ACE 2 Coding Variants: A Potential X-linked Risk Factor for COVID-19 Disease
Bibliographic record
Abstract
Abstract Viral genetic variants are widely known to influence disease progression among infected humans. Given the recent and rapid emergence of pandemic SARS-CoV-2 infection, the cause of COVID-19 disease, viral protein variants have attracted research interest. However, little has yet been written about genetic risk factors among human hosts. Human genetic variation has proven to affect disease progression and outcome for important diseases such as HIV infection and malaria infestation. The fact that the human ACE2 protein is encoded on the X chromosome means that males who carry rare ACE2 coding variants will express those variants in all ACE2-expressing cells, whereas females will typically express those variants in a mosaic distribution determined by early X-inactivation events. This sex-based difference in ACE2 expression has unique implications for epidemiological studies designed to assess host genetic factors influencing progression from asymptomatic SARS-coV-2 infection to COVID-19. Here we present theoretical modelling of rare ACE2 coding variants documented to occur naturally in several human superpopulations and subpopulations, and show that rare variants predicted to affect the binding of ACE2 to the SARS-CoV-2 spike protein exist in people. Though the rs4646116 (p.Lys26Arg) allele is found in 1 in 70 Ashkenazi Jewish males, and in 1 in 172 non-Finnish European males, this allele is found at higher frequencies in females. Furthermore, the class of missense ACE2 alleles predicted to affect SARS-CoV-2 binding are found in aggregate among 1.43% and 2.16% of Ashkenazi males and females, respectively, as well as in 0.58% and 1.24% of European males and females outside of Finland. These alleles are rarer in other population groups, and almost absent from East Asians genotyped to date. Though we are aware that full genome-wide and exome-wide sequencing studies may ultimately be required to assess human genetic susceptibility to SARS-CoV-2 fully, we argue on the basis of strong prior probabilities that genotyping of this class of alleles is justified in cases of atypical SARS-CoV-2 diseases, such as asymptomatic super-spreaders (if any are identified), and in neonatal/paediatric-onset COVID-19 disease. Even relatively rare susceptibility factors (1% or fewer carriers) may become quantitatively important in the context of hundreds of thousands of infections. A small number of asymptomatic carriers, or a small number of super-spreaders, or a small segment of the population that is disproportionately likely to require intensive care, can magnify the medical, social and economic impacts of a pandemic of this size. The speed of the pandemic and the large number of affected cases worldwide justify efforts to identify all possible risk factors for adverse outcomes, including efforts to identify genetic susceptibility factors in human hosts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".