XPNPEP2 is genome-wide significant in a COVID-19 +ve/-ve analysis of UK Biobank participants

On April 19 I wrote about a biologically interesting signal in XPNPEP2 in a comparison of COVID-19 positive and negative individuals who have participated in UK Biobank. In the latest data release, which represents a doubling in sample size for this analysis, the hit is genome-wide significant in an analysis of white Europeans. However, it is not genome-wide significant when analysing individuals with all ancestries.

The top hit is rs2076205, a single nucleotide polymorphism whose common allele occurs in 68% of COVID-19 negative individuals and 79% of COVID-19 positive individuals. It has a p-value of 1.1 in 10 million, which is below the widely-used threshold of 5 in 10 million that is often deemed statistically interesting.

In the analysis of individuals of all ancestries, the signal is substantially muted, and no variants are genome-wide significant. The reasons for the difference in analysis are still unclear, but the SNP does appear to be strongly population stratified.

XPNPEP2 is of interest because its product's normal function (Aminopeptidase P) includes degrading bradykinin, also degraded by ACE2. The involvement of XPNPEP2 variants in ACE inhibitor-associated angioedema has been proposed as combining with the drug to produce higher circulating bradykinin.

Bradykinin has already been suggested as an important mediator of the COVID-19 because of clinical similarities between COVID-19 and ACE inhibitor induced angioedema. Since SARS-CoV-2 gains entry to the cell via ACE2, the virus may inhibit its normal role and contribute to elevated bradykinin. The dry cough associated with COVID-19 has been likened to the 'bradykinin cough' associated with the use of ACE inhibitors.

Interestingly, bradykinin inhibitors such as icatibant already exist and are used in ACE inhibitor induced angioedema. However, to make the jump that such drugs might be useful against COVID-19 on this evidence alone is speculative, and there are several caveats to the association itself, including the requirement to replicate the effect in an independent population. There is a need to understand the robustness of the association to population stratification and other possible confounders.

Update

This post was updated a second time on 8 May to include inpatient status in the subgroup analysis.

To dig a little deeper into the result I have looked at effect sizes in different groups defined by

• Ethnicity: British (self reported), euranc (white European genetically) or any (these are the only groupings so far with sufficient sample size)
• Sex: female, male or any
• Close relatives: whether they are excluded (as above) or included in the analysis (larger sample size but potential for family clusters to over-influence the results)
• Inpatients: whether individuals were in (ever inpatients when tested) or nin (never inpatients when tested)

The results suggest that the effect is both larger in magnitude and stronger in statistical significance in males. But this difference depends on inpatient status: the signal appears strong and similar in magnitude among male and female inpatients. In non-inpatients, the signal is non-significant in almost every subgroup. Including individuals not identified genetically as white European slightly dilutes the signal, as does including close relatives.

The graph shows the effect (and 95% confidence interval) of the rarer allele: negative coefficients (on a log odds scale) indicate that the rare allele reduces the risk of returning a positive test. To illustrate, an effect size of -0.35 would imply that a male with the rare allele is 30% less likely to test positive than a male with the common allele.

Some technical details: the effect size analysis is based on a logistic regression, whereas the GWAS above uses the SAIGE tool. The covariates included are: sex, age, age*age, sex*age and genetic principal components (40 in the logistic regression, 20 in the GWAS). The results are being contributed to the COVID-19 Host Genetics Initiative.