The genetic basis of dermatophytosis skin infection susceptibility

Domain: chemistry
Published: 2026-03-06
Editorial outcome: minor_revision

Abstract
Dermatophytosis is a fungal infection affecting keratinizePd tissues such as skin, nails, and hair, presenting
as red and itchy patches, nail thickening, or hair loss. It affects around 20% of the global population but
the genetic architecture remains poorly understood. We performed a genome-wide association meta-
analysis of over 250,000 cases and 1.37 million controls from FinnGen, Estonian Biobank, UK Biobank, and
the Million Veteran Program and identified 30 genome-wide significant loci, including seven missense
variants and two loci in high linkage disequilibrium with missense variants. Top associations were near
ZNF646, HLA-DQB1, FLG, FTO, SLURP2, and KRT77. Additionally, dermatophytosis subtype analyses
revealed 44 signals. Our results highlight the role of disrupted keratin biology, skin barrier defects,
immune dysfunction, and oRbesity in dermatophytosis risk. We also observed genetic overlap with other
skin conditions and obesity-related traits, providing insights into disease mechanisms and potential
targets for prevention and treatment.

Introduction
Dermatophytosis, commonly known as ringworm, is a prevalent fungal infection affecting the skin, hair,
and nails. It is caused by dermatophytes, a group of keratinophilic fungi with the unique ability to utilize
keratin, a structural protein in the outer layer of human skin, as a nutrient source, leading to various
clinical symptoms 1,2. While typically limited to the outermost layers of the skin, dermatophytosis can
become more severe in certain patient groups. For example in immunocompromised and diabetic patients
with compromised immune system or skin barrier, the infection may invade deeper layers of the skin and
can lead to severe and invasive disease 3.

The global incidence of dermatophytosis has made it a significant public health concern, particularly in
regions with warm and humid climates that favor fungal growth. It has been estimated that around 20-25
% of people are infected with dermatophytes at some point in their lives and the incidence rate is
constantly rising 4. The prevalence varies between continents and countries but more recent studies from
European countries have estimated prevalence rates around 12-17 % 5,6. Dermatophytosis skin infections
can develop to people of all ages. Infections of the scalp (tinea capitis), are more common in children,
while other tineas more commonly affect post pubertal individuals with a peak at mid-age 7,8. Besides
geographic location and age, the individual vulnerability to dermatophytosis depends on several factors
including sex, season, socioeconomic status, personal hygiene and cultural conditions 7,8. In addition,
existing skin diseases or skin lesions together with immunocompromising factors can affect the
susceptibility to dermatophytosis.

Dermatophytosis presents a wide range of symptoms, depending on the site of infection. On the skin (e.g.
tinea corporis, tinea pedis), it typically manifests as red, scaly, and itchy patches that often form a ring-
like pattern, hence the name "ringworm". Infections of the scalp (tinea capitis) can lead to hair loss and
inflammation, while nail infections (onychomycosis) cause thickening, discoloration, and brittleness of the
nails 9. The infection is highly contagious and can spread through direct contact with infected individuals
or animals, as well as indirectly through contaminated objects like clothing, towels, and grooming tools
10.

While environmental factors associate with dermatophytosis infections, genetic studies provide an
avenue to understand biological mechanisms th at contribute to risk and development of
dermatophytosis. Here we aimed to understand host factors that affect susceptibility to dermatophytosis
infections by performing the largest genoIme-wide association analysis with over 250,000
dermatophytosis cases from FinnGen, the UEK Biobank (UKB), the Estonian biobank (EstBB) and the Million
Veteran Program (MVP). Our findings higLhlight the role of barrier organs and variety of immune functions
in the development of dermatophytoCsis.

Results
GWAS shows an association between dermatophytosis and 30 genetic loci
To explore the host genetic components contributing to dermatophytosis, we performed GWAS and
meta-analysis in FinnGen (N = 27,662 cases and 471,729 controls), UKB (N = 27,755 cases and 380,368
controls), EstBB (N = 50,241 cases and 106,586 controls) and Million Veterans Program (N = 151,164 cases
and 413,818 controls).

With data from 256,822 dermatophytosis cases and 1,372,501 controls, we identified 30 genome-wide
significant loci (p < 5x10-8) associated with dermatophytosis infection (Figure 1, Table 1, Supplementary
Data Table S1). The most significant loci were ZNF646 (p = 6.60x10-79), HLA (p = 1.42x10-36), FLG (p =
1.96x10-27), SLURP2 (p = 3.33x10-24) and KRT77 (p = 1.28x10-15).

Dermatophytosis can be classified into subtypes based on the region of the body where the infection is
manifesting. To understand the subtype specific associations, we additionally ran GWAS on each of these
subtypes in FinnGen, EstBB, UKB, along with the publicly available genome-wide association data for
subtypes in the MVP cohort (Figure 2). The association statistics for each of our lead variants in these
subtypes are reported in Supplementary Data S9 (case and control numbers are presented in S8). We
show association of several lead variants from the main dermatophytosis analysis at the subtype level (p
< 0.0002, Bonferroni corrected threshold) especially in subtypes B35.1 (nail ringworm), B35.3 (athlete’s
foot) and B35.4 (dermatophytosis of the body). When studying the lead variants at the subtype level, we
see a consistent pleiotropic association with a subset of variants across several different subtypes,
including; FLG, KTR77, ZNF646 and FTO at genome-wide significant level (p < 5x10-8) highlighting the role
of these genes in the defense against dermatophytosis infections. In addition, we report associations in
another zinc finger gene ZNF668 (B35.1 and B35.3) located in proximity of the ZNF646 gene. Furthermore,
we report 34 previously unreported associations, such as rs112088479 closest to NOTCH2 gene that is
linked to skin homeostasis and epidermal differentiation 11,12. We present Manhattan plots for each
subtype in Supplementary Figures and the lead variants in Supplementary Data S10.

The majority of the genetic associations of compIlex diseases are regulatory variants typically located at
the non-coding or intronic regions of the gEenome and usually, these associations typically affect gene
expression levels rather than have directL impact on protein structure 13. In our meta-analysis nine out of
thirty variants were either missense vCariants or in high LD (linkage disequilibrium) with a missense variant
(http://www.mulinlab.org/vportal/Iindex.html). (Table 2)

The missense variants with the most significant association with dermatophytosis infection were
identified within the ZNF646 gene (rs7196726, beta = 0.066, p = 6.60x10-79) and its neighboring genes
PRSS53 (rs35713203, beta = 0.065, p = 1.90x10-78) and HSD3B7 (rs9938550, beta = 0.056, p = 3.43x10-57).
In addition, we identified highly significant missense variants (< 5x10-15) within the FLG gene
(rs558269137, beta = -0.222, p = 1.96x10-27) and the KRT1 gene (rs14024, beta = -0.030, p = 3.95x10-15).
All of the reported missense variants have minor allele frequency above 1 % and all of them are predicted
to be benign based on their Polyphen score (<0.15), estimating impact of an amino acid substitution on
the structure and function of a human protein 14.

While missense variation can indicate a likely causal gene at the locus, the majority of the associated
variants were located in non-coding or intronic regions and likely contribute to disease risk by affecting
gene expression. To elucidate possible affected genes near the regions of the strongest non-missense
associations (rs10094888 closest to SLURP2 and rs1794269 closest to FTO), we performed a colocalization
analysis with expression data from GTEx 15 (https://gtexportal.org/home/) (Supplementary Data Table
S2-S3). We identified a strong shared signal between SLURP2 (rs10094888) and a structurally and
functionally highly similar Ly6SF-group gene LYNX1 expression in skin tissue and in dermatophytosis with
posterior probabilities of 0.999 (LYNX1) and 0.987 (SLURP2). The results indicate the same causal variant
for differential expression of SLURP2 and LYNX1 in dermatophytosis and skin tissue (Figure 3A-B). These
proteins are secreted primarily in the skin by keratinocytes and have been previously implicated in skin
diseases 16.

We also discovered associations in canonical loci that have been earlier reported in infections and obesity
including the FTO locus. The lead variant at the FTO locus was the same variant that has been implicated
as a causal variant in higher body mass index (rs1421085), and formal colocalization analysis showed a
signal with IRX3, with a posterior probability of 0.86 (Figure 3C) aligning with the signal reported earlier
for obesity 17. Although obesity-associated variants are located within the FTO gene, they regulate the
expression of the distant IRX3 gene through long-range chromatin interactions explaining the
colocalization signal at this gene region 17. These findings may implicate the role of BMI (body mass index)
in dermatophytosis as suggested in previous studies 18.

In addition to showing the likely association of variants froRm SLURP2 and FTO to dermatophytosis and
aforementioned missense variants, our GWAS shows Pan association between the HLA region and
dermatophytosis infections, aligning with previous st udies 19,20. The lead variant for the association
(rs1794269, beta = 0.046 and p = 1.42x10-36) was located closest to the HLA-DQB1 gene but a correlation
analysis suggests a strongest correlation with DQB1*05:01 with r = 0.48. Formal HLA fine mapping in
FinnGen supports the association of DQB1*05:01 with dermatophytosis with p-value of 0.005. The
association between HLA region and dermatophytosis highlights an overall immune signal in
dermatophytosis.

GWAS associations highlight the role of compromised keratin processing behind dermatophytosis
Dermatophytes are fungi with the unique ability to utilize keratin as a source of energy, allowing them to
invade and colonize the outer layers of skin, hair, and nails, causing superficial infections 21. We identified
several lead variants that were located within or near genes involved in the keratin lifecycle, including the
expression, differentiation, migration, and apoptosis of keratin-producing cells (keratinocytes). The
strongest associations were identified in Profilaggrin (FLG) that forms the outermost layer of the skin,
keratin 1 (KRT1), and SLURP2, all of which play critical roles in keratin function. These findings highlight
keratin's crucial role in the development and progression of dermatophytosis. Many keratin-related
proteins are essential for maintaining skin integrity and barrier function, which are key to protecting
against fungal infections 22.

Moreover, we observed our strongest association at ZNF646. ZNF646 belongs to a family of zinc finger
proteins that act in transcriptional regulation and protein degradation 23. This protein family is implicated
in tissue development, particularly in the skin where several family members can modulate keratinocyte
gene expression and differentiation 23. Zinc finger proteins have been earlier implicated in the regulation
of FLG 23,24. This observation raises a possibility that ZNF646 modulates FLG expression. To test this, we
examined the effect of the lead missense variant, rs7196726, at ZNF646 on FLG and KRT1 expression in
the skin using GTEx eQLT calculator (https://gtexportal.org/home/testyourown). We observed that
ZNF646 was a trans-eQTL for FLG expression in the sun-exposed skin (NES= -0.063 and p = 0.0039) as well
as in the not sun-exposed skin (NES= -0.058 and p = 0.031) suggesting an effector role of ZNF646 on FLG.
Another significant group of genes associated with dermatophytosis involves those related to immune
defense, such as HLA genes, IL13, and SH2B3 (LNK). These genes play crucial roles in antigen presentation,
immune signaling, and cytokine production, all of which are essential for the body’s defense against
pathogens 25-27.

A third group of genes identified is related to obesity. Among these, the most notable was the association
with FTO and IRX3, with IRX3 mediating the functional effects of FTO 17. The structure of the skin, the
roles of each layer, and the potential causal genes for dermatophytosis—grouped by their function—are
presented in Figure 4.

Variant annotations support associations with skin we ll-being, general immune defense and BMI
To get a broader understanding of lead variants in dermatophytosis GWAS, we assessed their associations
with other diseases and traits using FinnGen’s annotation tool (https://anno.finngen.fi/,
https://github.com/juhis/genetics-results-browser) covering all endpoints in FinnGen data freeze 12 (R12)
(https://risteys.finregistry.fi/), FinnGen R12 and UKB combined meta-analysis and Open targets
(https://platform.opentargets.org/). The strongest genome-wide significant (p < 5x10-8) signal for each
variant is shown in Table 3. Besides the strongest associations presented in the table, most of the variants
are associated with multiple or up to few hundred other traits at a genome-wide significant level.
Several lead variants are most strongly associated with circulating immune cells and autoimmune
diseases, highlighting the connection of dermatophytosis to suboptimal internal immune defense.
Secondly, we see associations with vitamin D levels, a vitamin that is essential for skin well-being and is
known to regulate, for example, epidermal keratinocytes 28. A few of the most significant genetic
associations are also directly linked with skin diseases such as atopic dermatitis or skin cancer. Lastly, we
see a group of associations with traits related to high body weight.

Stratified LDSC suggests association to skin and immune tissues
Next, we performed a tissue-stratified linkage disequilibrium score regression (s-LDSC) analysis to identify
which tissues are the most relevant for dermatophytosis infection (Figure 5, Supplementary Data Table
S5). We show top associations with connective tissue (p = 4.32x10-10) and immune cells (p = 1.96x10-8). As
skin tissue is included in the connective tissue, the top associations strengthen our other results regarding
the relevance of skin integrity and well-being and the internal immune defense defending against
dermatophytosis fungal infections.

To further study the specific related tissue types we performed s-LDSC multi-tissue analysis with 80
immune cell type and chromatin marker combinations and 39 skin cell and chromatin marker
combinations 29. We identified strongest association in the immune subset with T-helper cells (Primary T
helper 17 cells PMA-I stimulated, H3K4me1, p = 0.0005) and in the skin subset with fibroblasts (Foreskin
Fibroblast Primary Cells skin02, H3K27ac, p = 0.005) and keratinocytes (Foreskin Keratinocyte Primary
Cells skin03, H3K4me3, p = 0.024). Only the association with PMA-I stimulated primary T helper cells
(H3K4me1) passes the Bonferroni corrected p-value threshold (threshoSld p-value 0.0006 for immune cells
and 0.001 for skin cells) (Supplementary Data Table S5).

Dermatophytosis shows genetic correlation with other diseases of skin and high BMI
We employed LDSC to study the genetic correlation of dermatophytosis with other infections of the skin
and subcutaneous tissue, certain infectious and pIarasitic diseases and obesity. By using our meta-analysis
summary statistics and all relevant endpoinEts in FinnGen we found associations with several skin disease
endpoints (Figure 6, Supplementary DatLa Table S7), many of which include itchiness, rash or redness of
the skin. The most significant associatCions were with erysipelas (ICD10: A46, p = 3.5x10-20), other disorders
of skin and subcutaneous tissue (ICID10: L98, p = 7.4x10-16) and infections of skin and subcutaneous tissue
(ICD10: L00-L08, p = 1.5x10-10). Moreover, we observed shared genetic architecture between Scabies,
Herpes simplex infection and other Bacterial diseases endpoints (P < 0.0001). The finding highlights the
similar etiology behind skin-related diseases and proposes that the same genetic variants may increase
our susceptibility to several types of skin diseases.

In addition, we studied the genetic correlation between dermatophytosis and BMI due to the association
of several lead variants with changes in BMI and found a strong correlation with obesity (p = 4.4x10-49)
and BMI IRN (inverse rank normalized) (p = 3.2x10-35).

Methods
Cohorts
FinnGen is a population-based public-private population cohort established in 2017 39. The study
combines genetic data with electronic health record data, including International Classification of Diseases
(ICD) codes spanning an individual's entire lifespan, derived from primary care registers, hospital inpatient
and outpatient visits and drug prescriptions of 520,000 participants. The project aims to improve
understanding of the genetic etiology of diseases and disorders potentially leading to drug development.
The UK biobank (UKB) is a prospective open-access study containing over 500,000 individuals aged 40-69
years upon entry to the study between 2006-1010 40. At the time of the entry to the cohort, a variety of
health and lifestyle measures were collected, and blood and urine samples were taken for genetic and
biochemistry analysis. Hospital in-patient (HES; N~470,000) and primary care (GP; N~231,000) records
were later linked up to provide longitudinal data on disease diagnosis, operations, deaths, medications
and deaths. In this study we used publicly available summary statistics from pan-UKB study 41 for main
dermatophytosis and self-analyzed summary statistics for subtypes.
The Estonian biobank (EstBB) is a population-based biobank with 212,955 participants 42. Information on
ICD-10 codes is obtained through regular linking with the National Health Insurance Fund and other
relevant databases. The majority of the electronic health records have been collected since 2004.
The Million Veteran Project (MVP) is a longitudinal cohort study of diverse U.S. Veterans looking at how
genes, lifestyle, military experiences, and exposures affect health and wellness 43. It combines genetic
data with electronic health records of 635,969 participants (data freeSze 4) across four ethnic groups. In
this study we extracted data from the publicly released summary sStatistics for MVP for dermatophytosis
for all ancestries (AFR, AMR, EAS, and EUR).

Ethics statements
Study subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank
Act. Alternatively, separate research cohortEs, collected prior the Finnish Biobank Act came into effect (in
September 2013) and start of FinnGen (LAugust 2017), were collected based on study-specific consents
and later transferred to the Finnish Cbiobanks after approval by Fimea (Finnish Medicines Agency), the
National Supervisory Authority foIr Welfare and Health. Recruitment protocols followed the biobank
protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and
Uusimaa (HUS) statement number for the FinnGen study is Nr HUS/990/2017.
The FinnGen study is approved by Finnish Institute for Health and Welfare (permit numbers:
THL/2031/6.02.00/2017, THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018,
THL/283/6.02.00/2019, THL/1721/5.05.00/2019 and THL/1524/5.05.00/2020), Digital and population
data service agency (permit numbers: VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3), the
Social Insurance Institution (permit numbers: KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019,
KELA 98/522/2019, KELA 134/522/2019, KELA 138/522/2019, KELA 2/522/2020, KELA 16/522/2020),
Findata permit numbers THL/2364/14.02/2020, THL/4055/14.06.00/2020, THL/3433/14.06.00/2020,
THL/4432/14.06/2020, THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020,
THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021, THL/1965/14.06.00/2021,
THL/5546/14.02.00/2020, THL/2658/14.06.00/2021, THL/4235/14.06.00/2021, Statistics Finland (permit
numbers: TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20) TK/1735/07.03.00/2021,
TK/3112/07.03.00/2021) and Finnish Registry for Kidney Diseases permission/extract from the meeting
minutes on 4th July 2019.
The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 11 include:
THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7,
BB2019_8, BB2019_26, BB2020_1, BB2021_65, Finnish Red Cross Blood Service Biobank 7.12.2017,
Helsinki Biobank HUS/359/2017, HUS/248/2020, HUS/430/2021 §28, §29, HUS/150/2022 §12, §13, §14,
§15, §16, §17, §18, §23, §58, §59, HUS/128/2023 §18, Auria Biobank AB17-5154 and amendment #1
(August 17 2020) and amendments BB_2021-0140, BB_2021-0156 (August 26 2021, Feb 2 2022),
BB_2021-0169, BB_2021-0179, BB_2021-0161, AB20-5926 and amendment #1 (April 23 2020) and it´s
modifications (Sep 22 2021), BB_2022-0262, BB_2022-0256, Biobank Borealis of Northern
Finland_2017_1013, 2021_5010, 2021_5010 Amendment, 2021_5018, 2021_5018 Amendment,
2021_5015, 2021_5015 Amendment, 2021_5015 Amendment_2, 2021_5023, 2021_5023 Amendment,
2021_5023 Amendment_2, 2021_5017, 2021_5017 Amendment, 2022_6001, 2022_6001 Amendment,
2022_6006 Amendment, 2022_6006 Amendment, 2022_6006 Amendment_2, BB22-0067, 2022_0262,
2022_0262 Amendment, Biobank of Eastern Finland 1186/2018 and amendment 22§/2020, 53§/2021,
13§/2022, 14§/2022, 15§/2022, 27§/2022, 28§/2022, 29§/2022, 33§/2022, 35§/2022, 36§/2022,
37§/2022, 39§/2022, 7§/2023, 32§/2023, 33§/2023, 34§/2023, 35§/2023, 36§/2023, 37§/2023,
38§/2023, 39§/2023, 40§/2023, 41§/2023, Finnish Clinical Biobank Tampere MH0004 and amendments
(21.02.2020 & 06.10.2020), BB2021-0140 8§/2021, 9§/2021, §9/2022, §10/2022, §12/2022, 13§/2022,
§20/2022, §21/2022, §22/2022, §23/2022, 28§/2022, 29§/2022, 30§/2022, 31§/2022, 32§/2022,
38§/2022, 40§/2022, 42§/2022, 1§/2023, Central Finland Biobank 1-2017, BB_2021-0161, BB_2021-0169,
BB_2021-0179, BB_2021-0170, BB_2022-0256, BB_N2022-0262, BB22-0067, Decision allowing to continue
data processing until 31st Aug 2024 for projectIs: BB_2021-0179, BB22-0067,BB_2022-0262, BB_2021-
0170, BB_2021-0164, BB_2021-0161, and EBB_2021-0169, and Terveystalo Biobank STB 2018001 and
amendment 25th Aug 2020, Finnish HemLatological Registry and Clinical Biobank decision 18th June 2021,
Arctic biobank P0844: ARC_2021_100C1.
The activities of the EstBB are rTegulated by the Human Genes Research Act, which was adopted in 2000
specifically for the operationRs of the EstBB. Individual level data analysis in the EstBB was carried out under
ethical approval 1.1-12/A624 from the Estonian Committee on Bioethics and Human Research (Estonian
Ministry of Social Affairs), using data according to release application 6-7/GI/33543 from the Estonian
Biobank.
The North West Multi-centre Research Ethics Committee (MREC) has granted the Research Tissue Bank
(RTB) approval for the UKB that covers the collection and distribution of data and samples
(http://www.ukbiobank.ac.uk/ethics/). Our work was performed under the UKB application numbers
22627 (main meta-analysis) and 6818 (subtype analysis). All participants included in the conducted
analyses have given a written consent to participate.
We would like to thank the participants and researchers from the UK Biobank who contributed or
collected data.
No compensation was given to the participants.

Genotyping and quality control
FinnGen R12 contains genetic data for 520 210 individuals. The samples were genotyped using Illumina
(Illumina) and Affymetrix arrays (Thermo Fisher Scientific). The array consisted of 735,145 probes looking
for 655,973 variants consisting of core backbone variants for imputation, rare coding variants enriched in
the Finnish population, variants for KIR and HLA haplotypes, disease-specific markers and
pharmacogenomic markers. Genotyping data produced with previous chip platforms and reference
genome builds were lifted over to build v.38 (GRCh38/hg38). For sample-wise quality control individuals
exhibiting a discrepancy between genetically inferred sex and reported sex in registries, high genotype
missingness (>5%), and excess heterozygosity (±4 standard deviations) were excluded. For variant-level
QC, variants with high missingness (>2%), low Hardy–Weinberg equilibrium (P < 1 × 10–6), and a minor
allele count < 3 were filtered out. Chip-genotyped samples were pre-phased with Eagle 2.3.5 and imputed
with the Finnish-specific SISu v4 imputation reference panel. Post-imputation quality control involved
excluding variants with INFO score < 0.7 39.
All EstBB participants have been genotyped at the Core Genotyping Lab of the Institute of Genomics,
University of Tartu, using Illumina Global Screening Array v3.0_EST. Samples were genotyped and PLINK
format files were created using Illumina GenomeStudio v2.0.4. Individuals were excluded from the
analysis if their call rate was < 95%, if they were outlie rs of the absolute value of heterozygosity (> 3SD
from the mean) or if sex defined based on heterozygosity of X chromosome did not match sex in
phenotype data 44. Before imputation, variants I were filtered by call rate < 95%, HWE p-value < 1x10-4
(autosomal variants only), and minor allele Efrequency < 1%. Genotyped variant positions were in build 37
and were lifted over to build 38 using PiLcard. Phasing was performed using the Beagle v5.4 software 45.
Imputation was performed with Beagle v5.4 software (beagle.22Jul22.46e.jar) and default settings. The
dataset was split into batches of 5,000. A population-specific reference panel consisting of 2,695 WGS
samples was utilized for imputation and standard Beagle hg38 recombination maps were used. Based on
the principal component analysis, samples which were not from European ancestry individuals were
removed. Duplicate and monozygous twin detection was performed with KING 2.2.7 46, and one sample
was removed from the pair of duplicates.
A total of 488,477 participants in the UK Biobank (UKB) were genotyped, including 49,950 individuals using
the Affymetrix (Thermo Fisher Scientific) UK BiLEVE Axiom Array and 438,427 using the closely related
Affymetrix UK Biobank Axiom Array. These platforms captured up to 825,927 SNPs and short indels,
prioritizing known coding variants, previously disease-associated variants, and ancestry-informative
markers to ensure robust imputation performance. DNA was extracted from blood samples collected
during baseline assessments conducted between 2006 and 2010. Genotyping was performed in 106
sequential batches, resulting in genotype calls for 812,428 unique variants across 489,212 participants.
For genome-wide association study (GWAS) analyses, genotype data were filtered using PLINK2 (v2.0.0,
24 Nov 2024) to remove variants with call rates below 99%, minor allele frequencies under 1%, Hardy-
Weinberg equilibrium exact test p-values less than 1e-15, and variants with linkage disequilibrium (r² >
0.9) within 100kb windows. Genotype and sequencing data were imputed using the TOPMed R2 reference
panel by the TOPMed Informatics Research Center.

GWAS
Main dermatophytosis (B35) GWAS in FinnGen was conducted using the REGENIE v3.3 pipeline
(https://github.com/FINNGEN/regenie-pipelines) adjusting for age, sex, chip, batch and ten first principal
components 47.
Association analysis in the EstBB was carried out for all variants with an INFO score > 0.4 using the additive
model as implemented in REGENIE v3.0.3 with standard binary trait settings 47. Logistic regression was
carried out with adjustment for current age, age², sex and 10 PCs as covariates, analyzing only variants
with a minimum minor allele count of 2.
UKB summary statistics were readily available from pan-UKB study (only European subset) 41 and MVP
summary statistics from their web server
(https://phenomics.va.ornl.gov/pheweb/gia/meta/pheno/Phe_110).
In all the cohorts, sex has been genetically determined. All studies have been adjusted with sex. The case
and control definitions, numbers of cases and controls, used GWAS software and used covariates in each
GWAS analysis are presented in Table 4.
In addition to main dermatophytosIis GWAS analyses, we performed subtype specific GWAS analyses in
FinnGen, EstBB and UKB using REGENIE. The analyses were adjusted with ten first principal components,
age, sex and genotyping array. In each subtype specific analysis all dermatophytosis cases were excluded
from the controls. MVP summary statistics for three of the phenotypes were obtained from their web
server (https://phenomics.va.ornl.gov/pheweb/gia/meta/). Case and control numbers can be found
from Supplementary Data S8.

Meta-analysis
We conducted a meta-analysis with summary statistics from FinnGen, EstBB, UKB and MVP for main
dermatophytosis and each subtype using the standard error method in METAL software 48. The variants
are annotated according to genome build 38.
The Manhattan plots for meta-analyses were plotted using R version 4.3.1 (packages: qqman and
RColorBrewer).

HLA fine mapping
Association testing for HLA variants in the FinnGen cohort was conducted using multivariate logistic
regression to elucidate the relationships between individual HLA alleles and dermatophytosis infection.
Multivariate logistic regression analysis was adjusted for age at death or the end of follow-up, sex, and
the first 10 genetic principal components accounting for population structure. Multivariate logistic
regression was performed in a stepwise manner by sequentially adding the most strongly associated HLA
allele as a covariate to the analysis. This iterative process was repeated until no significant (p<0.05) alleles
remained. The multivariate logistic regression analyses were conducted using R 4.0.1.
HLA imputation in FinnGen was performed for HLA A, HLA B, HLA C, HLA DRB1, HLA DQA1, HLA DQB1, HLA
DPB1, HLA DRB3, HLA DRB4 and HLA DRB5 using R library HIBAG (HLA genotype imputation with attribute
bagging), as described by Ritari et al. 49. The HLA imputation of FinnGen data is based on a set of SNPs
directly genotyped on the FinnGen array. A Finnish reference panel, genotyped at clinical grade accuracy
(4-digits, amino acid), was used.

Colocalization analysis
To assess the shared association of our lead variants to dermatophytosis and tissue-specific eQTLs, we
performed colocalization analysis. For the analyNsis we used meta-analysis summary statistics from
dermatophytosis from a region +-50,000 baseI pairs around our lead variants and imported eQTL
association statistics from GTEx 15 (https://gEtexportal.org/home/) for the same region for all tissues.
Colocalization was performed using tChe R package Coloc (v5.1.0.1 in R v4.2.2) 50 and co-localization plots
were generated with LocusCompaIreR R package (v1.0.0) 51 using LD r2 from 1000 Genomes 52 European-
ancestry samples.

Tissue and cell type-specific analyses
To study relevant tissue and cell types for dermatophytosis infection, we employed stratified LDSC
method 29. First, we assessed relevant tissue types using cell-type groups data as used in Finucane et al.
consisting of two gene expression datasets, GTEx project and ‘Franke lab’ dataset with 205 tissues and
cell types, that were classified into nine categories (Connective/Bone, Immune, Other, CNS, Skeletal
Muscle, Liver, Adrenal/Pancreas, Kidney, GI, Cardiovascular) 53-55. Second, we studied relevant cell types
using Multi_tissue_chromatin_1000Gv3_ldscores dataset from Finucane et al. (2018) composed of
chromatin data from Roadmap Epigenomics and ENCODE projects. This dataset contains 489 tissue-
specific chromatin-based annotations from peaks for six epigenetic marks (H3K27ac, H3K4me1, H3K4me3,
H3K9ac, H3K36me3 and DHS) 56,57. We used only annotations related to skin and immune cells, resulting
in 80 immune cell type and chromatin marker combinations and 39 skin cell and chromatin marker
combinations.

Genetic correlation
We performed genetic correlation between dermatophytosis infection and FinnGen endpoints of other
infections of skin and subcutaneous tissue, certain infectious and parasitic diseases and obesity using the
LD score regression method 58. HapMap 3 SNP list and European LD score files, which are provided with
the software, were used in our LD Score regression analyses. For dermatophytosis, we used summary
statistics from our meta-analysis, and for FinnGen we used all endpoints in categories AB1 and L12 as well
as selected BMI related endpoints (BMI_IRN and E4_OBESITYNAS). Further information on these FinnGen
endpoints can be found at https://risteys.finregistry.fi/. When assessing the relevance of the genetic
correlation, we adjusted p-value with Bonferroni correction. The forest plot for genetic correlation was
generated using the ggplot2 package in R (version 4.4.1).

Data availability
The summary statistics data of dermatophytosis meta-analysis generated in this study have been
deposited in the GWAS catalog (https://www.ebi.ac.uk/gwas/) under accession codes GCST90624029-
GCST90624037. The individual level data from the biobanks is limited to authorized researchers due to
privacy laws. The summary level data created in this stu dy is provided in the Supplementary Data file.

Code availability
We have used REGENIE from https://githLub.com/FINNGEN, Metal package for Meta-analysis of the
summary statistics https://github.comC/statgen/METAL, COLOC package for colocalization analysis
https://github.com/chr1swallace/cIoloc and LDSC pipeline for running genetic correlation and
partitioned heritability (tissue type analysis) https://github.com/bulik/ldsc. Plotting codes can be shared
upon request from the corresponding author.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:The authors describe a genome-wide association study of dermatophytosis (ringworm) infection in 4 large national biobanks. Through meta-analysis, they identify 30 genome-wide significant loci including missense variants, variants in LD with missense variants and variants driving gene expression and overlapping with related conditions. Assessing the function of the genes underlying the associated loci, the authors suggest a role for altered keratin processing in dermatophytosis and, using LD score regression, the authors further show implication of connective tissue and immune cells as the primary tissues implicated in dermatophytosis. Finally, the authors demonstrate genetic overlap between dermatophytosis and several skin and immune traits as well as obesity. Overall this is a well-constructed study that improve our understanding of the genetic architecture of dermatophytosis, I have only minor comments/suggestions:\n1. The analysis of missense variants as presented suggests these are causal variants at the associated loci. While this may be the case, their polyphen scores suggest they are benign and it is equally likely that there may be regulatory variants in LD with the GWAS lead SNPs that are causal. I recommend editing the text to reflect that more work needs to be done to assess the functionality of the missense variants\n2. The overlap between FTO and IRX3 could be better described. I’m no an obesity researcher and am unfamiliar with the function of IRX3. Some additional description of the potential biology at this locus is warranted.\n3. The section running from lines 180 to 213 comes off more as discussion points as there are very few results reported. The overall presentation of the manuscript would be improved by moving most of this to discussion and including the eQTL results in the co-localization section (presuming that a formal colocalization analysis can be done between ZNF646 and FLG).\n4. Table 1 should report the effect allele. Also, given that this is a binary phenotype, I suggest reporting the effect size as Odds Ration and 95% CI rather than beta and SE but this is personal preference.\n5. Similarly in table two, when reporting association statistics the effect allele and variance around the point estimate of the effect should be reported. For this particular table, the association statistics could be moved to the supplementary materials to simplify the presentation.", "reviewer_2": "This study provides valuable insights into dermatophytosis and its susceptibility to skin infections, employing a genetic approach through GWAS data, which includes cohorts from the UK Biobank, the Estonian Biobank, the FinnGen project, and the Million Veterans Program. A total of 250,000 cases and 1.37 million controls were included in the meta-analysis. The researchers identified 30 genome-wide significant loci, including 9 missense variants or variants in high linkage disequilibrium (LD) with missense variants, in genes related to keratin lifecycle, skin integrity, immune defense, and obesity. Overall, the manuscript is very interesting, the methods seem sound. However, I noticed the absence of a citation and discussion of a recent 2022 study that addresses the same topic; the researchers had also utilized the UK Biobank cohort, but identified a different gene locus. Given that - in your study - a meta-analysis was conducted, I expect that data from each cohort, including the UK Biobank, are taken separately. The presence of such substantial differences in the gene loci identified from the same cohort raises important questions, yet these have not been discussed. Even if you might have reasons for not having included the study, the study cannot be completely neglected. Differences and potential overlaps with the 2022 study should be addressed and discussed.\nAdditionally, there are several points that should be clarified:\n1. Introduction: Is there any specific age peak for skin infections, how about children and tinea?\n2. Are there any other genetic studies available beyond the 2022 research?\n3. Are there any heritability estimates known?\n4. Results: Line 94 mentions an HLA locus, but it is not specified. Please clarify.\n5. Given the wide variety of tinea types (e.g., skin, hair, nail infections), and considering the large cohort with comprehensive clinical data, wouldn’t it be valuable to stratify the analysis based on different subtypes of tinea.\n6. Discussion: Many of the identified loci are also linked to other autoimmune disorders, such as rheumatoid arthritis, alopecia areata, and atopic conditions. These are summarized under inflammation, but this might not be immediately clear to readers unfamiliar with the genetic associations of these disorders."}, "author_response": "Question 1.1 The analysis of missense variants as presented suggests these are causal variants at the associated loci. While this may be the case, their polyphen scores suggest they are benign and it is equally likely that there may be regulatory variants in LD with the GWAS lead SNPs that are causal. I recommend editing the text to reflect that more work needs to be done to assess the functionality of the missense variants.\nResponse 1.1 Thank you for the suggestion. We have edited the text to highlight that the lead variant can also be a regulatory variant in LD with a causal variant. We also have added that more work is needed to assess the functionality of the missense variants. This text is incorporated into the discussion as follows:\n“It should be also noted that the association with a missense variant does not necessarily mean that they would be the causal variant and instead could indicate LD with a regulatory variant. Therefore, the role of the missense variants should be addressed in future functional studies.”\nQuestion 1.2 The overlap between FTO and IRX3 could be better described. I’m not an obesity researcher and am unfamiliar with the function of IRX3. Some additional description of the potential biology at this locus is warranted.\nResponse 1.2\nWe now describe the overlap between FTO and IRX3 in the results describing its earlier reported co-localization and chromatin interaction. Additionally, we also describe FTO locus in obesity in the discussion.\nResults: “Although obesity-associated variants are located within the FTO gene, they regulate the expression of the distant IRX3 gene through long-range chromatin interactions explaining the colocalization signal at this gene region 15. “\nDiscussion: “FTO locus is a well-known genetic region responsible for energy homeostasis and body weight. Variation in its intronic region are among the strongest associations with obesity 15. The functional effects of FTO variants are mediated through homeobox genes including IRX3 and the variants at the FTO locus act as long-range enhancers 15.”\nQuestion 1.3 The section running from lines 180 to 213 comes off more as discussion points as there are very few results reported. The overall presentation of the manuscript would be improved by moving most of this to discussion and including the eQTL results in the co-localization section (presuming that a formal colocalization analysis can be done between ZNF646 and FLG).\nResponse 1.3 Thank you for your comment. This is the chapter “GWAS associations highlight the role of compromised keratin processing behind dermatophytosis”. In order to provide the reader the context of results following this paragraph, we feel that grouping the genes into three different categories would be beneficial and facilitate understanding of the following sections.\nWe did not perform a formal colocalization analysis for the missense variants, as they are likely located within the causal gene itself.\nQuestion 1.4 Table 1 should report the effect allele. Also, given that this is a binary phenotype, I suggest reporting the effect size as Odds Ratio and 95% CI rather than beta and SE but this is personal preference.\nResponse 1.4.\nWe have added the OR (95% CI) as a column in Table 1 and 2. Additionally, we have also added effect and reference alleles to both tables.\nQuestion 1.5 Similarly in table two, when reporting association statistics the effect allele and variance around the point estimate of the effect should be reported. For this particular table, the association statistics could be moved to the supplementary materials to simplify the presentation.\nResponse 1.5\nWe have added effect and reference alleles as well as confidence intervals around the point estimate (OR 95 % CI) in Table 2 and moved beta and se to supplementary material.\nResponse to summary: Thank you for the overall comments and particularly pointing out the UKB paper. The earlier paper from UKB used Hospital diagnosis data only which contains 630 individuals with Dermatophytosis. We used additionally the data from primary care that includes 30,000 individuals with dermatophytosis diagnosis [Figure R1]. The difference in the cohorts for number of individuals and number of associating loci stem from the large difference in the number of cases that were not included in the earlier study.\nAdditionally, the earlier described variant does not have correlated variants in the regional association, and does not show any association with Dermatophytosis in our cohort (P = 0.67). This makes it possible that the earlier described association is false positive driven by the small number of individuals in the earlier GWAS and the low allele frequency of the risk TINAG variant.\nTo acknowledge the earlier effort, we have included the result from the paper in the discussion including a citation to the paper.\nFigure R1. Number of individuals with dermatophytosis from UKB registry data. The difference between the 2022 publication and our work is likely the number of cases. Earlier work used hospital level data only (N = 616), whereas our study also uses primary care data (N=32,414). It is possible that additional differences are related to the clinical picture, but this is unlikely given the relatively small sample size by the earlier publication.\nAdditionally, there are several points that should be clarified:\nQuestion 2.1. Introduction: Is there any specific age peak for skin infections, how about children and tinea?\nResponse 2.1 We added the following information in the introduction: “Dermatophytosis skin infections can develop to people of all ages. Infections of the scalp (tinea capitis), are more common in children, while other tineas more commonly affect postpubertal individuals with a peak at mid-age.7,8 ”\nQuestion 2.2. Are there any other genetic studies available beyond the 2022 research?\nResponse 2.2. We did not find other genome-wide association studies on dermatophytosis.\nQuestion 2.3. Are there any heritability estimates known?\nResponse 2.3. Earlier twin studies or GWAS have not estimated the heritability of dermatophytosis.\nTo fill this gap in the knowledge we computed the snp-based heritability from the current meta-analysis summary statistics as well as for all dermatophytosis subtypes (ICD-10 B35.x) in FinnGen.\nThe heritabilities were generally low (h2 meta-analysis = 0.0075). These heritabilities align with overall infection disease heritabilities where the environmental exposure to infection to start with typically has a large impact. For example, COVID-19 heritability was at a similar scale (h2 = 0.01).\nHowever, it does not necessarily mean that there would be only a small number of associating variants. We provide the heritability estimates in Table S8.\nQuestion 2.4. Results: Line 94 mentions an HLA locus, but it is not specified. Please clarify.\nResponse 2.4 We have additionally performed a correlation analysis between our lead variant rs1794269 and all HLA alleles with prevalence over 1 % in FinnGen study. We show a strongest correlation with DQB1*05:01 with r = 0.48. In addition we performed a formal HLA fine mapping in FinnGen for all HLA haplotypes. This analysis supports the association of DQB1*05:01 with Dermatophytosis with p-value of 0.005 and beta = 0.03.\nWe have edited the text:\n“The lead variant for the association (rs1794269, beta = 0.046 and p = 1.42x10-36) was located closest to the HLA-DQB1 gene but a correlation analysis suggests a strongest correlation with DQB1*05:01 with r = 0.48. Formal HLA fine mapping in FinnGen supports the association of DQB1*05:01 with Dermatophytosis with p-value of 0.005.The association between HLA region and dermatophytosis highlights an overall immune signal in dermatophytosis.”\nQuestion 2.5. Given the wide variety of tinea types (e.g., skin, hair, nail infections), and considering the large cohort with comprehensive clinical data, wouldn’t it be valuable to stratify the analysis based on different subtypes of tinea.\nResponse 2.5 We have run GWAS analyses separately for each subtype of dermatophytosis (B35.1, B35.2, B35.3, B35.4, B35.4, B35.5, B35.6, B35.8, and B35.9.) using data from FinnGen, Estonian Biobank, MVP and UK Biobank. We report the association statistics of main dermatophytosis lead variants in each subtype meta-analysis (Supplementary excel S9) and show that many of these are seen also in specific subtypes especially in B35.1 Tinea unguium (nail ringworm) and B35.3 Tinea pedis (athlete's foot), which have most cases and controls.\nAdditionally, we report novel subtype specific associations in the manuscript.\nWe have added the following results to the main manuscript: “Dermatophytosis can be classified into subtypes based on the region of the body where the infection is manifesting. To understand the subtype specific associations we additionally ran GWAS on each of these subtypes in FinnGen, EstBB, UKB, along with the publicly available genome-wide association data for subtypes in the MVP cohort (Figure 2). The association statistics for each of our lead variants in these subtypes are reported in Supplementary Table S9 (case and control numbers are presented in S8). We show association of 10 lead variants from the main dermatophytosis analysis at the subtype level (p < 0.0002, Bonferroni corrected threshold) especially in subtypes B35.1 (nail ringworm) and B35.3 (athlete’s foot). When studying the lead variants at the subtype level, we see a consistent pleiotropic association with a subset of variants across several different subtypes; FLG (B35.1 and B35.3), HLA (B35.1), LYNX1 (B35.3) and FTO (B35.1) at genome-wide significant level (p < 5x10-8) highlighting the role of these genes in the defence against dermatophytosis infections. In addition, we report novel associations in another zinc finger gene ZNF668 (B35.1 and B35.3) located in proximity of the ZNF646 gene. Furthermore we report 24 novel associations, such as rs12091247 closest to NOTCH2 gene that is linked to skin homeostasis and epidermal differentiation. We present Manhattan plots for each subtype in Supplementary Figures and the lead variants in Supplementary Table S10.“\nQuestion 2.6. Discussion: Many of the identified loci are also linked to other autoimmune disorders, such as rheumatoid arthritis, alopecia areata, and atopic conditions. These are summarized under inflammation, but this might not be immediately clear to readers unfamiliar with the genetic associations of these disorders.\nResponse 2.6. This is a great point, and we have clarified the connection between autoimmune diseases and dermatophytosis: “While dermatophytosis is primarily an infection, the variants that were associated with it, were also associated with autoimmune traits. The overall connection between infectious and immune loci that associate with autoimmunity needs to be further explored in future studies.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately responded to my suggestions. I have no further comments and support publication of the revised manuscript.", "reviewer_2": "I am very much satisfied with all changes the author made."}, "author_response": ""}]

Decision letter