# Unraveling shared genetics across asthma subtypes and 81 asthma-related traits

Raphaël Vernet, Christophe Linhard, Anja Estermann, Yuka Suzuki, Florence

Demenais, Hugues Aschard, Hanna Julienne, Emmanuelle Bouzigon

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Raphaël Vernet, Christophe Linhard, Anja Estermann, Yuka Suzuki, Florence Demenais, et al. Unraveling shared genetics across asthma subtypes and 81 asthma-related traits. Journal of Allergy and Clinical Immunology, In press, Online ahead of print. 10.1016/j.jaci.2025.07.036. hal-05307589

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# Unraveling shared genetics across asthma subtypes and 81 asthma-related traits

Raphaël Vernet, MD, Christophe Linhard, MSc, Anja Estermann, MSc, Yuka Suzuki, PhD, Florence Demenais, MD, Hugues Aschard, PhD, et al

## GRAPHICAL ABSTRACT

<figure><img src="image_2.png" /></figure>

**Capsule summary:** Newly uncovered shared genetics between asthma subtypes and multiple biological and physiologic traits highlights distinct genetic architectures across asthma subtypes, improving our understanding of asthma heterogeneity.
# Unraveling shared genetics across asthma subtypes and 81 asthma-related traits

Raphaël Vernet, MD,ª Christophe Linhard, MSc,ª Anja Estermann, MSc,ª Yuka Suzuki, PhD,ᵇ Florence Demenais, MD,ª Hugues Aschard, PhD,ᵇ Hanna Julienne, PhD,ᵇ,c and Emmanuelle Bouzigon, MD, PhDª Paris, France

**Background:** Asthma presents clinical and biological heterogeneity.

**Objective:** We sought to better understand asthma heterogeneity by investigating the shared genetics between various asthma subtypes and a large number of biological and physiologic traits involved in asthma pathophysiology.

**Methods:** We built a harmonized comprehensive database of 254 full genome-wide association study summary statistics datasets on asthma, asthma subtypes and 81 asthma-related traits (blood cells and molecular, anthropometric, and lung function traits). We enriched this database by performing meta-analyses for asthma-related traits reported in ≥2 studies. Then we identified shared genome-wide significant loci and estimated genetic overlaps and correlations ($r_g$) between asthma subtypes and asthma-related traits by MiXeR software and linkage disequilibrium score regression.

**Results:** Overall, asthma-associated loci were more pleiotropic than non-asthma-associated loci (median of shared traits, 4 vs 1, $P = 1.3 \times 10^{-36}$). Childhood-onset and moderate-to-severe asthma had higher SNP heritability ($h_{SNP}^2$ ± standard error [SE], 0.27 ± 0.004 and 0.16 ± 0.02, respectively) than adult-onset asthma (0.08 ± 0.002) and asthma ever (0.06 ± 0.001). All asthma subtypes showed significant $r_g$ with eosinophils and IgE levels (0.24 ≤ $r_g$ ≤ 0.40, 5.1 × $10^{-14}$ ≤ $P$ ≤ .04), with childhood-onset asthma sharing 94% of “causal” variants with IgE, whereas other asthma subtypes shared <60%. Adult-onset asthma showed significant $r_g$ and shared a substantial amount of causal variants with adult body mass index ($r_g = 0.26, P < .007$; shared variants, 84%) and forced expiratory volume in 1 second ($r_g = -0.36, P < 7.7 \times 10^{-10}$; shared variants, 95%). Finally, moderate-to-severe asthma was characterized by a significant $r_g$ with hepatocyte growth factor levels ($r_g = 0.38, P = 6 \times 10^{-4}$), a potential biomarker of lung injury.

**Conclusion:** Common and specific genetic architectures underlie different asthma subtypes and can help us understand the pathophysiologic mechanisms underlying asthma heterogeneity. (J Allergy Clin Immunol 2025;■■■:■■■-■■■.)

**Abbreviations used**

BMI: Body mass index

FEV₁: Forced expiratory volume in 1 second

FVC: Forced vital capacity

GWAS: Genome-wide association study

$h_{SNP}^2$: SNP heritability

HGF: Hepatocyte growth factor

LD: Linkage disequilibrium

LDSC: LD score regression

$r_g$: Genetic correlation

SE: Standard error

SNP: Single nucleotide polymorphism

**Key words:** Asthma, asthma-related traits, genetic heterogeneity, genetic correlation, GWAS summary statistics

Asthma is a complex and heterogeneous disease with a large spectrum of clinical and biological expressions reflecting the multiple underlying pathophysiologic mechanisms. It can be divided into subtypes according to various features, including age at asthma onset, presence of comorbidities (eg, allergy, obesity), inflammatory profiles (eg, eosinophilic, neutrophilic), and disease severity.¹,² This heterogeneity likely involves genetic heterogeneity.³

Genetics plays an important role in asthma, as evidenced by genome-wide association studies (GWAS), which have identified more than 200 asthma-associated genetic loci.⁴⁻⁶ Some of these have been found to be specifically associated with a given asthma subtype. Indeed, the largest multiancestry meta-analysis of asthma GWAS⁵ reported 12 loci specifically associated with adult-onset asthma, while 11 were specific to childhood-onset asthma, including the well-established ORMDL3/GSDMB locus in 17q12 region.⁷ GWAS have also been performed for many clinical and biological traits related to asthma, including lung function parameters, anthropometric measurements, blood cell counts, and molecular traits (eg, blood levels of IgE and multiple cytokines).⁸⁻¹³ Exploring the shared genetics between asthma subtypes and asthma-related traits can help in investigating the etiology of asthma heterogeneity. Indeed, compared to phenotypic correlations, genetic correlations can give more mechanistic insights through the identification of subtype-specific genetic pathways, and they are less prone to confounding by environmental factors.³ However, the huge amount of GWAS data for asthma and asthma-related traits has been reported in a heterogeneous manner, which impedes the exploration of the shared genetic components between asthma and its related traits.

Here, we integrated hundreds of publicly available full GWAS datasets on asthma subtypes and physiologic and biological asthma-related traits in order to investigate the genetic heterogeneity of asthma and the contribution of asthma-related traits to

<footnote>Fromª the Institut National de la Santé et de la Recherche Médicale (INSERM), Health-Fex, Group of Genomic Epidemiology of Multifactorial Diseases, Université Paris Cité; ᵇ the Institut Pasteur, Université Paris Cité, Department of Computational Biology; and ᶜ the Institut Pasteur, Bioinformatics and Biostatistics Hub, Université Paris Cité, Paris.</footnote>

<footnote>Received for publication February 12, 2025; revised July 18, 2025; accepted for publication July 31, 2025.</footnote>

<footnote>Corresponding author: Emmanuelle Bouzigon, MD, PhD, INSERM UMRS 1124, Université Paris Cité, Campus Saint-Germain-des-Prés, 45 rue des Saint Pères, 75006 Paris, France. E-mail: emmanuelle.bouzigon@inserm.fr.
0091-6749</footnote>

<footnote>© 2025 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>

<footnote>https://doi.org/10.1016/j.jaci.2025.07.036</footnote>
this heterogeneity. First, we harmonized and imputed summary statistics, and performed meta-analyses for each asthma-related trait reported in more than one GWAS, thereby enriching our database. We then identified shared genome-wide significant loci across asthma subtypes and asthma-related traits, and we used linkage disequilibrium (LD) score regression (LDSC) to estimate the genome-wide genetic correlations between asthma subtypes and asthma-related traits. Finally, we assessed their genetic overlap (ie, the number of shared genetic variants) using causal mixture models implemented in MiXeR.

# METHODS

## Study design

The overall study design is illustrated in Fig 1 and is further detailed in the Methods section in this article's Online Repository available at www.jacionline.org.

## Building a harmonized database of GWAS summary statistics on asthma and asthma-related traits

We performed an extensive review of the full GWAS summary statistics available for asthma, asthma subtypes, and a large number of biological and physiologic asthma-related traits using the GWAS Catalog<sup>14</sup> (www.ebi.ac.uk/gwas) and other GWAS resources including the Global Biobank Meta-analysis Initiative (www.globalbiobankmeta.org/resources), DeCODE Genetics (www.decode.com/summarydata), the GIANT consortium (gi-ant-consortium.web.broadinstitute.org/GIANT_cohort), and the Blood Cell Consortium (mhi-humangenetics.org/en/resources). We selected GWAS from subjects of European ancestry or multiancestry GWAS meta-analyses including mainly European subjects for asthma as well as GWAS involving exclusively European-ancestry subjects for asthma-related traits. In total, we gathered full genome-wide summary statistics from 15 GWAS of asthma subtypes (asthma ever, childhood-onset asthma, adult-onset asthma, and moderate-to-severe asthma; see Table E1 in the Online Repository available at www.jacionline.org) and 239 GWAS of 81 asthma-related traits comprising 52 molecular traits, 22 blood cell counts, 4 anthropometric measurements, and 3 lung function measurements (see Table E2, also in the Online Repository).

All GWAS datasets were harmonized by the JASS (Joint Analysis of Summary Statistics) preprocessing pipeline,<sup>15</sup> which aligned all single nucleotide polymorphisms (SNPs) to the 1,000G reference panel and excluded SNPs with <1% minor allele frequency (detailed in the Methods section in the Online Repository). Not all GWAS report the same sets of SNPs, so we also performed imputation of missing summary statistics using RAISS (Robust and Accurate Imputation of Summary Statistics) v3.0<sup>16</sup> on the 239 GWAS datasets of asthma-related traits to increase the number of SNP-trait pairs with a Z score available.

## Meta-analyses for asthma-related traits and identification of new loci

Of the 81 asthma-related traits considered, 54 had data from more than one GWAS. For each of these traits considered separately, we selected studies with no overlapping samples and combined their results using weighted Z score meta-analysis (see the Methods section and Table E2 in the Online Repository

available at www.jacionline.org). This gave us a single dataset of GWAS summary statistics per trait. For each asthma-related trait, we then checked whether our meta-analyses revealed new genome-wide significant loci ($P \le 5 \times 10^{-8}$), and we performed functional annotation of these loci (see the Methods section in the Online Repository).

## Shared genome-wide significant loci across asthma subtypes and asthma-related traits

We identified shared genomic loci across asthma subtypes and asthma-related traits using a strategy similar to the one used by Watanabe et al<sup>17</sup> (see, in the Online Repository available at www.jacionline.org, the Methods section and Fig E1). Briefly, we started by identifying “LD regions” (groups of SNPs in LD) associated with each asthma subtype and each asthma-related trait at the genome-wide significance level ($P \le 5 \times 10^{-8}$) using the ‘clump’ function in PLINK v1.9 ($r^2$ threshold of 0.6 and distance window of 250 kb). We then grouped overlapping LD regions across asthma subtypes and asthma-related traits. This grouping procedure allowed us to differentiate two types of genomic loci: first, an asthma locus associated with $\ge 1$ asthma subtype and zero or any number of asthma-related traits, and second, a non-asthma locus associated only with asthma-related traits.

## SNP heritability of asthma subtypes and asthma-related traits and their genetic correlations

We performed univariate LDSC to estimate the SNP heritability ($h^2_{SNP}$) of asthma subtypes and 81 asthma-related traits.<sup>18</sup> For binary traits, the SNP heritability was converted to the liability scale<sup>19</sup> using disease prevalence curated from the literature (see Table E3 in the Online Repository available at www.jacionline.org).

We then performed bivariate LDSC to calculate genetic correlations ($r_g$) between asthma subtypes and asthma-related traits.<sup>20</sup> All LDSC analyses used LD scores derived from the 1,000G European-ancestry reference panel. We used the Benjamini-Hochberg procedure to control the false discovery rate across all genetic correlations tested. All LDSC analyses were performed on nonimputed Z scores to avoid potential bias or inflation in $h^2_{SNP}$ and $r_g$ estimates. We also run LDSC on imputed summary statistics to check the robustness of the results.

## Genetic overlap

The genome-wide genetic correlation estimated by LDSC measures the genetic similarity between two traits, but the estimate can be close to zero even in the presence of genetic overlap—for example, if ~50% of the shared genetic variants have opposite directions of effect on the two traits. To estimate the genetic overlap between asthma and asthma-related traits irrespective of effect direction, we used the univariate and bivariate causal mixture models for GWAS summary statistics implemented in MiXeR v1.3 software.<sup>21,22</sup> MiXeR allowed us to estimate the number of variants explaining 90% of the SNP heritability of each asthma subtype and asthma-related trait (termed *causal variants*—that is, variants with a nonzero genetic effect on the trait) as well as the number of shared causal variants between asthma subtypes and asthma-related traits (see the Methods section in the Online Repository). All analyses were
<figure><img src="image_3.png" /><figcaption>FIG 1. Overall study design. EUR, European ancestry; WHR<sub>adjBMI</sub>, waist-to-hip ratio adjusted for BMI.</figcaption></figure>

performed using the 1,000G European-ancestry reference panel and after excluding SNPs in the HLA region (chr6: 26-34 Mbp).

cell proliferation (MDM1, SPOUT1, CEP192, KIAA1614), and chromatin remodeling and transcriptional regulation (TRIP4, ZNF33B, COPRS, KDM3B) (see Table E4 in the Online Repository).

## RESULTS

### Collection and harmonization of summary statistics

We collected full GWAS summary statistics from 15 GWAS of asthma and asthma subtypes (10 for asthma ever, 2 for childhood-onset asthma, 2 for adult-onset asthma, and 1 for moderate-to-severe asthma) (Table E1) and 239 GWAS of 81 different asthma-related traits (Table E2). After curation and harmonization, our database included 7,251,915 SNPs and their Z scores for association with at least one asthma subtype or asthma-related trait. Imputation of missing summary statistics using RAISS¹⁶ decreased the proportion of missing Z scores from 7.8% to 4.3%.

The meta-analyses conducted separately for each of the 54 asthma-related traits studied in more than one GWAS revealed 7,089 new genome-wide significant signals. This allowed us to identify 41 new loci (as explained in the Methods section in the Online Repository) for 19 molecular traits (24 loci) and 3 blood cell traits (17 loci) (Table I), thus enriching our working database. Manhattan plots of these 22 traits are shown in Fig E2 in the Online Repository available at www.jacionline.org. Functional annotation of these loci allowed the identification of 42 putative candidate genes, of which 7 (17%) were already reported associated with asthma (Table I).¹⁴ Other candidates included genes involved in inflammation and immune response (IFNG, PDGFB, ORAI1), cell signaling (ANKDD1A, MCF2L, DGKD, AKAP13),

### Shared genome-wide significant loci across asthma subtypes and asthma-related traits

We applied our grouping procedure to the genome-wide significant SNPs and identified 1,592 distinct genomic loci associated with asthma and/or asthma-related traits at $P \le 5 \times 10^{-8}$ (see Table E5 in the Online Repository available at www.jacionline.org). These included 249 asthma loci, of which 70 (28%) were subtype specific: 60 loci specific to childhood-onset asthma, 10 to adult-onset asthma, and none to moderate-to-severe asthma (Fig 2).

The 1,592 loci were associated with a mean number of 2.2 asthma-related traits, ranging from 0 traits (asthma loci not associated with any asthma-related trait) to 19 traits (a locus spanning the HLA region, associated with all asthma subtypes and multiple lung function, anthropometric, and molecular traits). Overall, asthma loci were associated with a higher number of asthma-related traits than nonasthma loci (median of 4 traits vs 1 trait, $P = 1.3 \times 10^{-36}$). This difference remained significant when restricting the comparison to blood cells ($P = 2.7 \times 10^{-31}$) or molecular traits ($P = .009$). Among asthma-related traits, eosinophils shared the greatest number of loci with asthma (134 shared loci,
<table><caption>TABLE I. New loci associated with asthma-related traits in meta-analysis and their functional annotations based on cis-eQTLs</caption><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Chr</th><th rowspan="2">Bp<br>(GRCh37)</th><th rowspan="2">Index SNP</th><th rowspan="2">Gene<br>(previous/<br>next)</th><th rowspan="2">Effect<br>allele</th><th colspan="2">Meta-analysis</th><th colspan="3">eQTL search results</th></tr><tr><th>Z</th><th>P</th><th>Gene (eQTL<br>proxy; r<sup>2</sup> with<br>index SNP)</th><th>Direction<br>of effect*</th><th>P</th><th>Source/<br>tissue</th></tr></thead><tbody><tr><td>CCL17</td><td>12</td><td>6,493,351</td><td>rs10849448</td><td>LTBR</td><td>A</td><td>5.92</td><td>3.2 × 10<sup>-9</sup></td><td>LTBR†</td><td>+</td><td>2.9 × 10<sup>-195</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>CCL2</td><td>15</td><td>64,570,076</td><td>rs190136609</td><td>CSNK1G1</td><td>G</td><td>5.56</td><td>2.8 × 10<sup>-8</sup></td><td>TRIP4</td><td>+</td><td>1.50 × 10<sup>-142</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>ANKDD1A</td><td>—</td><td>7.26 × 10<sup>-90</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>CCL24</td><td>14</td><td>30,295,088</td><td>rs185279566</td><td>PRKD1</td><td>A</td><td>5.62</td><td>1.9 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>CCL27</td><td>2</td><td>177,011,655</td><td>rs10196832</td><td>HOXD3</td><td>C</td><td>5.78</td><td>7.3 × 10<sup>-9</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>CCL3</td><td>5</td><td>24,232,202</td><td>rs145085649</td><td>C5orf17/<br>CDH10</td><td>C</td><td>5.67</td><td>1.5 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>CCL4</td><td>9</td><td>120,467,576</td><td>rs10818073</td><td>TLR4</td><td>T</td><td>-5.55</td><td>2.8 × 10<sup>-8</sup></td><td>RP11-281A20.1<br>(rs4986791;<br>r<sup>2</sup> = 0.95)</td><td>+</td><td>8.81 × 10<sup>-6</sup></td><td>BIOSeqtl/<br>blood</td></tr><tr><td>CCL5</td><td>13</td><td>87,604,563</td><td>rs370824499</td><td>SLITRK6/<br>SLITRK5</td><td>G</td><td>5.50</td><td>3.9 × 10<sup>-8</sup></td><td>SLITRK5<br>(rs9301841;<br>r<sup>2</sup> = 1.0)</td><td>—</td><td>7.73 × 10<sup>-12</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>CXCL10</td><td>12</td><td>68,597,086</td><td>rs741344</td><td>IL26</td><td>G</td><td>5.70</td><td>1.2 × 10<sup>-8</sup></td><td>MDM1</td><td>—</td><td>3.69 × 10<sup>-33</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>IFNG</td><td>+</td><td>6.36 × 10<sup>-13</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>CXCL8</td><td>17</td><td>38,175,462</td><td>rs12309</td><td>MED24</td><td>T</td><td>5.48</td><td>4.3 × 10<sup>-8</sup></td><td>GSDMB†</td><td>—</td><td>4.47 × 10<sup>-288</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>ORMDL3†</td><td>—</td><td>9.92 × 10<sup>-218</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>ECP</td><td>10</td><td>43,132,398</td><td>rs61842935</td><td>ZNF33B</td><td>T</td><td>-5.52</td><td>3.5 × 10<sup>-8</sup></td><td>ZNF33B</td><td>—</td><td>5.93 × 10<sup>-17</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>EDN</td><td>5</td><td>75,482,512</td><td>rs7448058</td><td>SV2C</td><td>A</td><td>6.16</td><td>7.2 × 10<sup>-10</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>FGF2</td><td>10</td><td>84,469,123</td><td>rs34000176</td><td>NRG3</td><td>G</td><td>5.47</td><td>4.6 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>HGF</td><td>8</td><td>106,589,247</td><td>rs16873402</td><td>ZFPM2</td><td>T</td><td>-6.32</td><td>2.6 × 10<sup>-10</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>IgE</td><td>2</td><td>11,503,540</td><td>rs142305545</td><td>ROCK2/<br>E2F6</td><td>C</td><td>5.49</td><td>4.0 × 10<sup>-8</sup></td><td>SLC66A3</td><td>+</td><td>2.67 × 10<sup>-42</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>MCF2L<br>(rs113051014;<br>r<sup>2</sup> = 0.92)</td><td>+</td><td>6.15 × 10<sup>-12</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>IL17A</td><td>12</td><td>77,726,714</td><td>rs78629931</td><td>E2F7/NAV3</td><td>C</td><td>-5.80</td><td>6.7 × 10<sup>-9</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>IL18</td><td>5</td><td>72,524,333</td><td>rs151103086</td><td>TMEM174/<br>FOXD1</td><td>G</td><td>-5.47</td><td>4.6 × 10<sup>-8</sup></td><td>TMEM171</td><td>—</td><td>6.34 × 10<sup>-23</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>IL4</td><td>15</td><td>79,597,917</td><td>rs76678067</td><td>ANKRD34C/<br>TMED3</td><td>G</td><td>-5.73</td><td>1.0 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>MIF</td><td>2</td><td>234,665,983</td><td>rs111741722</td><td>UGT1A3;<br>UGT1A10</td><td>G</td><td>5.55</td><td>2.8 × 10<sup>-8</sup></td><td>USP40<br>(rs6742078;<br>r<sup>2</sup> = 1.0)</td><td>+</td><td>1.97 × 10<sup>-10</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>DGKD</td><td>—</td><td>3.97 × 10<sup>-7</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>PDGFβ</td><td>1</td><td>205,240,141</td><td>rs1668868</td><td>TMCC2</td><td>T</td><td>5.79</td><td>6.9 × 10<sup>-9</sup></td><td>DSTYK</td><td>—</td><td>&lt;10<sup>-300</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>DSTYK</td><td>+</td><td>1.34 × 10<sup>-7</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>TMEM81</td><td>—</td><td>1.02 × 10<sup>-7</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>TMEM81</td><td>—</td><td>3.86 × 10<sup>-38</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>TMCC2</td><td>—</td><td>1.01 × 10<sup>-51</sup></td><td>eQTLGen/<br>blood</td></tr></tbody></table>

(Continued)
<table><caption>TABLE I. (Continued)</caption><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Chr</th><th rowspan="2">Bp<br>(GRCh37)</th><th rowspan="2">Index SNP</th><th rowspan="2">Gene<br>(previous/<br>next)</th><th rowspan="2">Effect<br>allele</th><th colspan="2">Meta-analysis</th><th colspan="3">eQTL search results</th></tr><tr><th>Z</th><th>P</th><th>Gene (eQTL<br>proxy; r<sup>2</sup> with<br>index SNP)</th><th>Direction<br>of effect*</th><th>P</th><th>Source/<br>tissue</th></tr></thead><tbody><tr><td></td><td>9</td><td>4,763,176</td><td>rs385893</td><td>AK3/RCL1</td><td>C</td><td>6.41</td><td>1.4 × 10<sup>-10</sup></td><td>AK3</td><td>—</td><td>1.99 × 10<sup>-20</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>9</td><td>99,089,943</td><td>rs11792848</td><td>SLC35D2</td><td>A</td><td>-6.76</td><td>1.4 × 10<sup>-11</sup></td><td>HABP4</td><td>+</td><td>1.79 × 10<sup>-68</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>12</td><td>122,216,910</td><td>rs11553699</td><td>TMEM120B;<br>RHOF</td><td>G</td><td>5.56</td><td>2.7 × 10<sup>-8</sup></td><td>ORAI1</td><td>+</td><td>4.15 × 10<sup>-24</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>HPD</td><td>—</td><td>5.37 × 10<sup>-22</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>22</td><td>39,693,408</td><td>rs137602</td><td>PDGFB/<br>RPL3</td><td>G</td><td>5.83</td><td>5.5 × 10<sup>-9</sup></td><td>SYNGR1</td><td>+</td><td>&lt;10<sup>-300</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>PDGFB</td><td>+</td><td>4.78 × 10<sup>-20</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td rowspan="4">Basophils</td><td>4</td><td>185,769,840</td><td>rs4862429</td><td>ACSL1/HELT</td><td>T</td><td>5.46</td><td>4.8 × 10<sup>-8</sup></td><td>RP11-701P16.5</td><td>+</td><td>5.35 × 10<sup>-11</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>5</td><td>118,685,011</td><td>rs17145248</td><td>TNFAIP8</td><td>T</td><td>-5.48</td><td>4.2 × 10<sup>-8</sup></td><td>TNFAIP8†<br>(rs77384170;<br>r<sup>2</sup> = 0.9)</td><td>—</td><td>2.94 × 10<sup>-51</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td>9</td><td>131,562,232</td><td>rs2417125</td><td>TBC1D13</td><td>G</td><td>-5.46</td><td>4.7 × 10<sup>-8</sup></td><td>ENDOG</td><td>—</td><td>2.88 × 10<sup>-182</sup></td><td>BIOSQTL/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>SPOUT1</td><td>—</td><td>2.88 × 10<sup>-182</sup></td><td>BIOSQTL/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>ENDOG</td><td>—</td><td>1.48 × 10<sup>-9</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>SPOUT1</td><td>—</td><td>3.86 × 10<sup>-34</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td>12</td><td>48,209,663</td><td>rs11168251</td><td>HDAC7</td><td>A</td><td>-5.48</td><td>4.2 × 10<sup>-8</sup></td><td>SLC48A1</td><td>+</td><td>1.78 × 10<sup>-55</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>RPAP3</td><td>—</td><td>7.98 × 10<sup>-53</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>17</td><td>30,104,598</td><td>rs1012638</td><td>GPR160P2/<br>COPRS</td><td>A</td><td>-5.51</td><td>3.7 × 10<sup>-8</sup></td><td>COPRS</td><td>+</td><td>2.86 × 10<sup>-17</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td rowspan="2">Leukocytes</td><td>6</td><td>151,721,749</td><td>rs7771156</td><td>ZBTB2/<br>RMND1</td><td>A</td><td>-5.47</td><td>4.5 × 10<sup>-8</sup></td><td>RMND1</td><td>—</td><td>1.03 × 10<sup>-173</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>RMND1</td><td>—</td><td>1.33 × 10<sup>-9</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td>14</td><td>70,196,357</td><td>rs61982064</td><td>SRSF5</td><td>A</td><td>-5.55</td><td>2.9 × 10<sup>-8</sup></td><td>SUSD6†</td><td>+</td><td>6.55 × 10<sup>-17</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>14</td><td>94,851,806</td><td>rs56019009</td><td>SERPINA1</td><td>T</td><td>-5.52</td><td>3.4 × 10<sup>-8</sup></td><td>SERPINA1†</td><td>—</td><td>4.41 × 10<sup>-249</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>15</td><td>51,705,986</td><td>rs2445754</td><td>GLDN/<br>DMXL2</td><td>G</td><td>5.58</td><td>2.4 × 10<sup>-8</sup></td><td>TMOD2</td><td>—</td><td>6.74 × 10<sup>-103</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>18</td><td>12,741,267</td><td>rs7242404</td><td>PSMG2/<br>PTPN2</td><td>A</td><td>-5.46</td><td>4.7 × 10<sup>-8</sup></td><td>CEP192</td><td>—</td><td>2.68 × 10<sup>-20</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td rowspan="2">Lymphocytes</td><td>1</td><td>180,909,061</td><td>rs1609829</td><td>KIAA1614</td><td>T</td><td>-5.52</td><td>3.4 × 10<sup>-8</sup></td><td>KIAA1614</td><td>+</td><td>3.82 × 10<sup>-15</sup></td><td>GTExV8/<br>fibroblasts</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>KIAA1614</td><td>+</td><td>6.27 × 10<sup>-7</sup></td><td>GTExV8/<br>lung</td></tr><tr><td></td><td>5</td><td>137,854,924</td><td>rs13155345</td><td>ETF1</td><td>C</td><td>-5.52</td><td>3.4 × 10<sup>-8</sup></td><td>ETF1†</td><td>—</td><td>4.89 × 10<sup>-31</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>KDM3B</td><td>+</td><td>3.68 × 10<sup>-51</sup></td><td>eQTLGen/<br>blood</td></tr></tbody></table>

(Continued)
<table><caption>TABLE I. (Continued)</caption><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Chr</th><th rowspan="2">Bp<br>(GRCh37)</th><th rowspan="2">Index SNP</th><th rowspan="2">Gene<br>(previous/<br>next)</th><th rowspan="2">Effect<br>allele</th><th colspan="2">Meta-analysis</th><th colspan="4">eQTL search results</th></tr><tr><th>Z</th><th>P</th><th>Gene (eQTL<br>proxy; r<sup>2</sup> with<br>index SNP)</th><th>Direction<br>of effect*</th><th>P</th><th>Source/<br>tissue</th></tr></thead><tbody><tr><td></td><td>5</td><td>143,188,976</td><td>rs180798958</td><td>NR3C1/<br>HMHB1</td><td>A</td><td>-5.46</td><td>4.7 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>8</td><td>13,215,223</td><td>rs73203985</td><td>DLC1</td><td>A</td><td>5.49</td><td>3.9 × 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>10</td><td>114,188,625</td><td>rs2256624</td><td>ACSL5/<br>ZDHHC6</td><td>A</td><td>-5.51</td><td>3.7 × 10<sup>-8</sup></td><td>ACSL5</td><td>+</td><td>3.28 × 10<sup>-89</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>11</td><td>75,110,952</td><td>rs534812</td><td>RPS3</td><td>G</td><td>5.46</td><td>4.7 × 10<sup>-8</sup></td><td>KLHL35</td><td>-</td><td>&lt;10<sup>-300</sup></td><td>eQTLGen/<br>blood</td></tr><tr><td></td><td>15</td><td>86,162,874</td><td>rs4843086</td><td>AKAP13</td><td>A</td><td>5.49</td><td>4.0 × 10<sup>-8</sup></td><td>AKAP13</td><td>-</td><td>2.42 × 10<sup>-66</sup></td><td>eQTLGen/<br>blood</td></tr></tbody></table>

eQTL, Expression quantitative trait locus.

*Direction of eQTL effect considering effect allele of index SNP in meta-analysis (or allele correlated with effect allele of index SNP for proxies).

†eQTL genes that have already been associated with asthma in GWAS catalog.

<figure><img src="image_4.png" /><figcaption>FIG 2. Number of shared genome-wide significant genomic loci across asthma subtypes. Right, Bar plot indicating number of loci associated with each asthma subtype at $P \le 5 \times 10^{-8}$. Bar plot at top indicates size of each combination—that is, number of loci associated with that specific combination of asthma subtypes. Combinations corresponding to loci specifically associated with childhood-onset asthma and adult-onset asthma (with or without asthma ever) appear in purple and blue, respectively.</figcaption></figure>

53.8% of asthma loci) (see Fig E3 in the Online Repository available at www.jacionline.org).

## SNP heritability of asthma, asthma subtypes, and asthma-related traits

Analyses were conducted on the largest study for each asthma subtype: Tsuo et al⁵ for asthma ever, Zhu et al²³ for childhood-onset and adult-onset asthma, and Shrine et al²⁴ for moderate-to-severe asthma.

LDSC heritability estimates for asthma subtypes and asthma-related traits are shown in Table E6 in the Online Repository available at www.jacionline.org. Childhood-onset asthma and moderate-to-severe asthma had higher $h^2_{SNP}$ ($h^2_{SNP} \pm SE$: 0.27 $\pm$ 0.004 and 0.16 $\pm$ 0.02, respectively) than adult-onset asthma (0.08 $\pm$ 0.002) and asthma ever (0.06 $\pm$ 0.001). Among the 81 asthma-related traits, anthropometric measurements had the highest $h^2_{SNP}$ (mean $h^2_{SNP}$ = 0.20 with a maximum $h^2_{SNP}$

of 0.41 $\pm$ 0.04 for childhood obesity), followed by blood cell traits (mean $h^2_{SNP}$ = 0.16), lung function traits (mean $h^2_{SNP}$ = 0.11), and finally molecular traits (mean $h^2_{SNP}$ = 0.07), among which IgE had the highest $h^2_{SNP}$ (0.24 $\pm$ 0.06) (Table E6).

All asthma subtypes and 38 (47%) of 81 asthma-related traits showed sufficient polygenic signal to be included in subsequent genetic correlation and genetic overlap analyses according to previously suggested criteria:²⁵ $h^2_{SNP}$ $Z > 1.5$ and mean $\chi^2 > 1.02$.

## Genetic correlations between asthma, asthma subtypes, and asthma-related traits

We conducted bivariate LDSC analysis to estimate the $r_g$ between asthma subtypes and 38 asthma-related traits that had sufficient polygenic signal, totaling 861 genetic correlations (see Table E7 in the Online Repository available at www.jacionline.org).
<table><caption>TABLE II. Liability-scale SNP-based heritability (diagonal) of asthma subtypes and their genetic correlations (lower diagonal) estimated by LDSC</caption><thead><tr><th>Characteristic</th><th>Asthma ever<br>(121,940 vs 1,254,131)</th><th>Childhood-onset asthma<br>(9,676 vs 347,481)</th><th>Adult-onset asthma<br>(22,296 vs 347,481)</th><th>Moderate-to-severe asthma<br>(5,135 vs 25,675)</th></tr></thead><tbody><tr><td>Asthma ever</td><td>0.06 (0.001)</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Childhood-onset asthma</td><td>0.68 (0.04)*</td><td>0.27 (0.004)</td><td>—</td><td>—</td></tr><tr><td>Adult-onset asthma</td><td>0.95 (0.04)*</td><td>0.63 (0.07)*</td><td>0.08 (0.002)</td><td>—</td></tr><tr><td>Moderate-to-severe asthma</td><td>0.9 (0.04)*</td><td>0.78 (0.06)*</td><td>1.01 (0.07)*</td><td>0.16 (0.02)</td></tr></tbody></table>

Indicated with asthma subtypes are numbers of cases vs controls. Standard errors of heritability and genetic correlation estimates are in parentheses.

*Statistically significant genetic correlations after multiple-testing correction.

<figure><img src="image_5.png" /><figcaption>FIG 3. Genetic correlations between asthma subtypes and asthma-related traits. Only 21 traits showing nominally significant genetic correlation ($r_g$) with at least one asthma subtype are presented. Points represent LDSC estimates of genetic correlation between asthma-related traits (gray boxes) of each category (molecular, anthropometric, blood cell count, and lung function) and asthma ever (black), childhood-onset asthma (pink), adult-onset asthma (blue), and moderate-to-severe asthma (yellow). Error bars denote 95% confidence intervals. Denoted are significant genetic correlations at nominal threshold $P \le 5\%$ (black star) or at FDR $\le 5\%$ (red star) (FDR correction over 861 trait pairs between 4 asthma subtypes and 38 asthma-related traits, corresponding to $P$-value threshold of $P \le 6.1 \times 10^{-3}$). FDR, False discovery rate.</figcaption></figure>
A

<figure><img src="image_6.png" /></figure>

B

<figure><img src="image_7.png" /><figcaption>**FIG 4.** SNP heritabilities of asthma-related traits and their genetic correlations. (A) Bar plot of SNP heritabilities ($h^2_{SNP}$) of asthma-related traits estimated with LDSC. Error bars depict standard errors of observed-scale heritability. (B) Heat map of genetic correlations between asthma-related traits estimated with LDSC. *Asterisks* denote significant genetic correlations at nominal threshold *P* ≤ .05 or at FDR ≤ 5%—that is, **P* ≤ 6.1 × 10⁻³. Only 21 traits that had nominally significant genetic correlation with at least one asthma subtype are presented. *FDR*, False discovery rate.</figcaption></figure>

Asthma subtypes were strongly genetically correlated with each other ($0.63 \le r_g \le 0.95$, $P \le 3.5 \times 10^{-21}$) (Table II). Overall, asthma subtypes showed nominally significant genetic correlations ($P \le 5\%$) with 21 asthma-related traits (4 anthropometric, 3 lung function, 3 blood cell, and 11 molecular traits), of which 12 remained after multiple-testing correction ($P \le 6.1 \times 10^{-3}$) (Fig 3). All asthma subtypes showed positive genetic correlations

with eosinophil counts ($0.32 \le r_g \le 0.40$, $5.1 \times 10^{-14} \le P \le 1.8 \times 10^{-7}$) and IgE levels ($0.24 \le r_g \le 0.38$, $6.5 \times 10^{-5} \le P \le 3.6 \times 10^{-2}$). Asthma ever, adult-onset asthma, and moderate-to-severe asthma showed positive genetic correlations with body mass index (BMI) and childhood obesity ($0.15 \le r_g \le 0.26$, $P < .007$), and negative genetic correlations with lung function traits ($-0.39 \le r_g \le -0.18$, $P < .004$). Childhood-onset asthma
<figure><img src="image_8.png" /><figcaption>**FIG 5.** MiXeR estimates of genetic overlap between asthma subtypes and 5 characteristic asthma-related traits. Venn diagrams show number of variants (in thousands; standard error in parentheses) influencing each asthma subtype and (A) lymphocytes, (B) eosinophils, (C) IgE levels, (D) adult BMI, and (E) FEV₁, permitting discrimination of variants shared between trait and asthma (gray), variants unique to asthma (blue), and variants unique to trait (orange). Circle size is proportional to polygenicity. Bars below circles</figcaption></figure>
showed lower genetic correlations with lung function traits ($r_g = -0.16$ and $r_g = -0.22$ for forced expiratory volume in 1 second (FEV₁) and for the ratio of FEV₁ to forced vital capacity (FVC), respectively, $P < 0.005$) and no significant correlation with BMI or childhood obesity. Interestingly, moderate-to-severe asthma was genetically correlated with the highest number of molecular traits, among which were hepatocyte growth factor (HGF) ($r_g = 0.38, P = 6 \times 10^{-4}$) and 7 other traits (IgE, eosinophil cationic protein, CCL3, CXCL9, CXCL10, IL1RA, and stem cell growth factor β; $0.27 \le r_g \le 0.46, 0.01 < P < 0.05$).

Finally, we focused on the genetic correlations within those 21 asthma-related traits (Fig 4). There was an overall trend toward positive genetic correlations of molecular traits with blood cell counts and negative genetic correlations of all asthma-related traits with the 3 lung function measurements. For anthropometric traits, although adult BMI, childhood BMI, and childhood obesity showed strong $r_g$ with each other ($r_g \ge 0.73, P \le 6.8 \times 10^{-93}$), only adult BMI showed significant $r_g$ with molecular traits ($0.23 \le r_g \le 0.28$ for CCL3, IL-16, CCL7, and HGF, $P \le 9.0 \times 10^{-4}$) and lung function traits ($r_g < -0.15$ for FEV₁ and FVC, $P \le 1.8 \times 10^{-8}$). Of all asthma-related traits, HGF showed the strongest negative $r_g$ with FEV₁ and FVC ($r_g = -0.29$ and $-0.32$, respectively, $P \le 4 \times 10^{-4}$).

## Genetic overlap between asthma subtypes and asthma-related traits

To further analyze the shared genetics between asthma subtypes and the 38 asthma-related traits with sufficient polygenic signal, we applied univariate and bivariate MiXeR analyses (see Tables E8 and E9 in the Online Repository available at www.jacionline.org). Overall, 2,115 (SE ± 149) common variants were estimated to confer liability to asthma ever (ie, causal variants), suggesting that asthma ever is the most polygenic of asthma subtypes under study.

The different asthma subtypes shared varying proportions of estimated causal variants with blood cell counts, anthropometric measurements, lung function traits, and IgE levels (ranging between 16% to 98%; Fig 5, and see Fig E4 in the Online Repository available at www.jacionline.org) but few variants with other molecular traits (0.1-12%). All asthma subtypes showed substantial genetic overlap with eosinophils (proportion of asthma causal variants shared with eosinophils, >55%), particularly childhood-onset asthma (98%) and moderate-to-severe asthma (95%). Childhood-onset asthma also overlapped strongly with lymphocytes (96%) and IgE levels (94%), and moderate-to-severe asthma and adult-onset asthma with adult BMI (92% and 84%, respectively). Finally, both adult-onset asthma and asthma ever overlapped most with FEV₁ (95% and 80%, respectively). Moreover, all asthma subtypes showed substantial genetic overlap with neutrophils (52-81%; Fig E4), but approximately 50% of the shared variants had opposite directions of effect, consistent with the null genetic correlation observed between asthma and neutrophils in LDSC analysis.

Overall, MiXeR estimates of $h^2_{SNP}$ and $r_g$ were consistent and showed strong correlation with those from LDSC ($r = 0.85$ for $h^2_{SNP}$ and $r = 0.83$ for $r_g$), although greater variability was

observed for molecular traits (see Fig E5 in the Online Repository available at www.jacionline.org).

## DISCUSSION

In this study, we built what is to our knowledge the largest database of harmonized full GWAS summary statistics on asthma subtypes and 81 physiologic and biological asthma-related traits, which allowed us to uncover their shared genetics. We identified genome-wide significant loci shared between asthma subtypes and asthma-related traits. We then showed that the asthma subtypes had both common and unique patterns of genetic correlation and overlap across asthma-related traits. While all asthma subtypes showed strong genetic correlations with eosinophils, adult-onset asthma and asthma ever were characterized by significant genetic correlations and substantial overlap with adult BMI and FEV₁, childhood-onset asthma by a near-complete overlap in causal variants with IgE levels, and moderate-to-severe asthma by a significant positive genetic correlation with HGF.

Asthma subtypes varied in polygenicity and SNP heritability. Asthma ever, the most polygenic phenotype, was also the least heritable. Conversely, childhood-onset asthma, which had the highest SNP heritability, displayed the least polygenicity, suggesting that it may represent the most clinically and biologically homogeneous subtype.

Overall, our analysis of shared genome-wide significant loci identified both asthma loci specific to certain asthma subtypes and asthma loci common to all subtypes, strengthening previous findings,⁷,²⁶,²⁷ such as the GSDMB/ORMDL3 locus that was shared between moderate-to-severe and childhood-onset asthma but not adult-onset asthma. Childhood-onset asthma had the largest number of subtype-specific loci (Fig 2) and showed significant but incomplete genetic correlation with adult-onset and moderate-to-severe asthma ($r_g = 0.63$ and 0.78, respectively). These findings, taken together, support the well-documented hypothesis that distinct genetic factors contribute to the development of different asthma subtypes.³

All asthma subtypes were genetically correlated with total IgE levels (average $r_g = 0.30$) and eosinophil counts (average $r_g = 0.35$), which were also genetically correlated with each other ($r_g = 0.22, P = 9 \times 10^{-4}$). IgE and eosinophils are known to play a major role in asthma pathophysiology and are the targets of biological therapies used in severe forms of asthma.²⁸ Previous studies have reported a similar positive genetic correlation of eosinophils with asthma ever²⁹,³⁰ and moderate-to-severe asthma,³⁰ and our study demonstrated that this correlation also applied to childhood-onset and adult-onset asthma ($r_g = 0.34$ and 0.40, respectively). A single study reported a genetic correlation between IgE levels and asthma in East Asian adults ($r_g = 0.5, P = 5 \times 10^{-4}$),³¹ but it did not examine different asthma subtypes. The fact that we observed genetic correlations of IgE with all asthma subtypes is in agreement with the previously reported shared genetic component between asthma and allergic diseases.⁶,³² Interestingly, MiXeR analysis revealed that almost all “causal” variants of childhood-onset asthma (94%) were shared with IgE levels, enhancing the primary role of the allergic component in this asthma subtype.¹,² Conversely, adult-onset
asthma appeared to share a lower fraction of its causal variants (24%) with IgE levels, supporting the hypothesis that nonallergic mechanisms (or at least non-IgE-mediated mechanisms) may play a key role in later-onset asthma.<sup>1,2</sup>

Some patterns of genetic correlations with asthma-related traits were specific to certain asthma subtypes. Indeed, most genetic correlations with cytokines were specific to moderate-to-severe asthma, in particular the correlation with HGF ($r_g = 0.38, P = 6 \times 10^{-7}$). HGF is a cytokine secreted by mesenchymal cells, including alveolar fibroblast.<sup>33</sup> It exerts mainly anti-inflammatory activity<sup>34</sup> and has been shown to promote lung tissue repair and regulate airway remodeling,<sup>35</sup> which usually increase with asthma severity.<sup>36</sup> In our study, HGF showed the strongest negative genetic correlations with FEV₁ ($r_g = -0.29$) and FVC ($r_g = -0.32$), supporting its potential role as a biomarker of lung damage. HGF also showed strong positive genetic correlations with several proinflammatory cytokines—CCL3, CCL7, and CXCL10 (0.48 ≤ $r_g$ ≤ 0.56)—which could be explained by a previously suggested feedback mechanism where HGF contributes to counterregulating inflammation after the release of inflammatory mediators.<sup>34,37</sup>

Adult-onset and moderate-to-severe asthma were genetically correlated with adult BMI, childhood BMI, and childhood obesity ($r_g = 0.15$ to 0.26), and shared, respectively, 84% and 92% of their causal variants with adult BMI. This is in line with previous findings that obesity can increase asthma severity and impair the therapeutic response to inhaled corticosteroids.<sup>38-40</sup> Of note, our $r_g$ estimates between adult-onset asthma and adulthood and childhood BMI ($r_g = 0.26$ and 0.15, respectively) were consistent with the ones reported in two previous studies.<sup>41,42</sup> More surprisingly, we observed no significant genetic correlations between childhood-onset asthma and childhood obesity or BMI, even though these phenotypes have been considered risk factors for asthma in children.<sup>43</sup> This could be due to insufficient statistical power. Indeed, Han et al<sup>42</sup> found a positive genetic correlation between childhood BMI and childhood-onset asthma of the same order of magnitude as that estimated in our study ($r_g = 0.1, P = .02$ vs $r_g = 0.08, P = .1$ in our study).

Although neutrophil counts were not genetically correlated with any asthma subtype in LDSC analysis, MiXeR analysis revealed a substantial genetic overlap between neutrophils and all asthma subtypes. This discordance was explained by the fact that half of the shared variants had opposite effect directions on asthma and neutrophils, which could reflect the complex role of neutrophils that have both proinflammatory or anti-inflammatory activity, depending on the biological context.<sup>44</sup> Neutrophils play an important role in the defense response against microbes, and previous findings suggested a genetic link between neutrophil activity and the severity of rhinovirus infections.<sup>45</sup> Moreover, previous GWAS of asthma showed that the best candidate genes at many asthma-associated loci were involved in immune responses to viruses and bacteria.<sup>6</sup> Thus, the genetic overlap we observed between neutrophils and asthma could relate to the immune response against respiratory infections. Viral respiratory infections and perturbations of the airway microbiome have been shown to increase asthma risk,<sup>46,47</sup> and the integration of nasopharyngeal metagenome and respiratory virus measurements with asthma polygenic risk score successfully identified specific bronchiolitis endotypes at higher risk of childhood-onset asthma.<sup>48</sup> Integrating GWAS data on respiratory infections and/or airway microbiome would help us interpret our results further.

However, the lack of full GWAS summary statistics available for these phenotypes prevents performing such analyses. Moreover, neutrophils had the stronger $r_g$ with HGF among blood cells ($r_g = 0.29 \pm 0.06, P = 5.5 \times 10^{-7}$), in line with the suggested role for HGF in modulating neutrophil survival and neutrophilic inflammation.<sup>49</sup> It also strengthens the link between HGF and moderate-to-severe asthma, as airway neutrophilia is associated with lower lung function and asthma severity.<sup>2</sup>

The meta-analyses conducted as part of our harmonization process identified 41 new genome-wide significant loci for 22 asthma-related traits, annotated to 42 putative candidate genes, 7 of which were previously reported to be associated with asthma.<sup>14</sup> For example, we identified a new locus associated with the potent neutrophil chemoattractant CXCL8, whose index SNP (rs12309; chr17:38,175,462) was associated with the expression levels of GSDMB and ORMDL3 (Table I), two genes previously found to be associated with childhood-onset asthma.<sup>7</sup> Thus, we not only expanded the repertoire of genome-wide significant loci for asthma-related traits but also linked some of these traits to known asthma genes, thereby providing more insights on their role in asthma. ZNF33B, the candidate gene of the new 10q11 locus (index SNP rs61842935) associated with levels of eosinophil cationic protein, has never been reported to be associated with asthma. However, a sex-differential association was detected for this gene with atopy and eosinophil counts in asthmatic children.<sup>50</sup> Other candidate genes not reported associated with asthma had biological functions relevant to this disease. For instance, DGKD, at the new 2q37 locus (index SNP rs111741722) associated with the levels of macrophage inhibitory factor, modulates the proliferation of airway smooth muscle cells via ERK1/2 signaling, similar to macrophage inhibitory factor's role in asthma, suggesting it as a potential therapeutic target in asthma.<sup>51,52</sup> Four candidate genes (CEP192, KIAA1614, MDM1, and SPOUT1) had a functional link with the centrosome, a cellular component that helps organize microtubules and plays a key role in cell division. Among them, CEP192, in the 18p11 locus (index SNP rs7242404) associated with leukocyte counts, encodes a centrosomal protein that regulates the proliferation and contraction of airway smooth muscle cells by recruiting the polo-like protein kinase 1 to the centrosome, and thus might contribute to airway smooth muscle layer thickening in asthma.<sup>53</sup>

Our study has some limitations. The inclusion of only GWAS with full summary statistics available potentially omitted significant asthma and asthma-related trait loci—hence the need for the widest possible sharing of full GWAS results. Because there were substantial differences in sample size across the traits studied (ranging from 7,980 subjects for stem cell growth factor $\beta$ to 565,896 subjects for leukocyte count), we may have missed genetic correlations requiring larger sample sizes to be appropriately estimated, especially for molecular traits. However, our meta-analyses increased sample sizes for traits reported in more than one GWAS, thereby increasing the power of the LDSC and MiXeR analyses. Another limitation is the fact that we focused on European-ancestry GWAS. It would be interesting to replicate our analyses for non-European ancestries. However, GWASs of asthma-related traits in non-European populations are scarce, especially for molecular traits. When such studies exist and provide publicly available summary statistics, they tend to have small sample sizes. This highlights the need for more publicly accessible GWAS data focusing on asthma subtypes and related traits in non-European populations to conduct similar analyses in
diverse populations. Finally, all cell counts and molecular traits included in our study were measured in blood samples. Other tissues relevant to asthma, such as sputum or bronchoalveolar lavage fluid, might provide valuable information.

In conclusion, this study revealed shared genetic components between asthma subtypes and various biological and physiologic traits, highlighting distinct genetic architectures across asthma subtypes. Moderate-to-severe asthma showed strong genetic correlations with cytokines, notably HGF, a potential biomarker for lung injury and asthma severity. While IgE levels displayed significant genetic correlations with all asthma subtypes, the patterns of genetic overlap support a more prominent role of IgE in childhood-onset asthma. Further investigations are needed to elucidate the genetic heterogeneity of asthma. Future work includes identifying genetic clusters underlying asthma endotypes, leveraging our collection of harmonized GWAS summary statistics.

## DISCLOSURE STATEMENT

Supported by the Agence Nationale de la Recherche (ANR-20-CE36-0009 GenCAST) and a Université Paris Cité Doctoral Fellowship.

Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest.

We thank Jessica Sambourg, medical intern in the Group of Genomic Epidemiology of Multifactorial Diseases, for her contribution to data collection. We pay tribute to our late colleague, Hamida Mohamdi, engineer in the Group of Genomic Epidemiology of Multifactorial Diseases, who set up the computational pipeline used in this study.

### Key messages

* A harmonized database integrating hundreds of GWAS datasets on asthma subtypes and a wide range of biological and physiologic asthma-related traits, enriched by new meta-analyses, provided a unique resource for investigating their shared genetics.

* Asthma subtypes (asthma ever; and childhood-onset, adult-onset, and moderate-to-severe asthma) displayed both common and subtype-specific patterns of genetic overlap and correlation across biological and physiologic asthma-related traits.

* Moderate-to-severe asthma was characterized by a strong and significant positive genetic correlation with the levels of HGF, supporting its potential as a biomarker for asthma severity.

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