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Genetic correlation analysis does not associate male pattern baldness with COVID-19

  • Hamzeh M. Tanha
    Correspondence
    Correspondence to: Hamzeh M. Tanha, MSc, School of Biomedical Sciences, Faculty of Health, and Centre for Genomics and Personalised Health, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove QLD 4059, Australia
    Affiliations
    School of Biomedical Sciences, Faculty of Health, and Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
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  • Sarah Medland
    Affiliations
    QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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  • Nicholas G. Martin
    Affiliations
    QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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  • Dale R. Nyholt
    Correspondence
    Dale R. Nyholt, PhD, School of Biomedical Sciences, Faculty of Health, and Centre for Genomics and Personalised Health, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove QLD 4059, Australia
    Affiliations
    School of Biomedical Sciences, Faculty of Health, and Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
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      To the Editor: A recent study has reported an association between androgenetic alopecia in men, commonly known as male pattern baldness (MPB), and severe symptoms of COVID-19.
      • Lee J.
      • Yousaf A.
      • Fang W.
      • Kolodney M.S.
      Male balding is a major risk factor for severe COVID-19.
      Here, we aimed to determine whether these epidemiological associations reflect shared genetic factors. Utilizing the genome-wide association study (GWAS) data, we estimated genetic correlations (rg) between different phenotypes of MPB and COVID-19.
      GWAS identifies genetic variants associated with differences in disease status among individuals. GWAS data for MPB were sourced from the study by Yap et al
      • Yap C.X.
      • Sidorenko J.
      • Wu Y.
      • et al.
      Dissection of genetic variation and evidence for pleiotropy in male pattern baldness.
      and http://www.nealelab.is/uk-biobank. The MPB GWAS enrolled male participants from the UK Biobank European population who were asked to choose a pattern from 4 options matching their baldness pattern: pattern 1 for no balding, pattern 2 for vertex balding, pattern 3 for crown balding, and pattern 4 for vertex plus crown balding.
      • Yap C.X.
      • Sidorenko J.
      • Wu Y.
      • et al.
      Dissection of genetic variation and evidence for pleiotropy in male pattern baldness.
       In total, 5 GWASs testing for associations between single-nucleotide polymorphism (SNP) genotypes and [a] adjusted MPB patterns, [b] MPB-2 versus MPB-1, [c] MPB-3 versus MPB-1, [d] MPB-4 versus MPB-1, and [e] MPB-2,3,4 versus MPB1 were included in the present study. Data sets [b], [c] and [d] were obtained using the case-case GWAS approach.
      • Peyrot W.J.
      • Price A.L.
      Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS.
      GWAS results for COVID-19 phenotypes were obtained from the “The COVID-19 Host Genetics Initiative” resource (https://www.covid19hg.org/results/; round 5, Jan 18, 2021). These GWAS results show that susceptibility for COVID-19 and its severe symptoms is associated with genetic factors. Three COVID-19 GWAS phenotypes (“COVID-19 positive versus population,” “Hospitalized COVID-19 versus population,” and “Very severe respiratory COVID-19 versus population”) were included. Table I provides a summary of the GWAS data sets used in this study.
      Table ISummary of genome-wide association study data analysed in this study and their estimated Ph2
      PhenotypeStudy populationh2 (SE)Ph2
      MPB GWASs
       MPB
      GWAS tested for association between “adjusted MPB pattern” and SNP genotypes using a linear mixed model.2
      205,3270.321 (0.026)9 × 10−36
       MPB-2 vs MPB-1
      GWASs tested for an association between a binary phenotype and SNP genotypes using a linear mixed model form http://www.nealelab.is/uk-biobank was utilized to obtain GWASs comparing MPB patterns with MPB-1 applying case-case GWAS.3
      38,044 vs 53,0760.128 (0.014)1 × 10−21
       MPB-3 vs MPB-1
      GWASs tested for an association between a binary phenotype and SNP genotypes using a linear mixed model form http://www.nealelab.is/uk-biobank was utilized to obtain GWASs comparing MPB patterns with MPB-1 applying case-case GWAS.3
      44,304 vs 53,0760.194 (0.023)3 × 10−17
       MPB-4 vs MPB-1
      GWASs tested for an association between a binary phenotype and SNP genotypes using a linear mixed model form http://www.nealelab.is/uk-biobank was utilized to obtain GWASs comparing MPB patterns with MPB-1 applying case-case GWAS.3
      30,225 vs 53,0760.459 (0.037)7 × 10−35
       MPB-2,3,4 vs MPB-1
      Directly resourced from http://www.nealelab.is/uk-biobank. Note that COVID-19 h2 estimates appear small because they are on the observed scale as it is not possible to determine an appropriate population prevalence (required to estimate h2 on the liability scale) for the COVID-19 binary phenotypes.
      112,573 vs 53,0760.186 (0.017)2 × 10−27
      COVID-19 GWASs
       COVID-19 positive vs population38,984 vs 1,644,7840.001 (3 × 10−4)7 × 10−6
       Hospitalized COVID-19 vs population8316 vs 1,549,0950.003 (6 × 10−4)3 × 10−7
       Very severe respiratory COVID-19 vs population5101 vs 1,383,2410.004 (7 × 10−4)6 × 10−8
      MPB, Male pattern baldness; GWAS, genome-wide association study; h2, observed scale heritability estimated by linkage disequilibrium score regression
      • Bulik-Sullivan B.
      • Finucane H.K.
      • Anttila V.
      • et al.
      An atlas of genetic correlations across human diseases and traits.
      ; Ph2, P value for the h2 estimate; SE, standard error of the h2 estimate; SNP, single-nucleotide polymorphism.
      GWAS tested for association between “adjusted MPB pattern” and SNP genotypes using a linear mixed model.
      • Yap C.X.
      • Sidorenko J.
      • Wu Y.
      • et al.
      Dissection of genetic variation and evidence for pleiotropy in male pattern baldness.
      GWASs tested for an association between a binary phenotype and SNP genotypes using a linear mixed model form http://www.nealelab.is/uk-biobank was utilized to obtain GWASs comparing MPB patterns with MPB-1 applying case-case GWAS.
      • Peyrot W.J.
      • Price A.L.
      Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS.
      Directly resourced from http://www.nealelab.is/uk-biobank. Note that COVID-19 h2 estimates appear small because they are on the observed scale as it is not possible to determine an appropriate population prevalence (required to estimate h2 on the liability scale) for the COVID-19 binary phenotypes.
      Linkage disequilibrium score regression analysis was conducted to estimate heritability (h2) and genome-wide genetic correlation (rg) between MPB and COVID-19 phenotypes for approximately 1,000,000 autosomal SNPs.
      • Bulik-Sullivan B.
      • Finucane H.K.
      • Anttila V.
      • et al.
      An atlas of genetic correlations across human diseases and traits.
      All MPB and COVID-19 GWASs had significant heritability (9 × 10−36 < Ph2 < 7 × 10−6) and were subsequently utilized in the rg analyses (Table I).
      We found no significant rg between the MPB and COVID-19 phenotypes (Table II). Furthermore, although not significant, the rg and previously reported associations are not directionally consistent.
      • Lee J.
      • Yousaf A.
      • Fang W.
      • Kolodney M.S.
      Male balding is a major risk factor for severe COVID-19.
      For example, the increased risk for MPB has a negative rg with susceptibility (rg = −0.078, P = .1048), hospitalization (rg = −0.019, P = .6485), and severity (rg = −0.026, P = .5846) of COVID-19.
      Table IILinkage disequilibrium score regression genetic correlation results between male pattern baldness and COVID-19 phenotypes
      MPB phenotypeCOVID-19 phenotyperg (SE)P
      MPBCOVID-19 positive vs population−0.078 (0.048).1048
      Hospitalized COVID-19 vs population−0.019 (0.042).6485
      Very severe respiratory COVID-19 vs population−0.026 (0.048).5846
      MPB-2 vs MPB-1COVID-19 positive vs population−0.031 (0.072).6644
      Hospitalized COVID-19 vs population0.058 (0.071).4146
      Very severe respiratory COVID-19 vs population0.030 (0.068).6641
      MPB-3 vs MPB-1COVID-19 positive vs population−0.120 (0.071).0929
      Hospitalized COVID-19 vs population−0.017 (0.057).7605
      Very severe respiratory COVID-19 vs population−0.004 (0.063).9552
      MPB-4 vs MPB-1COVID-19 positive vs population−0.076 (0.054).1589
      Hospitalized COVID-19 vs population0.035 (0.052).4973
      Very severe respiratory COVID-19 vs population0.006 (0.052).9051
      MPB-2,3,4 vs MPB-1COVID-19 positive vs population−0.091 (0.057).1087
      Hospitalized COVID-19 vs population0.016 (0.051).7520
      Very severe respiratory COVID-19 vs population0.004 (0.054).9374
      MPB, Male pattern baldness; P, P value for the rg estimate; rg, genetic correlation estimated by LDSC
      • Bulik-Sullivan B.
      • Finucane H.K.
      • Anttila V.
      • et al.
      An atlas of genetic correlations across human diseases and traits.
      ; SE, standard error of the rg estimate.
      Two possible limitations of this study need to be mentioned. First, the baldness pattern from UK Biobank is self-reported. Second, the linkage disequilibrium score regression rg was estimated utilizing autosomal SNPs and therefore did not evaluate X-linked genetic factors, including the androgen receptor gene, which has been implicated in both MPB and severe COVID-19.
      • McCoy J.
      • Wambier C.G.
      • Herrera S.
      • et al.
      Androgen receptor genetic variant predicts COVID-19 disease severity: a prospective longitudinal study of hospitalized COVID-19 male patients.
      However, the analysis of independent SNPs around androgen receptor gene found no correlation (P > .05) in risk effects between the MPB and COVID-19 GWAS phenotypes—consistent with the linkage disequilibrium score regression autosomal rg results.
      Although we found no evidence for a global genetic correlation across MPB and COVID-19 phenotypes, given pleiotropic effects, where genetic variants influence multiple traits, are widespread in human complex traits (https://www.ebi.ac.uk/gwas/), it is possible that other/specific genes, including genes on chromosome X, could contribute to MPB and COVID-19 risk—noting many pleiotropic variants with consistent effect directions are required to produce a significant genetic correlation.

      Conflicts of interest

      None disclosed.

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