causal effect of genetically increased circulating ACE2 level on increased risk of AD. Our current finding is consistent with recent study evaluating the genetic association between circulating ACE2 and other COVID‐19 risk factors in both magnitude and direction. 14 Yang et al. 14 have identified significant positive genetic relations between circulating ACE2 and several COVID‐19 medical comorbidities including asthma, diabetes, coronary artery disease, hypertension, and other vascular disease‐related phenotypes. Yang et al. also investigated the genetic relation between circulating ACE2 and AD using AD GWAS dataset from IGAP 2013, including 17,008 AD and 37,154 controls. 36 However, they did not identify any significant genetic relation between circulating ACE2 and AD (rg = 0.0563, rg_SE = 0.0998, p = 0.573). Here, we used the largest AD GWAS in 94,437 individuals of European ancestry. 16 Therefore, the large‐scale AD GWAS dataset may contribute to identify more significant positive genetic relation.

In stage 2, we performed a cross‐trait association analysis, and found 19 genetic variants that were significantly associated with both AD and circulating ACE2 at the genome‐wide significance p < 5.00E‐08 at chromosome 6p21.32, 8p21.2‐p21.1, 17p13.2, and 19q13.32. In stage 3, we mapped these 19 genetic variants to 117 corresponding genes using positional mapping and eQTLs analysis. Interestingly, growing evidence supports our current findings that these genes are associated with AD and/or COVID‐19. At chromosome 6p21.32, HLA‐DRA, HLA‐DRB1, and HLA‐DQA1 are also identified to be AD risk genes. 16 A gene prioritization approach highlights HLA‐DRB1, HLA‐DRA, HLA‐DQA1, HLA‐DPA1, and HLA‐DRB5 to be the top candidate genes among 46 genes in the MHC locus. 16 Cell specific peripheral immune responses indicate that HLA‐DQA1, HLA‐DRB5, and HLA‐DPB1 are the most predictive of survival in CD16 monocytes from critical COVID‐19 patients. 37

At chromosome 8p21.2‐p21.1, EPHX2 encodes soluble epoxide hydrolase (sEH), a key enzyme for epoxyeicosatrienoic acid (EET) signaling. 38 , 39 sEH inhibition or Ephx2 deletion delays AD progression and alleviates AD pathology in mouse models of AD. 38 , 39 Evidence from 50 COVID‐19 patients and 94 age‐ and sex‐matched controls shows that SARS‐CoV‐2 serum had significantly increased sEH activity compared to age‐ and sex‐matched SARS‐CoV‐2‐negative group. 40 At chromosome 19q13.32, BCL3 is a nuclear member of the inhibitor of NF‐κB family, which regulates the NF‐κB signaling pathway. 41 Gene‐based test of AD GWAS datasets have identified BCL3 to be an AD susceptibility gene. 42 Differential gene expression analysis revealed a downregulation of BCL3 in COVID‐19 patients compared to controls in lung, liver, kidney, and heart tissues. 43 , 44

Tissue‐specific gene expression analysis showed that some genes were highly expressed across GTEx v8 54 tissue types, and others were highly expressed specifically in specific tissues, such as LST1 and AGER. Tissue‐specific enrichment analysis suggested that these genes were significantly upregulated in lung, spleen, and small intestine, and downregulated in brain tissues. Gene set enrichment analysis highlighted significantly enriched pathways involved in immune system, immune diseases, and infectious diseases. Our findings are in line with the pathology observed in post‐mortem tissues obtained from COVID‐19 patients. COVID‐19 causes multi‐organ dysfunction, and predominantly affects the lung, and also harms other body organs including spleen, small intestine, heart, gut, liver, kidneys, and brain. 45 , 46

In stage 4, we investigated the association of shared genetic variants and their corresponding genes with COVID‐19 outcomes. We identified three genetic variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 that associated with COVID‐19 infection, hospitalization, and severity. Importantly, these three genetic variants had the same directions of the effect alleles across AD, circulating ACE2, and COVID‐19 outcomes. Meanwhile, we found that the plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID‐19 infection, ventilation, and death. Interestingly, recent findings support the involvement of LST1, AGER, TNXB, and APOC1 in COVID‐19. Interestingly, recent findings support the involvement of LST1, AGER, TNXB, and APOC1 in COVID‐19. A large‐scale genome‐wide analysis has identified LST1 to be a COVID‐19 locus and a potential effector gene. 47 Single‐cell RNA‐Seq datasets in COVID‐19 patients suggested that LST1 may play a role in the effect of Angiotensin II receptor blocker on COVID‐19‐related mortality. 48 Therefore, LST1 not only contributes to predict the COVID‐19 outcomes, but also may be a potential COVID‐19 treatment target. AGER is also named RAGE, and its plasma protein level was identified to be significantly upregulated in ICU COVID‐19 patients compared to controls. 49 High level of soluble RAGE is associated with a greater risk of mortality in COVID‐19 patients treated with dexamethasone, 50 and is considered to be a biomarker of COVID‐19 disease severity and indicator of the need for mechanical ventilation, acute respiratory distress syndrome and mortality. 51

Our current study still has some limitations. First, it is important to check the results of ACE2 and related genes in the serum in three stages of AD (early stage, middle stage, and late stage) and normal controls. However, there are no large‐scale publicly available serum data from normal controls and AD including early stage, middle stage, and late stage. We will further evaluate the ACE2 and related genes in the serum when relevant data is publicly available in future. Second, in addition to ACE2, there are some other factors increase the risk of COVID‐19. Evidence shows that several genes contribute to viral entry into the cell and viral persistence including TMPRSS2, TPCN2, TMPRSS4, NRP1, CTSL, CD147, DPP4, and TMEM106B. 5 , 52

Together, our findings suggest the shared genetic etiology between plasma ACE2 levels and AD, and plasma ACE2 levels may partially explain the link between AD and COVID‐19. Our findings have potential clinical implications. On the one hand, AD patients with plasma ACE2 levels may have increased risk of COVID‐19 infection, hospitalization, and mortality, and assessment of plasma ACE2 levels may be a means of identifying AD patients at high risk for adverse COVID‐19 outcomes. On the other hand, COVID‐19 patients with plasma ACE2 levels may have increased risk of AD, and assessment of plasma ACE2 levels may be a means of identifying COVID‐19 patients at high risk of AD.

AUTHOR CONTRIBUTIONS

GYL, YZ, and YC conceived and initiated the project. GYL, YZ, TW, and ZFH analyzed the data, and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.

FUNDING INFORMATION

This work was supported by funding from the National Natural Science Foundation of China (Grant Nos 82071212, and 81901181), Natural Science Foundation of Heilongjiang Province (Grant No. LH2019H076), Beijing Natural Science Funds for Distinguished Young Scholar (Grant No. JQ21022), the Mathematical Tianyuan Fund of the National Natural Science Foundation of China (Grant No. 12026414), and Beijing Ten Thousand Talents Project (Grant No. 2020A15). This work was also partially supported by funding from the Science and Technology Beijing One Hundred Leading Talent Training Project (Z141107001514006), the Beijing Municipal Administration of Hospitals' Mission Plan (SML20150802), the Funds of Academic Promotion Programme of Shandong First Medical University & Shandong Academy of Medical Sciences (Nos 2019QL016, 2019PT007).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

CONSENT STATEMENT

This article contains human participants collected by several studies performed by previous studies. All participants gave informed consent in all the corresponding original studies. Here, our study is based on the publicly available datasets, and not the individual‐level data. Therefore, consent was not necessary.

Supporting information

Tables S1–S11:

CNS-30-e14873-s001.xlsx (171.8KB, xlsx)

ACKNOWLEDGMENTS

We thank the International Genomics of Alzheimer's Project (IGAP) for the AD GWAS summary statistics, and Xia Shen for the ACE2 GWAS summary statistics. We thank the Genotype‐Tissue Expression (GTEx) Project. The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from: https://www.gtexportal.org/home/ the GTEx Portal (GTEx Analysis Release V8 (dbGaP Accession phs000424.v8.p2)) on December 16, 2022. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i‐Select chips were funded by the French National Foundation on AD and related disorders. EADI was supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (Grant No. 503480), Alzheimer's Research UK (Grant No. 503176), the Wellcome Trust (Grant No. 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) Grant Nos 01GI0102, 01GI0711, and 01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01‐AG‐12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer's Association grant ADGC‐10‐196728.

Zhang Y, Xu F, Wang T, et al. Shared genetics and causal association between plasma levels of SARS‐CoV‐2 entry receptor ACE2 and Alzheimer's disease. CNS Neurosci Ther. 2024;30:e14873. doi: 10.1111/cns.14873

Contributor Information

Yan Chen, Email: bingyan-1209@163.com.

Guiyou Liu, Email: liuguiyou1981@163.com.

DATA AVAILABILITY STATEMENT

All relevant data are within the paper. The authors confirm that all data underlying the findings are either fully available without restriction through consortia websites, or may be made available from consortia upon request. ACE2 GWAS summary statistics: https://figshare.com/articles/dataset/SCALLOP_ACE2_GWAS_Summary_Statistics/19189307. AD GWAS summary statistics: https://www.niagads.org/datasets/ng00075. COVID19‐hg GWAS meta‐analyses round 7: https://www.covid19hg.org/results/r7/. COVID‐19 Proteomics Data and Analytics Browser: https://covid.proteomics.wustl.edu/. GTEx: https://www.gtexportal.org/. eQTLGen Consortium: https://www.eqtlgen.org/.

REFERENCES

  • 1. Wang Q, Davis PB, Gurney ME, Xu R. COVID‐19 and dementia: analyses of risk, disparity, and outcomes from electronic health records in the US. Alzheimers Dement. 2021;17(8):1297‐1306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Wang Y, Li M, Kazis LE, Xia W. Clinical outcomes of COVID‐19 infection among patients with Alzheimer's disease or mild cognitive impairment. Alzheimers Dement. 2022;18(5):911‐923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Zarifkar P, Peinkhofer C, Benros ME, Kondziella D. Frequency of neurological diseases after COVID‐19, influenza A/B and bacterial pneumonia. Front Neurol. 2022;13:904796. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Wang L, Davis PB, Volkow ND, Berger NA, Kaelber DC, Xu R. Association of COVID‐19 with new‐onset Alzheimer's disease. J Alzheimers Dis. 2022;89(2):411‐414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Hoffmann M, Kleine‐Weber H, Schroeder S, et al. SARS‐CoV‐2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181(2):271‐280 e278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Kragstrup TW, Singh HS, Grundberg I, et al. Plasma ACE2 predicts outcome of COVID‐19 in hospitalized patients. PLoS ONE. 2021;16(6):e0252799. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Furuhashi M, Sakai A, Tanaka M, et al. Distinct regulation of U‐ACE2 and P‐ACE2 (urinary and plasma angiotensin‐converting enzyme 2) in a Japanese general population. Hypertension. 2021;78(4):1138‐1149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Patel SK, Juno JA, Lee WS, et al. Plasma ACE2 activity is persistently elevated following SARS‐CoV‐2 infection: implications for COVID‐19 pathogenesis and consequences. Eur Respir J. 2021;57(5):2003730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Batlle D, Monteil V, Garreta E, et al. Evidence in favor of the essentiality of human cell membrane‐bound ACE2 and against soluble ACE2 for SARS‐CoV‐2 infectivity. Cell. 2022;185(11):1837‐1839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Lundstrom A, Ziegler L, Havervall S, et al. Soluble angiotensin‐converting enzyme 2 is transiently elevated in COVID‐19 and correlates with specific inflammatory and endothelial markers. J Med Virol. 2021;93(10):5908‐5916. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Fagyas M, Fejes Z, Suto R, et al. Circulating ACE2 activity predicts mortality and disease severity in hospitalized COVID‐19 patients. Int J Infect Dis. 2022;115:8‐16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Ramchand J, Burrell LM. Circulating ACE2: a novel biomarker of cardiovascular risk. Lancet. 2020;396(10256):937‐939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Narula S, Yusuf S, Chong M, et al. Plasma ACE2 and risk of death or cardiometabolic diseases: a case‐cohort analysis. Lancet. 2020;396(10256):968‐976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Yang Z, Macdonald‐Dunlop E, Chen J, et al. Genetic landscape of the ACE2 coronavirus receptor. Circulation. 2022;145(18):1398‐1411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Cui H, Su S, Cao Y, Ma C, Qiu W. The altered anatomical distribution of ACE2 in the brain with Alzheimer's disease pathology. Front Cell Dev Biol. 2021;9:684874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Kunkle BW, Grenier‐Boley B, Sims R, et al. Genetic meta‐analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet. 2019;51(3):414‐430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR‐egger method. Eur J Epidemiol. 2017;32(5):377‐389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression. Int J Epidemiol. 2015;44(2):512‐525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734‐1739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta‐analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190‐2191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930‐D934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. GTEx Consortium . Human genomics. The genotype‐tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648‐660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Sieberts SK, Perumal TM, Carrasquillo MM, et al. Large eQTL meta‐analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. Sci Data. 2020;7(1):340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Lopes KP, Snijders GJL, Humphrey J, et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022;54(1):4‐17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Vosa U, Claringbould A, Westra HJ, et al. Large‐scale cis‐ and trans‐eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300‐1310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Lloyd‐Jones LR, Holloway A, McRae A, et al. The genetic architecture of gene expression in peripheral blood. Am J Hum Genet. 2017;100(2):228‐237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Zhernakova DV, Deelen P, Vermaat M, et al. Identification of context‐dependent expression quantitative trait loci in whole blood. Nat Genet. 2017;49(1):139‐145. [DOI] [PubMed] [Google Scholar]
  • 28. Joehanes R, Zhang X, Huan T, et al. Integrated genome‐wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 2017;18(1):16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199‐W205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. COVID‐19 Host Genetics Initiative . Mapping the human genetic architecture of COVID‐19. Nature. 2021;600(7889):472‐477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Wang L, Western D, Timsina J, et al. Plasma proteomics of SARS‐CoV‐2 infection and severity reveals impact on Alzheimer's and coronary disease pathways. iScience. 2023;26(4):106408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Liu S, Liu J, Miura Y, et al. Conversion of Abeta43 to Abeta40 by the successive action of angiotensin‐converting enzyme 2 and angiotensin‐converting enzyme. J Neurosci Res. 2014;92(9):1178‐1186. [DOI] [PubMed] [Google Scholar]
  • 34. Singh V, Beer A, Kraus A, et al. Stroke increases the expression of ACE2, the SARS‐CoV‐2 binding receptor, in murine lungs. Brain Behav Immun. 2021;94:458‐462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Labandeira CM, Pedrosa MA, Quijano A, et al. Angiotensin type‐1 receptor and ACE2 autoantibodies in Parkinson s disease. NPJ Parkinsons Dis. 2022;8(1):76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Lambert JC, Ibrahim‐Verbaas CA, Harold D, et al. Meta‐analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45(12):1452‐1458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Amrute JM, Perry AM, Anand G, et al. Cell specific peripheral immune responses predict survival in critical COVID‐19 patients. Nat Commun. 2022;13(1):882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Lee HT, Lee KI, Chen CH, Lee TS. Genetic deletion of soluble epoxide hydrolase delays the progression of Alzheimer's disease. J Neuroinflammation. 2019;16(1):267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Chen W, Wang M, Zhu M, Xiong W, Qin X, Zhu X. 14,15‐Epoxyeicosatrienoic acid alleviates pathology in a mouse model of Alzheimer's disease. J Neurosci. 2020;40(42):8188‐8203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Turnbull J, Jha RR, Ortori CA, et al. Serum levels of Proinflammatory lipid mediators and specialized Proresolving molecules are increased in patients with severe acute respiratory syndrome coronavirus 2 and correlate with markers of the adaptive immune response. J Infect Dis. 2022;225(12):2142‐2154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Poveda J, Sanz AB, Carrasco S, et al. Bcl3: a regulator of NF‐κB inducible by TWEAK in acute kidney injury with anti‐inflammatory and antiapoptotic properties in tubular cells. Exp Mol Med. 2017;49(7):e352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Nazarian A, Yashin AI, Kulminski AM. Genome‐wide analysis of genetic predisposition to Alzheimer's disease and related sex disparities. Alzheimers Res Ther. 2019;11(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Hammoudeh SM, Hammoudeh AM, Bhamidimarri PM, et al. Systems immunology analysis reveals the contribution of pulmonary and Extrapulmonary tissues to the Immunopathogenesis of severe COVID‐19 patients. Front Immunol. 2021;12:595150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Khan MA, Islam A. SARS‐CoV‐2 proteins exploit Host's genetic and epigenetic mediators for the annexation of key host signaling pathways. Front Mol Biosci. 2020;7:598583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID‐19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat Rev Cardiol. 2020;17(9):543‐558. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Merad M, Martin JC. Pathological inflammation in patients with COVID‐19: a key role for monocytes and macrophages. Nat Rev Immunol. 2020;20(6):355‐362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Horowitz JE, Kosmicki JA, Damask A, et al. Genome‐wide analysis provides genetic evidence that ACE2 influences COVID‐19 risk and yields risk scores associated with severe disease. Nat Genet. 2022;54(4):382‐392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Zhao L, Li Y, Yi W, et al. The clinical and bioinformatics analysis for the role of antihypertension drugs on mortality among patients with hypertension hospitalized with COVID‐19. J Med Virol. 2022;94(10):4727‐4734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Gutmann C, Takov K, Burnap SA, et al. SARS‐CoV‐2 RNAemia and proteomic trajectories inform prognostication in COVID‐19 patients admitted to intensive care. Nat Commun. 2021;12(1):3406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Butcher L, Zaldua JC, Carnicero JA, et al. High levels of soluble RAGE are associated with a greater risk of mortality in COVID‐19 patients treated with dexamethasone. Respir Res. 2022;23(1):303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Lim A, Radujkovic A, Weigand MA, Merle U. Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COVID‐19 disease severity and indicator of the need for mechanical ventilation, ARDS and mortality. Ann Intensive Care. 2021;11(1):50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Rockx B, Kuiken T, Herfst S, et al. Comparative pathogenesis of COVID‐19, MERS, and SARS in a nonhuman primate model. Science. 2020;368(6494):1012‐1015. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Tables S1–S11:

CNS-30-e14873-s001.xlsx (171.8KB, xlsx)

Data Availability Statement

All relevant data are within the paper. The authors confirm that all data underlying the findings are either fully available without restriction through consortia websites, or may be made available from consortia upon request. ACE2 GWAS summary statistics: https://figshare.com/articles/dataset/SCALLOP_ACE2_GWAS_Summary_Statistics/19189307. AD GWAS summary statistics: https://www.niagads.org/datasets/ng00075. COVID19‐hg GWAS meta‐analyses round 7: https://www.covid19hg.org/results/r7/. COVID‐19 Proteomics Data and Analytics Browser: https://covid.proteomics.wustl.edu/. GTEx: https://www.gtexportal.org/. eQTLGen Consortium: https://www.eqtlgen.org/.

Articles from CNS Neuroscience & Therapeutics are provided here courtesy of Wiley

RESOURCES