(2024) 22:337
Pearson and Novembre ﻿BMC Medicine
https://doi.org/10.1186/s12916-024-03539-0

BMC Medicine

CORRESPONDENCE

Open AccessNo evidence that ACE2 or TMPRSS2 drive
population disparity in COVID risks
Nathaniel M. Pearson1*   and John Novembre2

Abstract
Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data,
run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population’svariants than do small samples) as if showing real and congruent population differences for the two genes, rather
than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empiricalfindings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2—
and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such
more risk-informative loci.
Keywords ACE2, COVID, COVID19, Functional prediction, GWAS, Host genetics, Human genes, Immunity, Infection,
Polygenic risk, Population genetics, Population structure, Rare variants, Sample design, Sample size, Sampling, SARSCoV2, TMPRSS2
Background
In mid-2020, concurrent with early empirical inquiry intoroles of host genomic variation in SARS-CoV2 infection[1–5], Hou et al. set out to offer predictive guidance forsuch efforts, by assessing pre-pandemic public DNA datafrom two human genes, ACE2 and TMPRSS2, whose protein products were known to interact with other coronaviruses [6].
Pooling public genotype data sampled from various
human groups, without phenotypes, they shortlisted
ACE2 and TMPRSS2 variants that some computational
heuristics predicted likely to alter protein function, andfound that most such variants (each, typically, very rare)*Correspondence:
Nathaniel M. Pearson
nathaniel.pearson@gmail.com
1
Root Deep Insight, Boston, MA, USA
2
Department of Human Genetics, University of Chicago, Chicago, IL, USA

came from subsets of data labeled “African/AfricanAmerican” or “Non-Finnish European” versus labeled
otherwise (e.g., “East Asian”).1 This, they held, suggested
real-world population disparities in ACE2 and TMPRSS2
functional variant load, similar for both genes, that mightin turn drive population differences in COVID outcomes.

Errant interpretation of genotype data
Alas, Hou et al. had neglected a basic feature of the
public data they used—lopsided population sample
sizes—that made their summary findings artifactually
likely even with no difference between real populations.

1

Denoted by Hou et al. as, respectively, “African/African-American” and“Non-Finnish [and implicitly non-Amish, non-Ashkenazi] European” in
Hou et al., these classifications group genetically and culturally disparate
peoples, across wide modern and ancestral geographic ranges, at different
scales, under single umbrella terms.

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Pearson and Novembre ﻿BMC Medicine(2024) 22:337

Page 2 of 7

Fig. 1 Population-specific variant tallies in Hou et al. [6] reflect lopsided sampling. Scatterplots of population-specific tallies (y-axis) of shortlisted
variants in ACE2 (orange) or TMPRSS2 (blue), by sample size (x-axis; values denote maximum sampled alleles among shortlisted variant-position
genotypes for that gene in gnomAD (v.3.0) + NHLBI-GO ESP6500 genotypes, as pooled by Hou et al. [6]). Datapoints mark values for African/
African-American (AFR; TMPRSS2 count excludes 1 variant (p.Pro444Leu) reported by Hou et al., but not in public data, and not consistent
with reference variant at given protein residue); Amish (AMI); Ashkenazi (ASH); east Asian (EAS); south Asian (SAS); non-Finnish, non-Amish,
non-Ashkenazi European (EUR; TMPRSS2 count excludes 1 variant (p.Gly6Arg) reported by Hou et al., but not in public data, and not consistent
with reference variant at given protein residue); Finnish (FIN in Hou et al. [6]); Latino/Admixed American (AMR; ACE2 count includes 2 variants
wrongly omitted from this population by Hou et al.); or other (oth; PNA in Hou et al. [6]; ACE2 count excludes 2 variants wrongly tallied in thispopulation by Hou et al.). Best-fit trends (dashed) mark origin-rooted linear regression, conservatively proxying independent (versus cumulative)
discovery of potentially selection-constrained (versus selectively neutral) variants in samples from variably sized, mutually diverged populations(versus one steady-sized randomly mating population). We note that even in the contrasting case of cumulative discovery in a steady-sized
population, variants under selective constraint (as Hou et al. sought to tally) tend to accrue quasi-linearly, rather than strictly logarithmically,
with increasing overall sample size [8–11]

Specifically, they had pooled genotypes from > 36,000“non-Finnish European” and > 23,000 “African/AfricanAmerican” people, but far fewer “Amish” (450), “Ashkenazi” (1662), “East Asian” (1567), or other (< 15,000)
people.2 As such, even if variants were uniformly
2Compounding their lopsided sampling, Hou et al. also reported adding, tothe foregoing totals, duplicate copies of many non-Amish, non-Ashkenazi
genotypes in particular (from the 1000 Genomes Project dataset, which
their main gnomAD v3.0 data already included), further confounding any
frequency estimation of individual variants.

distributed across real populations, Hou et al. would
likelier find a given rare variant in either of their big
samples (“African/African-American” or “non-Finnish
European”) than in any of their much smaller samples ofother groups.
Consistent with such artifact, the number of ACE2 orTMPRSS2 variants Hou et al. shortlisted for a given population scales well with how many genotypes they sampled from that population (Fig. 1, origin-rooted linear

Pearson and Novembre ﻿BMC Medicine(2024) 22:337

r2 > 0.95 for both genes).3,4 Long known in theoretical andempirical population genetics [7, 11], the sampling effectapparent in Hou et al.’s summary findings reflects a simple fact: much as counting more of a forest’s birds canhelp document rare taxa living there, sequencing more ofa population’s gene copies helps document rare variants
among them. While other factors shape the emergenceand fate of such variants, and their rate of discovery
with increasing sample size [7–10, 12, 13], reliably finding and quantifying them entails sampling from many
individuals.
As such, current public genotype data inherently document more of the variants actually circulating in some
populations than of those actually circulating in otherpopulations—and allow more precise estimation, in thesame best-sampled (if at all) groups, of each variant’s
actual frequency (as may figure in functional prediction) or absence. For example, to be 90% confident thateven the least rare shortlisted ACE2 variant in non-Finnish, non-Amish, non-Ashkenazi European sample data
(p.R219C) is not actually more common in Amish, Ashkenazi, and/or Finnish populations, despite its absencein those groups’ small sample data, would require sampling > 22,700 additional Amish, Ashkenazi, and Finnish
copies of ACE2.5
Beyond summary tallies, none of Hou et al.’s shortlisted
variants reliably proxies any population to begin with.
One (TMPRSS2 p.V160M) appears in all studied populations—and in many individuals in each—so offers scantground to guess which population(s) a carrier comesfrom. All 130 other shortlisted variants appear too rare in
every population to typify members of any of them (evenin aggregate, their data suggest that > 96% of people inevery studied population likely carry none of those rarevariants (Fig. 2)).6 And as real populations also harbor

Page 3 of 7

unsampled but functionally relevant variants, whose
effects on basic protein function (let alone response to aparticular virus) current heuristics cannot reliably predict [14–16], the tallies and predictions of Hou et al. do
not warrant positing that ACE2 or TMPRSS2 (let aloneboth) functions worse with respect to SARS-CoV2 inany human population (let alone particular ones) than inothers.
In principle, real populations may indeed differ, if modestly, in functionally relevant patterns of variation in agene (or even genome-wide), pending their histories. All
else equal, for example, big populations tend to accrue
and keep more genetic variation (especially if non-harmful) than do small populations [12, 18, 19]; fast-growing
populations to accrue more new rare variation in particular [9, 20]; and genome segments under strong directional selection, in a given population’s environment, may(along with flanking segments coupled to them without
recombination) tend to lose such rare variation in particular [10].
But as human genes thus vary in summary patterns ofvariation, via potentially population-distinctive histories,
meaningfully comparing such patterns requires sampling
well, and assessing not just which variants appear at all
in data sampled from a population, but the summary distributions of their estimated frequencies (respective proportions of sampled gene copies harboring them).
To that end, frequency-sensitive summary metrics7
show less variation in human ACE2, both within andbetween most human populations,8 than for most other
X-borne9 or autosomal [21, 22, 28, 29] human genes, limiting the extent to which populations’ distinctive histories
may yield disparate patterns of variation. By comparison,such well grounded summary metrics show more overall variation in human TMPRSS2 [21]—much of it sharedacross populations, in varied patterns that reflect thecross-regional spread of variants old (and generally nonharmful) enough to have become common.

3

While cumulative discovery of selectively neutral variants, in a steadysized randomly mating population, tends to grow logarithmically withsample size [7], Hou et al. instead tallied selection-liable (protein-altering)
variants, non-cumulatively (i.e., via independent single samplings) frommutually diverged, variably sized, and potentially variably growing populations. The foregoing factors more conservatively suit linear than logarithmic(or other presumptive higher-order) regression [8–10].

4

Further, each ACE2 variant tally, and all but two TMPRSS2 variant tallies (those for the highly recently admixed “Admixed Latino” and “Population Not Assigned” samples), accords the 95%-confident expectation for arandom sample of its respective size, without replacement, from total genotypes studied for that gene, given total shortlisted variant incidences.
5
Confidence bound presumes accurate estimate of variant frequency in
non-Finnish, non-Amish, non-Ashkenazi Europeans, and large randomly
mating populations such that sampling probabilities approximate binomial
expectation.
6

Estimates (products of binomial probabilities) presume that variants
assort randomly, independently, at sampled population-specific frequencies,in half-XX/half-XY populaces.

7

For example, copy-pairwise per-site mismatch probability (nucleotide
diversity), Tajima’s D, and Wright’s FST.

8

Save for in mutually neighboring Baka, Bakola, and Bedzan peoples,
among whom three protein-coding variants rare or absent in other central
African and broader populations have become moderately common (perhaps via shared linkage to regulatory variants favored by local selection
over many past generations) [21]. A smaller survey [22] that reported somemetrics in European-Americans suggested excess intermediate-frequency
variation; but those metrics tend to spuriously infer such skew in small samples [23]. By contrast, in the same study [21], metrics robust to sample size[23] affirmed less overall variation in ACE2 than in other X-linked genes.

9

Overall, X-specific genes such as ACE2 tend, theoretically and empirically, to vary less among people overall or within a particular population,
but more (relative to such scant overall variation) between randomly chosenmembers of different populations, than do autosomal genes [24–27].

Pearson and Novembre ﻿BMC Medicine(2024) 22:337

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Fig. 2 Nearly everyone, in all studied populations, likely lacks all ostensibly population-distinctive variants shortlisted by Hou et al. [6]. Bar
plot of estimated percentage of people in each studied population who likely have none of the 130 notionally population-distinctive (i.e.,
absent in sample data from at least one studied population) ACE2 and TMPRSS2 variants shortlisted (without empirical evidence for any effecton protein function or other physiology, and omitting many other potentially functionally relevant variants in all populations) by Hou et al. [6].
Estimates (product of binomial probabilities) presume variants assort randomly, independently, at sampled population-specific frequencies,in half-XX/half-XY populaces. AFR = African/African-American; AMI = Amish; ASH = Ashkenazi; EAS = east Asian; SAS = south Asian; EUR = non-Finnish,
non-Amish, non-Ashkenazi European; FIN = Finnish; AMR = Latino/Admixed American; oth = other. Values may underestimate true minimum
region-wide percentage, as (i) the least rare such variant (ACE2 p.L731F), which most strongly suppresses the AFR estimate, appears mainly in datafrom over-proportionately sampled west Africa, more so than in data yet sampled from likewise populous peoples elsewhere in Africa and diaspora[17]; and (ii) any pairwise linkage among shortlisted variants would increase the proportion of people inheriting neither variant in such pairs

Importantly, even beyond the two genes’ contrasting
patterns of variation, pandemic-long cohort outcomes
have not shown variation in either ACE2 or TMPRSS2
to shape personal COVID risks nearly as significantly
as variation elsewhere in our genomes—including themost strongly and significantly risk-shaping locus, on
the short arm of chromosome 3; the ABO blood grouplocus on chromosome 9; and other autosomal loci [1–5,
17, 30]. Some non-protein-altering variants in ACE2 andTMPRSS2 have met multiple-test-stringent significancecriteria for association with risks of SARS-CoV2 infection (an ACE2 regulatory variant cluster) or severity(TMPRSS2 intronic variant), but their significance falls

short of that evident for other loci. And among variants
shortlisted by Hou et al., only one (the relatively common
TMPRSS2 p.V160M) has shown even suggestive (notmultiple-test-stringent) evidence for association with
any COVID risk [30–33]—while broader tests, tuned andpowered specifically to detect rare variant association per
se in clinically characterized population cohorts, havenot implicated shortlisted or other rare protein-coding
variation in either gene in COVID risks [34, 35].
Moreover, across all COVID-implicated loci, riskinformative variants differ in their population distributions and inferred effects, in many cases in partial
counter-balance to one another. The ACE2-regulatory

Pearson and Novembre ﻿BMC Medicine(2024) 22:337

rs190509934C variant, for example, associates significantly with below-average risk of SARS-CoV2 infection (and suggestively with below-average risk of severe
COVID), but appears least rare in a studied broad population (south Asian) in which other loci harbor variant
loads most strongly associated with above-average risk ofinfection (and severe disease) [36, 37].
Altogether, such mixes of above- and below-averagerisk-associated variants in human genomes worldwide
leave a broad range of risk-relevant personal variant load
within every studied human population [38–41]. And
those loads, in turn, explain < 10% of personal variability
in COVID risks evident, so far, in clinically characterized cohorts [39], while other factors, such as age, background health, and immune exposures, show far stronger
effects.10 As such, now-ample data suggest that COVID
incidence and severity likely differ among human populations far less by genetics than by factors such as age
structure, past and ongoing immune exposures, comorbidity prevalences, and access to effective health interventions [4, 17, 30, 39, 40, 45–52]—and pointedly, do notsupport speculation that variation in ACE2 or TMPRSS2
drives net population disparity in genetically attributable(or overall) COVID risks.11

Conclusions
Speculating early in the pandemic, on potential COVIDrelevance of variation in two of humanity’s many genes,
Hou et al. understandably settled for predictive heuristics
in lieu of clinical data. But in tallying shortlisted ACE2
and TMPRSS2 variants among populations, they mistook

10
Even for severe COVID (Hou et al.’s focus), sensitive liability estimates[42] at conservatively estimated population prevalences [43] were < 10%[30]. One non-genetic (pedigree-based) survey suggested possibly greater
heritability, still surpassed by inferred effects of residence environment
alone, but in lacking genetic data, could not control for close-kin or/andfamily size biases in exposures within, between, and beyond residences (e.g.,in room-sharing, social/care visitation, and/or similarity of work), or forfamily-wise differences in age or non-residence environmental exposures[44]. And on the specific question of population (versus personal or family) disparity in inborn severe COVID risk, early speculation from sparsely
typed small samples [38] has not borne out in well powered cohort data [39,45].

11Specifically, empirically estimated variance in population-mean genomically attributable risk (the summed product of significantly risk-associated variants’ sampling-inferred genotype frequencies and empiricallyestimated additive risk contributions [36]), for the focal outcome of Houet al. (severe COVID) [6], in the same populations they studied, is slightly
greater (~ 0.02089) when omitting ACE2 variation than when including it (~ 0.02004) or when also including TMPRSS2 variation (0.01822)[37]. These estimates warrant much caution, as variant-attributable effects
themselves vary among populations [30] (plausibly via population-stratified
genome-wide variation, local haplotype structure, environmental modifiers [53–56], and other factors), but suggest that, all else equal, ACE2 and(if relevant) TMPRSS2 variation unlikely exacerbates any net disparity insuch additively genomically attributable COVID risk among studied humanpopulations.

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a sample size artifact as if evidence of population differences—and tried to proxy population-representative
gene function by tallying shortlisted variants found at all,instead of summing empirically estimated, genotype frequency-scaled effects. Though their shortlist offered wellintended, if unvalidated, candidates for early-pandemic
study, genome-wide empirical insights have eclipsed
its utility—while leaving, unaddressed, their artifactual
summary claims. And those claims, in turn, have drawn
credulous citation in public discourse, hindering understanding of COVID risks and of human genetic diversity
itself [57–59].
While pitfalls of methodology and interpretation havelong plagued basic and clinical research [60–67], public
discourse invoking Hou et al. [6] highlights how platforms to usefully share and discuss such research can also
virally spread faulty inferences missed by authors andreviewers, misleading not just researchers and clinicians,
but also lay-people who may rely on published science inpersonal, professional, family, and civic decisions [64].
As such, we hope that correcting basic errors that misled Hou et al., and those citing their work, helps right therecord on human genomic variation in ACE2, TMPRSS2,
and loci more informative of COVID risks—and, further,encourages critical stringency in the interpretation of
population genetic and other data, amid efforts [68, 69]
to more promptly and soundly validate research published, and subsequently invoked, in and between societal
crises.
Abbreviations
ACE2	Angiotensin converting enzyme 2 [gene]
AMI	Amish
AMR	[Latina/o] Admixed American
ASH	Ashkenazi
COVID	Coronavirus diseaseDNA	Deoxyribonucleic acid
EAS	East Asian
EUR	[Non-Amish, non-Ashkenazi, non-Finnish] European
FIN	Finnishoth	Other [population(s)]SARS-CoV2	Severe acute respiratory syndrome coronavirus 2SAS	South AsianTMPRSS2	Transmembrane serine protease 2 [gene]Acknowledgements
We thank Arjun Biddanda, Charleston Chiang, Mark Iles, Ann Morgan, Richard
Sever, reviewers, and preprint commenters for helpful insights and suggestions; and Konrad Karczewski for guidance in accessing the gnomAD v3.0 data
used by Hou et al.
Authors’ contributions
NMP analyzed data, and conceived and drafted the manuscript; JN criticallyrefined analysis and manuscript with NMP. Both authors read and approved
the final manuscript.
Authors’ Twitter handles@genomenathan (NMP), @jnovembre (JN).
Funding
NIH NIGMS grant R35 GM149521 (JN).

Pearson and Novembre ﻿BMC Medicine(2024) 22:337

Availability of data and materials
Datasets analyzed in this study are available at:• https://​cloud.​google.​com/​life-​scien​ces/​docs/​resou​rces/​public-​datas​ets/​gnomad (gnomAD v3.0 data includes 1000Genomes data, as analyzed by Houet al.)• https://​esp.​gs.​washi​ngton.​edu/​drupal/​dbGaP_​Relea​ses• https://​www.​covid​19hg.​org/

Declarations
Ethics approval and consent to participate
N/A.
Consent for publication
N/A.
Competing interests
The authors declare that they have no competing interests.
Received: 5 December 2023 Accepted: 22 July 2024

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