Multi-ancestry GWAS of age-related hearing loss identifies 140 loci and key cellular mechanisms

Domain: chemistry
Published: 2026-02-21
Editorial outcome: major_revision

Abstract
Age-related hearing loss is a prevalent and growing public health issue among the elderly. Here, we perform a multi-ancestry genome-wide association study comprising 456,613 cases and 1,053,834 controls, identifying 140 independent loci associated with age-related hearing loss, including 44 novel signals. We further fine-map 9 likely causal missense variants for age-related hearing loss and provide evidence of purifying selection for age-related hearing loss-associated variants. Notably, genetic risk for age-related hearing loss is strongly correlated with behavior traits such as neuroticism score and irritability. Integration of molecular phenotypes identifies 22 genes and 85 DNA methylation sites significantly associated with age-related hearing loss. Moreover, analyses incorporating spatial and single-cell transcriptomic identify the inner ear as a crucial site of age-related hearing loss, emphasizing the importance of hair cells, supporting cells, basal and root cells of the stria vascularis to its pathogenesis. Our study provides genetic and cellular insights into age-related hearing loss and advance our understanding of its genetics architecture.

Introduction
Age-related hearing loss (ARHL) is a type of sensorineural hearing loss that gradually develops with increasing age and is one of the most common health issues among the elderly. As the global aging populations continue to increase, the prevalence of ARHL is gradually increasing over years1,2. According to World Health Organization (WHO), hearing loss affects over 25% of the adults over the age 60, which severely impacts communications, social engagement and mental health3–5. Genetic factors are known to play a key role in ARHL, as evidenced by numerous family-based and population-level studies6–8. Genome-wide association studies (GWAS) have identified a growing number of loci associated with ARHL. For instance, the largest GWAS meta-analysis to date, performed by Trpchevska et al. in a European cohort of 723,266 individuals with ICD diagnoses and self-reported hearing loss, identified 48 associated loci and highlighted the biological relevance of the stria vascularis in ARHL pathogenesis9.

Despite these advances, much of the genetic basis of ARHL remains unexplained. This is partly due to the high phenotypic and genetic heterogeneity of ARHL, as well as difference in phenotypic definition10. Therefore, larger sample sizes for ARHL GWAS are required to achieve statistical power comparable to that of other complex traits. Moreover, most existing GWAS have predominantly focused on individuals of European descent, which limits the generalizability of findings across diverse populations. Cross-ancestry analyses are therefore essential to improve our understanding of shared genetic architecture between ancestries and uncover ancestry-specific association signals. In addition, the increasing availability of high-resolution single-cell and multi-omics datasets enables integrative analyses that can link GWAS signals to specific genes, regulatory elements, and disease-relevant cell types.

In this study, we performed a large-scale cross-ancestry GWAS meta-analysis of ARHL, combining data from 456,613 cases and 1,053,834 controls from European (EUR), East Asian (EAS), African (AFR) and Admixed American (AMR) populations. We systematically explored the genetic architecture of ARHL, identified novel susceptibility loci, fine-mapped putative causal variants, prioritized functional genes and cell types through integration with multi-omics QTL datasets and spatially resolved transcriptomic data.

Results
Cross ancestry meta-analysis identifies 44 new risk loci for ARHL
An overview of the study is shown in Fig.1. We performed a multi-ancestry GWAS meta-analysis by combining summary statistics from EUR, EAS, AFR and AMR populations (Methods, Supplementary Figure 1 and Supplementary Data 1). The primary analysis included 456,613 cases and 1,053,834 controls, identifying 140 independent loci associated with ARHL at genome-wide significance (P < 5×10-8), of which 44 were novel (Methods; Fig. 2a, Supplementary Figure 2 and Supplementary Data 2). Regional association plots revealed that several of these novel loci reside in regions with complex linkage disequilibrium (LD) patterns, and the functional genes underlying these observed associations remain largely unresolved (Supplementary Figure 3). To assess whether confounding factors such as population stratification exist, we applied LD score regression (LDSC)11. The LDSC regression intercept was 1.0305 (SE = 0.0104) together with λ1000 (1.0006), suggesting that the observed genomic inflation was attributable to polygenic architecture rather than confounding (Supplementary Figure 2). The estimated SNP-based heritability (ℎ2_SNP) on the liability scale was 0.0219 (SE = 0.0009), assuming a population prevalence of 0.1 (Methods). To replicate the identified 140 lead SNPs, we used an independent cohort from FinnGen12, comprising 39,620 cases and 397,865 controls. Of the 140 SNPs identified, 127 were available for replication analysis. Among them, 15 SNPs (11.81%) reached the genome-wide significance (P < 5×10-8), 37 SNPs (29.13%) reached the Bonferroni corrected significance (P < 0.00039), and 81 SNPs (63.78%) reached the nominal significance (P < 0.05). In addition to high replication rates, we observed high concordance in the effect sizes between the meta-analysis discovery and FinnGen replication cohort (r = 0.83, P = 1.38×10-33; Fig. 2b). Popcorn13 analysis further estimated a genetic correlation of 0.63 (se = 0.12) between two datasets, supporting a moderate but robust consistency in the underlying genetic architecture. For comparison with the largest previous ARHL GWAS by Trpchevska et al.9, 102 of the 140 identified SNPs overlapped between datasets. Of these, 45 SNPs (44.12%) reached genome-wide significance (P < 5×10-8), and all of them reached the Bonferroni corrected significance (P < 0.00049). To further assess the cross-ancestry consistency, we compared effect size estimates and effect allele frequency (EAF) between EUR population and each of the three non-EUR groups. The results showed moderately high cross-ancestry concordance in both effect sizes and EAF, indicating a largely shared genetic architecture of ARHL across diverse populations (Fig. 2c-e, Supplementary Figure 4).

Natural selection of ARHL-associated variants
To investigate evolutional constraints acting on identified ARHL-associated loci, we examined the relationship between minor allele frequency (MAF) and effect size. We observed a significant negative relationship, where variants with lower MAF tended to have larger effects on ARHL (Fig. 3a). This pattern is consistent with a negative selection model, where deleterious variants are kept at low frequencies in the population14,15. To formally assess this, we performed a SBayesS16 analysis, a method implemented in GCTB, which quantifies the relationship between effect size variance and MAF using the S parameter (Methods). Analyzing each chromosome separately, we observed a significantly negative genome-wide S estimates (𝑆̂ = -0.52, SE = 0.06), providing robust evidence for negative selection acting on rare deleterious variants (Fig. 3b). In addition to selection inference, SBayesS method also allowed us to estimate the ℎ2_SNP and polygenicity (π, i.e. the proportion of SNPs with non-zero effect) jointly with the S parameter on each chromosome. We found that the ℎ2_SNP of each chromosome was strongly correlated with chromosome length (r = 0.91; Fig. 3c), indicating a broadly distributed polygenic architecture for ARHL. The mean polygenicity estimate across chromosomes (i.e., π) was 0.62%, suggesting that ARHL is influenced by a large number of variants with small effects (Fig. 3d). Taken together, these results demonstrate that ARHL is shaped by widespread negative selection and high polygenicity. The evidence of purifying selection further suggests that future whole-genome sequencing (WGS) studies in larger and more diverse cohorts are likely to uncover additional rare, high-impact variants that contribute to ARHL susceptibility.

Shared genetic analyses highlight link between ARHL and behaviour traits
To investigate the genetic overlap between ARHL and other complex traits, we performed a genetic correlation analysis across 1,738 phenotypes in the UK Biobank (UKB)17. To avoid the potential bias due to sample overlap, we applied de-meta-analysis to our GWAS summary statistics to exclude the UKB cohort before the analysis (Methods, Supplementary Figure 5). This yielded a dataset comprising 342,295 cases and 730,385 controls (total N = 1,072,680). We observed a high cross-population genetic correlations of 0.98 (se=0.0026), indicating strong concordance in genetic architecture between the full meta-analysis and the de-meta-analysis results. Using BADGERS18, a polygenic risk scores-based approach for biobank-wide association scans, we identified 121 traits significantly associated with ARHL after Bonferroni correction (P < 2.88×10-5; Fig. 4a, Supplementary Data 3). As expected, hearing-related traits exhibited the strongest associations with ARHL (Fig. 4a). The most significant associations were observed with the diagnosis of hearing difficulty, including hearing difficulty with background noise and general hearing loss (P < 1×10-300), further validating the robustness of our GWAS results. In addition to auditory phenotypes, we observed a broad range of health and behavior-related phenotypes genetically linked to ARHL, suggesting its systemic and psychological effects. We then classified these 121 significantly associated traits into 10 categories based on their clinical or functional relevance (Supplementary Data 4). Among non-auditory categories, we observed that physical health and pain, as well as mental health and mood, showed stronger associations with ARHL than other categories. Notable examples include long-standing illness (P = 1.03×10-21), chest pain or discomfort (P =5.11×10-16), neuroticism score (P = 1.33×10-24) and irritability (P = 1.03×10-16). In social and family history category, loneliness (P = 7.52×10-8) exhibited the strongest association with ARHL. Furthermore, we also observed strong correlations between ARHL and insomnia (P = 1.97×10-11), frequency of depressed mood in last 2 weeks (P = 1.85×10-13) and past tobacco smoking (P = 2.94×10-6), consistent with previous findings9. To assess the robustness of genetic correlation findings using BADGERS with de-meta-analysis results, we selected 12 representative phenotypes based on either being the most significantly associated ARHL within each non-auditory category (as defined above) or representing previously established associations replicated in our study. The estimated genetic correlations between the ARHL meta-analysis and these phenotypes remained strong and statistically significant (Supplementary Data 5), further supporting for the reliability of the BADGERS findings.
Using the same set of 12 significant phenotypes, we applied GSMR19,20, a multi-SNP-based Mendelian randomization (MR) method, to explore potential causal relationships between ARHL and these non-auditory traits (Methods). Of these, 8 traits showed significant evidence of causal association with ARHL at a false discovery rate threshold (FDR < 0.05; Fig. 4b and Supplementary Data 6). We further assessed the robustness of GSMR associations using five additional MR approaches (Methods). Across the 8 traits identified as significant by GSMR, the associations remained significant and showed consistent effect directions across all other methods, reinforcing the reliability of these associations (Supplementary Figure 6 and Supplementary Data 7). These findings highlight the complex interplay between hearing loss and behavioral traits, suggesting potential shared biological pathways.

Genome-wide fine mapping identifies 9 missense variants associated with ARHL
To prioritize putative causal variants underlying ARHL-associated loci, we performed statistical fine-mapping using a state-of-art genome-wide fine-mapping (GWFM)21 approach, which incorporates the functional annotation and genome-wide LD information. We used GWFM because it has demonstrated superior performance in identifying causal variants than the regional-based fine-mapping approaches21. Prior to fine-mapping, we first used the GWFM to assess heritability enrichment across 96 functional annotations defined in Gazal et al.22. The analysis highlighted the top 20 enriched categories, with the fold enrichment values ranging from 3.0 to 16.8 (Supplementary Figure 7). Notably, the nonsynonymous annotation showed the highest per-SNP heritability enrichment. We further observed that nonsynonymous annotation was enriched not only for higher proportion of putative causal variants but also for variants with larger effect sizes (Supplementary Figure 8), underscoring its biological importance in ARHL pathogenesis. Applying a posterior inclusion probability (PIP) threshold of 0.9, we identified 1,108 SNPs from 165 credible sets, collectively explaining 7.6% of the total ℎ2_SNP (Supplementary Data 8). Among these, 22 SNPs showed strong causal evidence (PIP > 0.9) for ARHL, including 9 missense variants (Supplementary Figure 9 and Supplementary Data 9, 10). Of these 9 missense variants, 4 are located in genes previously validated in herediatary hearing loss, according to the Hereditary Hearing Loss Homepage (https://hereditaryhearingloss.org/): LOXHD1 (DFNB77, [MIM: 613072]), CLRN2 (DFNB117, [MIM: 618988]), TMPRSS3 (DFNB8/10, [MIM: 605511]) and EYA4 (DFNA10, [MIM: 603550]). In addition, KLHDC7B and COL9A3 have been linked to auditory function in mice, as reported by the International Mouse Phenotyping Consortium (IMPC, https://www.mousephenotype.org/about-impc/). To evaluate the robustness of our fine-mapping results, we re-analyzed all loci using the SuSiE23,24 with the same LD reference. In the diagnostic analysis, SuSiE estimated a mean lambda of 0.006 across loci (Supplementary Figure 10), indicating good concordance between the LD reference and our ARHL GWAS data. Further fine-mapping analysis with SuSiE identified 9 SNPs with PIP > 0.9, of which 7 overlapped with the 22 SNPs identified by our GWFM analysis. Among the 22 fine-mapped variants identified by GWFM, 7 achieved a SuSiE PIP > 0.9, and 50% had PIPs > 0.5 and 87% had PIPs > 0.1 (Supplementary Data 11), confirming the consistency and reliability of our findings. These fine-mapping results highlight the contribution of missense variants to ARHL susceptibility and provide a refined set of high-confidence candidate variants and genes for future functional validation.

Prioritization of functional genes and DNA methylations
To prioritize functional genes for these identified GWAS loci in non-coding regions, we performed summary data-based Mendelian randomization (SMR)25 analysis using the top-associated expression quantitative trait locus (eQTL) as an instrument variable to test for associations between gene expression level of each gene and ARHL risk (Methods). We used the GWAS summary data from our meta-analysis and eQTL summary data from eQTLGen consortium26 (n = 14,115) for the SMR analysis. In total, we identified 47 genes at a Bonferroni corrected significance level (P < 3.20×10-6). To filter out the SMR associations caused by linkage (i.e., the causal variants for gene expression and the causal variants affecting ARHL risk were in linkage disequilibrium), we further performed the HEterogeneity InDependent Instruments (HEIDI) analysis for all significant SMR associations (Methods). Genes not rejected by HEIDI test (P > 0.01) are likely influenced by shared causal variants affecting both gene expression and ARHL. Of the 47 genes that passed SMR test, 22 were not rejected by the HEIDI test (P > 0.01; Supplementary Figure 11 and Supplementary Data 12), implying a pleiotropic association. To further prioritize the regulatory DNA methylation (DNAm) sites associated with ARHL risk, we performed the SMR and HEIDI analyses using methylation quantitative trait locus (mQTL) data from Brisbane Systems Genetics Study and Lothian Birth Cohorts27 (n = 1,980) to identify DNAm sites associated with ARHL through pleiotropy at a shared causal variant. In total, we identified 85 DNAm sites associated with ARHL at genome-wide significance level (P < 5.37×10-7) and P_HEIDI > 0.01 (Supplementary Figure 12 and Supplementary Data 13).
To infer the plausible genetic regulatory mechanisms, we integrated these 85 ARHL-associated DNAm sites with 22 ARHL-associated genes identified through SMR and HEIDI test above. We identified 44 DNAm site-gene pairs showing pleiotropic associations across 9 genes (P_SMR < 5.38×10-4, P_HEIDI > 0.01; Supplementary Data 14). The identification of these putative functional genes and their regulatory elements provide opportunities to infer the mechanism of genetic regulation at a GWAS locus. A notable example is the 17p13.1 locus, where three genes (ACADVL, DVL2 and ELP5) showed consistent association signals across data from GWAS, eQTL and mQTL studies, implying a plausible biological pathway. In this region, the ARHL-associated DNAm probes cg10691912, cg00072720, cg12805420 and cg11941546 were simultaneously associated with the expression level of all three genes, suggesting a potential co-regulation via DNAm (Supplementary Figure 13). Among these three genes, ACADVL emerged as strong a candidate gene with following evidence. First, the expression levels of ACADVL were positively associated with ARHL risk. Second, the SNP-association signals were identified for the DNAm probe cg19466160, and the methylation level of this probe was significantly associated with gene ACADVL (Fig. 5, Supplementary Data 13 and 14). Third, the DNAm probe cg19466160 is located near the enhancer regions of gene ACADVL according to the chromatin state annotations from the Roadmap Epigenomics Mapping Consortium (REMC) reference samples28. Together, these findings support a plausible regulatory mechanism in which the genetic variant alters the DNAm level at cg19466160, which in turn down-regulates the expression of ACADVL gene and therefore decreasing the risk of ARHL.

Tissue and cell type-specific enrichment of ARHL heritability
To identify biologically relevant tissue for ARHL, we applied gsMap29 method to quantify the spatial tissue and cell type-specific SNP heritability enrichment (Methods). Due to the lack of publicly available spatial transcriptomics (ST) data from human embryos, we used mouse embryo ST data from MOSTA30 database as a proxy (Fig. 6a, Supplementary Data 15). As expected, the inner ear region showed the strongest heritability enrichment for ARHL (Cauchy-combination P value = 3.97×10-6, Fig. 6b, Supplementary Data 16) , validating its central role in ARHL etiology.
To refine these tissue-level findings at the cellular level, we leveraged the single-cell RNA-seq (scRNA-seq) data from the mouse cochlear by Jean et al.31, which profiled 85,167 cells across 36 distinct cochlear cell types. Using this dataset, we applied three complementary approaches (i.e., LDSC-SEG, MAGMA and scDRS) to evaluate SNP heritability enrichment and to quantify disease relevance across cochlear cell types32–34 (Methods). Across these analyses, hair cells and supporting cells consistently showed the strongest enrichment, suggesting they are the primary contributors to the genetic architecture of ARHL (Fig. 6c; Supplementary Data 17). Moreover, significant enrichment signals were also observed in the basal and root cell compartments of the stria vascularis in both MAGMA and scDRS analyses, highlighting a potential complementary role of these non-sensory epithelial cells in hearing loss pathogenesis. To further validate these results, we repeated the analysis with four additional mouse cochlear scRNA-seq datasets used by Eshel et al.35–38, which spanned a broad age range from postnatal day 15 (P15) to 15 months. Similar enrichment patterns were observed in these datasets (Supplementary Figure 14 and Supplementary Data 18). These findings reconcile the apparent discrepancy between Eshel et al.37, who emphasized hair cells and supporting cells, and Trpchevska et al.9, who highlighted basal and root cells of the stria vascularis, by revealing their shared contribution to hearing loss. Collectively, these results provide robust evidence that ARHL heritability is significantly enriched in hair cells, supporting cells, and basal and root cells of the stria vascularis, reinforcing their collective roles in hearing loss pathogenesis.

Methods
Ethics
This study was approved by the Medical Ethical Committee of West China Hospital, Sichuan University [approval number: 2021 Audit (190)] and conducted in compliance with the Declaration of Helsinki. The meta-analysis was performed using only publicly available or previously published GWAS summary statistics and involved no new recruitment or handling of individual-level data. Ethics approval and informed consent were obtained for all original GWAS studies by their respective institutional review boards or ethics committees.

Data used in this study
The GWAS summary statistic data for ARHL analyzed in this study were obtained from four primary resources: the GWAS meta-analysis by Trpchevska et al.9, the GWAS meta-analysis by De Angelis et al., the VA Million Veteran Program Consortium (MVP)53 and Biobank Japan (BBJ)54. The meta-analysis dataset from Trpchevska et al.9 was the largest ARHL GWAS meta-analysis in European ancestry to date, comprising 147,997 cases and 575,269 controls from 17 population-based cohorts. The meta-analysis dataset from De Angelis et al. leveraging the UK Biobank (UKB), the Nurses’ Health Studies (I and II) and the Health Professionals Follow-up Study. Given that the GWAS summary statistics from Trpchevska et al.9 already incorporated UKB participants, we thus removed UKB-derived signals from the De Angelis et al. dataset through de-meta-analysis prior to downstream analyses. The de-meta-analysis was performed using the following formulas:
𝛽̂_DE-META = (𝛽̂_META / SE_META^2 - 𝛽̂_c / SE_c^2) / (1 / SE_META^2 - 1 / SE_c^2) (1)
SE_DE-META = 1 / sqrt(1 / SE_META^2 - 1 / SE_c^2) (2)
where META, c and DE-META refers to the GWAS summary statistic from cross-ancestry meta-analysis, the cohort to be excluded (i.e., UKB) and de-meta-analysis, respectively. The P value for each variant after de-meta-analysis was obtained using 2Φ(-|𝛽̂_DE-META| / SE_DE-META). After this adjustment, the dataset comprised 18,018 cases and 18,024 controls. The MVP GWAS summary data are ancestry-stratified, including EUR (229,580 cases and 176,720 controls), AMR (23,888 cases and 28,460 controls), EAS (2,671 cases and 3,302 controls) and AFR (31,058 cases and 76,733 controls). The GWAS summary data from BBJ included 3,400 cases and 175,326 controls of East Asian ancestry. For replication, we used the GWAS summary statistics from the FinnGen12, which includes 39,620 cases and 397,865 controls of EUR descent. The ARHL phenotype was defined either by ICD9/10 diagnoses of hearing loss (as applied in FinnGen, BBJ and MVP) or by self-reported hearing loss (De Angelis’s GWAS and all cohorts in Trpchevska’s GWAS except FinnGen). Self-reported hearing loss in included given prior evidence demonstrating that serves as a reliable proxy for formal hearing assessment55.
Comprehensive details on phenotype definitions, sequencing platforms, quality-control and filtering criteria, case-control sample sizes and the analytical approaches used for GWAS analysis are provided in Supplementary Data 1.

Cross-ancestry GWAS meta-analysis for ARHL
Prior to meta-analysis, we conducted standard quality control on all GWAS summary statistics. All datasets were harmonized to the hg19 reference genome build to ensure consistency of genomic coordinates and alleles. Duplicated SNPs and variants with inconsistent or mismatched alleles across cohort were excluded. We further filter out SNPs with a minor allele frequency (MAF) < 0.01. We then conducted a two-stage meta-analysis strategy to combine GWAS summary statistics. In the first stage, we performed ancestry-specific meta-analyses within EUR (totaling 395,596 cases and 770,013 controls) and EAS (totaling 6,071 cases and 178,628 controls). In the second stage, we performed a multi ancestry meta-analysis by combining results from EUR, EAS, AMR and AFR. This leads to a final GWAS summary statistic including 1,510,447 individuals for downstream analysis. All meta-analyses were performed using a fixed-effect inverse-variance-weighted (IVW) model implemented in METAL (the version released on 2011-03-25)56. The meta-analysis performed by METAL is based on the reported effect size and standard error for each SNP. Heterogeneity across studies was assessed using Cochran’s Q test and the I2 statistic.
To assess potential inflation due to population stratification or other confounding factors, we applied univariate LDSC11. The LDSC regression intercept would be close to one when the observed test statistic inflation is attributable to polygenicity rather than bias. In addition, we calculated λ1000 to further assess inflation standardized to a sample size of 1,000 cases and 1,000 controls. SNP-based heritability was then estimate on the liability scale using LDSC, assuming a population prevalence of 10% for ARHL. The genotype data of 1000 Genome Phase 357 European population was used as the LD reference.
To identify the independent genome-wide significant loci, we performed LD clumping using PLINK v1.9058 with setting clumping r2 0.05, genome-wide significant P value threshold as 5×10-8 and the window size as 1 Mb around each index SNP. A locus was considered novel if it satisfied either of the following criteria relative to previously reported lead variants from large-scale ARHL GWAS (i.e., the variants reported by Ivarsdottir et al., Kalra et al., Praveen et al., De Angelis et al., Wells et al. and Trpchevska et al. 50,52,59–61): it located > 500 kb away from any previously reported variants with r2 < 0.1; or if it was within 500 kb of known loci but met a stricter criterion of LD independence (r² < 0.05).

Estimating the genetic architecture for ARHL
To investigate the genetic architecture of ARHL, we first examined the relationship between MAF and effect size of our genome-wide significant lead SNPs. A negative correlation between MAF and effect size may indicate the action of purifying selection on deleterious variants. To obtain a more comprehensive insight into the genetic architecture of ARHL, we applied the SBayesS16 model, implemented in GCTB software, which estimates the joint distribution of SNP effect size and MAF, enabling inference of parameters of polygenicity, SNP-based heritability and the selection coefficient (S). For SBayesS analysis, we used the precomputed eigen-decomposition data provided by GCTB for genotype imputation and quality control. We further incorporated functional annotation information from Gazal et al.22 and shrunk sparse LD matrices, to improve the estimation of polygenicity and selection. Analyses were conducted at the chromosome level, allowing for comparison of genetic architecture across the genome.

Genetic correlation analysis with other traits
To explore the shared genetic architecture between ARHL and a broad range of complex traits, we applied BADGERS18, a PRS-based framework for biobank-wide association analysis. BADGERS estimates trait associations by leveraging GWAS summary statistics and a reference panel of PRS weights across multiple phenotypes. According to BADGERS, the reference panel comprises 1,738 traits from the UKB17, selected on the basis of nominally significant SNP-based heritability (P < 0.05). This selection ensures that only traits with sufficient heritability were retained for analysis. In this analysis, we evaluated the associations between ARHL and 1738 heritable traits from the UKB17, which served as the reference trait panel for polygenic weights and covariance structure. To avoid potential inflation due to sample overlap—since our primary ARHL GWAS includes UK Biobank participants—we first removed UKB-derived signals from GWAS summary statistics through de-meta-analysis. We further assessed the concordance of genetic architecture between GWAS summary statistics from our meta-analysis and de-meta-analysis using Popcorn13. Finally, these de-meta-analyzed summary statistics were then used as input for BADGERS.
We further employed GSMR19,20 method to investigate the potential causal associations between ARHL and 12 non-auditory phenotypes prioritized by BADGERS. GSMR uses generalized least squares to estimate causal effects, allowing multiple genome-wide significant SNPs as instrumental variables, thereby increasing statistical power and robustness against weak instrument bias. The genetic instrument variants were selected based on a clumping analysis using GSMR (i.e., P = 5×10-8 and LD r2 < 0.05 within a 1Mb windows). We used the 1000 Genomes Phase3 EUR population as the LD reference data for GSMR analysis. To further control for horizontal pleiotropy, we applied the HEIDI-outlier test (Heterogeneity In Dependent Instruments) with a significance threshold of P > 0.01 to exclude SNPs with potential horizontal pleiotropic effects from the analysis. We further validate the robustness of GSMR findings by applying five additional Mendelian randomization (MR) methods: inverse-variance weighting (IVW), pleiotropy residual sum and outlier (PRESSO), MR-Robust, Simple Median and robust adjusted profile score (RAPS) 62–65.

Genome-wide fine mapping analysis
We performed genome-wide fine-mapping (GWFM) analysis using a Bayesian hierarchical mixture model implemented in GCTB21. In contrast to traditional region-based fine-mapping approaches (e.g., FINEMAP), GWFM leverages the SBayesRC framework66, a hierarchical multi-component mixture model that jointly integrates the genome-wide functional annotations and LD to fine-map the likely causal variants. To capture genome-wide LD patterns, we used the precomputed eigen-decomposition LD reference data provided by GCTB. Functional annotations were incorporated from the comprehensive dataset published by Gazal et al.22, which includes 96 genomic features (e.g., coding, conserved region) relevant to complex trait architecture. Fine-mapping was performed across the entire genome, and variants with a posterior inclusion probability (PIP) ≥ 0.9 were considered as likely causal. Additionally, credible sets were defined based on ranked PIPs to identify minimal SNP subsets accounting for 90% of the posterior probability at each signal. In addition, we applied Sum of Single Effects (SuSiE)23,24 framework to evaluate the robustness of GWFM findings. By employing the same LD reference, we ensured methodological consistency across analyses, allowing us to directly assess the concordance and stability of the identified signals.

Summary-data-based Mendelian randomization analysis
To identify gene expression or DNA methylation levels associated with ARHL, we performed SMR and HEIDI25 analyses, which integrate GWAS summary statistics for ARHL with molecular QTL data to distinguish pleiotropic associations from those driven by linkage25. We first performed SMR analysis to test the association between gene expression level and ARHL, using the top cis-eQTL SNP (i.e. within 2 Mb of gene expression probe and P < 5×10-8) of gene as an instrument variable. The significant SMR associations can be caused by linkage, where two variants that one affecting the complex trait and another affecting gene expression level were in LD. We therefore applied the HEIDI test to distinguish pleiotropy (e.g. a causal variant affecting both trait and gene expression level) from the linkage using multiple SNP in cis region. Similarly, we applied the SMR and HEIDI analyses to test for association between DNAm and ARHL, and between ARHL-associated genes and ARHL-associated DNAm. In these SMR analyses, the mQTL summary data were collected from the study of Wu et al.67, which perform the meta-analysis of Brisbane Systems Genetics Study and Lothian Birth Cohorts27 (peripheral blood, n = 1,980) and the eQTL summary data were obtained from the eQTLGen Consortium26 (whole blood, n = 14,115). For visualization, we used OPERA27 to generate the locus plot that integrated omics data, including co-localization patterns between genetic variants, molecular traits, and ARHL.

Spatial distribution of ARHL related cells
To map the spatial distribution of cells associated with ARHL, we applied gsMap29, a method based on the stratified LDSC, to integrate GWAS summary statistics with spatial transcriptomic (ST) data. For this analysis, we used mouse embryonic ST data from embryonic day 16.5 (E16.5) embryos, covering 18 organs obtained from the MOSTA database30. Because the ST data was derived from mouse tissue, the analysis was limited to homologous genes between human and mouse to ensure biological relevance. Before stratified LDSC (S-LDSC), gene specific scores (GSS) of each spot were assigned to nearby SNPs (located within a 50 kb window of the gene’s transcription start site). These spot-specific SNP annotations were used to compute stratified LD scores based on a reference LD panel. We then applied S-LDSC to estimate the association between each spatial spot and ARHL risk. To assess the enrichment of ARHL heritability in specific tissues or cell-type regions, we used Cauchy-combination test to aggregate P values across spatial spots corresponding to each annotated tissue or cell type.

Stratified LD score regression analysis for tissue, cell type specificity
To investigate which tissue and cell types contributed to ARHL, we applied LDSC-SEG, MEGMA and scDRS32–34. These approach estimates SNP heritability enrichment in genes with specific expression patterns across tissue or cell types. As the GTEx resource lacks expression data from human cochlear tissue, we instead utilized publicly available single-cell RNA sequencing (scRNA-seq) dataset from the mouse cochlear generated by Jean et al.31, which includes a total of 85,167 cells from 36 cell types. In addition, we incorporated four publicly available scRNA-seq datasets used by Eshel et al, encompassing samples across a broad age range (postnatal day 15 (P15) to 15 months)35–38. The details of scRNA-seq datasets were as follows: expression matrix and cell annotations from Iyer et al. (P15), Eshel et al. (3 months) and Sun et al. (1, 2, 5, 12 and 15 months) were obtained from the gEAR platform68. For the dataset of Xu et al. (P28)36, we downloaded the expression matrix from GEO (GSE202920) and conducted scRNA-seq analysis according to the procedures described in the original publication, using the provided code. For comparison with the study of Trpchevska et al., we incorporated two mouse cochlear scRNA-seq datasets from Milon et al. and Ranum et al.45,46. While the former captured the spiral ganglion region and the lateral wall/stria vascularis but lack hair cells and Deiters' cells, the latter was limited to IHCs, OHCs, and Deiters’ cells.
We processed and calculated gene expression specificity following the procedures described by Trpchevska et al. We first excluded genes that were not expressed in any cell types. Next, we retained only the 1:1 orthologous gene between mouse and human, identified using biomaRt69,70. Gene expression was then normalized to transcripts per million (TPM), scaled to 1 TPM per cell type. Finally, gene expression specificity was calculated per gene per cell type as:
Specificity_g,c = TPM_g,c / ∑n_i=1 TPM_g,i (3)
where g denotes the gene and c denotes the cell type. For each cell type, we selected top 10% genes that ranked by the quality-related expression values for each cell type and applied ±100 kb window around gene’s transcription start site (TSS) to obtain a genome annotation. We then ran LDSC-SEG and MAGMA using these data to assess SNP heritability enrichment across cochlear cell types. In parallel, we reformatted the scRNA-seq data into the input required by scDRS and computed disease relevance score per cell, which were then aggregated to the cell type-level.

Statistics & Reproducibility
No statistical method was used to predetermine sample size and no data were excluded from the analyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review\nShi et al. present a multi-ancestry GWAS meta-analysis of age-related hearing loss (ARHL) in 1.5 million individuals,\nidentifying 98 independent risk loci, including 37 novel signals. The study integrates fine-mapping, genetic correlation,\nMendelian randomization, and single-cell transcriptomic analyses to prioritize candidate genes, regulatory elements, and\nimplicated cell types. The authors emphasize the identification of novel risk loci, the demonstration of negative selection\nacting on ARHL-associated variants, and the expansion of implicated cell types beyond previous studies.\nWhile the manuscript is comprehensive and employs state-of-the-art statistical approaches in GWAS, many of its findings\noverlap with the prior study by Trpchevska et al. (e.g., genetic correlations, missense variants), with incremental advances\nprimarily being in the inclusion of additional ancestry groups, DNA methylation mapping, and cell-type enrichment analyses\nthat identify new cell types involved in hearing loss.\nWhile Trpchevska et al. demonstrated that cell in the stria vascularis are enriched with GWAS signals for hearing loss, Eshel\net al. (https://doi.org/10.1016/j.ajhg.2024.01.008) suggest this is not the case, using other existing GWAS and scRNAseq\nressources. These contradicting findings are not solved in the current study, but the authors have the possibility of doing so.\nMajor Comments\n1. The majority of loci and functional conclusions mirror those of Trpchevska et al. Of the 98 loci, many were previously\nreported, and the manuscript’s main novelty lies in the identification of 37 new loci and refined fine-mapping. The meta-\nanalysis includes Trpchevska et al., MVP, and Biobank Japan, but omits other large GWAS such as those by De Angelis\n(https://doi.org/10.5281/zenodo.7897038), Ivarsdottir (http://www.decode.com/summarydata), and Praveen (mainly UKBB\ndriven), which are publicly available and would provide additional power and diversity (please see\nhttps://doi.org/10.1124/pharmrev.124.001195 for some insights on the 4 studies). The authors do use de-meta-analysis to\nremove UK Biobank from their summary statistics before genetic correlation analyses, but should apply the same strategy to\nenable inclusion of the studies from De Angelis et al. and Ivarsdottir et al. in the main meta-analysis to maximize discovery\nand generalizability. Authors should include details on phenotype harmonization, ancestry representation, and technical\ncompatibility (as evidenced in https://doi.org/10.1124/pharmrev.124.001195).\n2. The use of SBayesRC for polygenic risk estimation and fine-mapping is appropriate and reflects current best practices. The\napplication of SMR and HEIDI to integrate GWAS and eQTL/mQTL data is well executed but the tissue used (primarily\nblood) is not suitable for extrapolating on cochlear mechanisms. Despite the fact authors acknowledge this limitation, blood\neQTL/mQTL analysis does not yield relevant findings. Authors should remove these results, and should mention the\nlimitations of non-existing human cochlear eQTL resources in the discussion.\n3. The LDSC-SEG approach is informative but incomplete. The authors expand the list of implicated cochlear cell types\ncompared to Trpchevska et al. However, the interpretation of enrichment in the stria vascularis versus sensory epithelium\nremains unclear. Recent work by Eshel et al. argues that cochlear epithelial cells, not the stria vascularis, are the primary\nsite of GWAS signal enrichment. There, Eshel et al. use a different scRNAseq resource and different GWAS studies but non\nsystematically. Thus, the GWAS and scRNAseq dataset matter. Here is the opportunity to seal this debate with this 1.5\nmillion sample size. First, authors should use both LDSC and Magma as Trpchevska et al., but also scDRS as used by\nEshel et al. Only those cell types corroborated by at least two of these methods would be valid signals. Second, for\nreplication purposes, authors should use the scRNAseq datasets that Trpchevska et al. have used, as well as that from\nEshel et al. Since some of these signals were borderline significant, the new GWAS megasample, tested against the 3\nscRNAseq datasets including Jean et al., would validate or not the suggestions from the past studies. Only adult mouse data\nshould be used for comparison sake between the 3 scRNAseq studies. Ensure the method used is the exact same\nTrpchevska has reported, for sake of replication. The spatial transcriptomics at E16.5 from the mouse embryo is not relevant\nto humans with ARHL when compared to GTeX – please remove, it does not add significantly novel information.\n4. Of important note, the manuscript relies on the Jean et al. single-cell cochlear dataset, which, while comprehensive, uses\n10X Genomics technology with limited sequencing depth. This restricts the ability to resolve rare or transcriptionally subtle\ncell types, such as subtypes of Type 1 auditory neurons, which have been missed by Jean et al. There were also batch to\nbatch differences in the Jean et al. study that are indicative of weaknesses in the validity of their sequencing. While\nTrpchevska et al. relied on the scRNAseq data from Millon et al., which also was performed on 10X genomics, the output\nsuccessfully identified the 3 subtypes of Type 1 auditory neurons. This is why the corroboration, also through 3 distinct\nscRNAseq datasets, is needed. The authors should discuss the results from this analysis and the possibility that technical or\nreference biases that may influence the observed enrichments (which is why the use of the former datasets for replication\nand validation is needed).\n5. Discuss the value of finding missense variations (found in familial cases, thus being rare), and identified here as common\nvariants? Are these variants less penetrant in the studied population vs familial cases from more isolated consanguineous\npopulations? What is the importance of such findings in a GWAS?\n6. Authors should include sex stratified analyses as supplemental, with summary stats for the two sexes as well.\n7. The absence of X-chromosome analysis should be discussed in the limitations – what would be expected from this\nanalysis?\nMinor comments:\nThe manuscript would benefit from more detailed descriptions of quality control, phenotype harmonization, and ancestry-\nspecific analyses.\nFor loci claimed as novel, the authors should provide a comprehensive comparison with all recent GWAS, including those\nnot included in the meta-analysis, to avoid overstatement of novelty.\nThe link to the summary statistics from the present study should be made available to the reviewers and opened to public\nonce publication is accepted. https://zenodo.org/records is not sufficient for us to access the summary stats from the present\nmeta-analysis. https://umgear.org is too vague – please specify the full link or provide more indications to access this dataset\nwithin umgear. Access to the codes: https://github.com/Crazzy-Rabbit/project_hearing_loss/ was not possible. Please allow\nthe reviewer to verify the codes.\nCorrected FDR values for significance in Figure 6 seems wrong. Please check. Shouldn’t it be: p < 0,00139 ?\nI’d recommend one supplemental excel file with each supplementary table on separate sheets.\nThe use of AI for text and grammar should be acknowledged in the manuscript.", "reviewer_2": "In this study, Shi and colleagues conducted the largest cross-ancestry genome-wide association study (GWAS) to date on\nage-related hearing loss (ARHL), and applied a range of cutting-edged quantitative genetics approaches for post-GWAS\nanalyses. There are some interesting results that will be of interest to the readership. However, I have a few comments\nbelow that I think could enhance the quality of the manuscript.\n1. Please include Cochran’s Q statistic or I2 value for each SNP to quantify the heterogeneity in the cross-ancestry meta-\nanalysis GWAS.\n2. In addition to comparing effect sizes between EUR and non-EUR populations for the highlighted SNPs, it is also valuable\nto examine their MAF across different populations.\n3. Page #6: What is the sample size of the de-meta-analysed GWAS after excluding the UKB cohort? The substantial\nreduction in power may make it less representative of the original cross-ancestry meta-analysis, potentially introducing bias\ninto the BADGERS analysis. I recommend using Popcorn to estimate cross-population genetic correlations as an additional\nsensitivity check for BADGERS, which can account for potential sample overlap during estimation.\n4. To the best of my knowledge, GSMR cannot reliably distinguish between correlated and uncorrelated pleiotropy from true\ncausality, although it uses the HEIDI-outlier method to exclude instrumental SNPs that are likely pleiotropic to some extent. I\nrecommend conducting additional sensitivity analyses (e.g., two sample MR, CAUSE, LHC-MR, etc) to re-assess the\nsignificant GSMR findings and help identify potential confounding effects due to pleiotropy.\n5. For the results generated by S-LDSC, were they adjusted using the baseline model? If not, I recommend re-running the\nanalysis with baseline model adjustment.\nMinor:\n6. Page #4: Please state how many independent SNP loci from the study are replicated compared to Trpchevska et al.\n7. Page #4: Please provide the genetic correlation between the cross-ancestry meta-analysis GWAS and the FinnGen\nreplication GWAS.\n8. Page #6: Please remove the extra space in “Fig.4 a”\n9. Some quantitative genetics methods (e.g., SMR, S-LDSC) appear to have limited ability to account for LD patterns across\ndifferent populations, which may represent a limitation of these analyses.", "reviewer_3": "This manuscript presents a large-scale, multi-ancestry GWAS meta-analysis of age-related hearing loss incorporating ~1.5\nmillion individuals. The authors identify 98 genome-wide significant loci (37 novel), perform fine-mapping and functional\nfollow-up using multi-omics data, and explore cell-type heritability enrichment using spatial and single-cell transcriptomic\ndata. The scale, multi-ancestry approach to GWAS, and integrative analyses are well thought-out and the paper is well-\nwritten. However, there are methodological concerns that should be addressed.\nMajor Comments\n1. The manuscript defines a locus as novel if it is not previously reported and is located >500 kb from any known lead SNP.\nHowever, this approach risks both false positive (loci >500kb from previous hit that are actually in strong long-range LD) and\nfalse negatives (loci within 500kb but not in LD with known hits may be genuinely novel). It would strengthen the authors’\nargument to account for LD structure in additional to physical distance.\n2. The replication sample is FinnGen. This is a genetically isolated European population. Given the stated emphasis on\nmulti-ancestry discovery in this project, the use of a European-only replication cohort raises concerns about generalizability.\nAdditionally, many downstream analyses use 1000 Genomes 3 European LD reference panels (e.g., LDSC, GSMR). This\nintroduces potential biases when applied to the non-European ancestries represented in the discovery meta-analysis.\n3. The authors examine genetic correlations with 1,738 traits in UK Biobank but do not explain how these traits were\nselected. Were they filtered for heritability, relevance, or redundancy?\n4. The manuscript reports fine-mapping results using GWFM showing 1,042 variants in 157 credible sets and identifies only\nshow fine-mapping diagnostics and compare to another rigorously benchmarked fine-mapping method like Susie to help\ncontextualize performance.\n5. The authors use ST data from mouse embryos to infer spatial enrichment of ARHL heritability, but ARHL is a late-onset\ndisease. Further justification of this embryonic data as a suitable proxy for this human condition is needed.\nMinor Comments\n1. The LDSC intercept is reported (1.0095) and it suggests minimal inflation due to confounding. However, given the\nextremely large sample size of the analysis, it would be helpful to also report lambda 1000 (the genomic inflation factor\nstandardized to 1000 cases/controls) to further evaluate inflation relative to scale.\n2. The link to the GitHub code for this project is broken.\n3. In supplemental table 5, there is a row with NaN values (noisy workplace). What does this indicate?\n4. Please define beta hat in Figure 4b."}, "author_response": "Response to Reviewers’ Comments\nWe thank the three reviewers for their time spent on reading our manuscript and for\ntheir constructive comments, which have helped us to improve the manuscript. We have\nresponded to all the reviewers’ comments point-by-point below (in blue) and have\nhighlighted the relevant changes (in yellow) in the manuscript files.\nReviewer #1:\nShi et al. present a multi-ancestry GWAS meta-analysis of age-related hearing loss\n(ARHL) in 1.5 million individuals, identifying 98 independent risk loci, including 37\nnovel signals. The study integrates fine-mapping, genetic correlation, Mendelian\nrandomization, and single-cell transcriptomic analyses to prioritize candidate genes,\nregulatory elements, and implicated cell types. The authors emphasize the identification\nof novel risk loci, the demonstration of negative selection acting on ARHL-associated\nvariants, and the expansion of implicated cell types beyond previous studies.\nWhile the manuscript is comprehensive and employs state-of-the-art statistical\napproaches in GWAS, many of its findings overlap with the prior study by Trpchevska et\nal. (e.g., genetic correlations, missense variants), with incremental advances primarily\nbeing in the inclusion of additional ancestry groups, DNA methylation mapping, and cell-\ntype enrichment analyses that identify new cell types involved in hearing loss.\nWhile Trpchevska et al. demonstrated that cell in the stria vascularis are enriched with\nGWAS signals for hearing loss, Eshel et al. (https://doi.org/10.1016/j.ajhg.2024.01.008)\nsuggest this is not the case, using other existing GWAS and scRNAseq resources. These\ncontradicting findings are not solved in the current study, but the authors have the\npossibility of doing so.\nMajor Comments\n1. The majority of loci and functional conclusions mirror those of Trpchevska et al. Of\nthe 98 loci, many were previously reported, and the manuscript’s main novelty lies in\nthe identification of 37 new loci and refined fine-mapping. The meta-analysis includes\nTrpchevska et al., MVP, and Biobank Japan, but omits other large GWAS such as those by\nDe Angelis (https://doi.org/10.5281/zenodo.7897038), Ivarsdottir\n(http://www.decode.com/summarydata), and Praveen (mainly UKBB driven), which\nare publicly available and would provide additional power and diversity (please see\nhttps://doi.org/10.1124/pharmrev.124.001195 for some insights on the 4 studies). The\nauthors do use de-meta-analysis to remove UK Biobank from their summary statistics\nbefore genetic correlation analyses, but should apply the same strategy to enable\ninclusion of the studies from De Angelis et al. and Ivarsdottir et al. in the main meta-\nanalysis to maximize discovery and generalizability. Authors should include details on\nphenotype harmonization, ancestry representation and technical compatibility (as\nevidenced in https://doi.org/10.1124/pharmrev.124.001195).\nRe: We thank the reviewer for these constructive comments. Compared with the study\nby Trpchevska et al.1, our work provides three key advances. First, whereas Trpchevska\net al. focused only on European cohorts, we performed a cross-ancestry meta-analysis,\nenabling the assessment of cross-population similarities and improving the\ngeneralizability of findings. Second, by increasing the sample size from ~0.7 million to\n1.5 million individuals, we substantially boosted discovery power and identified 140\nindependent loci, nearly a threefold increase in locus discovery. Third, our cell type\nenrichment analyses highlighted hair cells and supporting cells as the most strongly\nenriched cell types, in contrast to the stria vascularis reported by Trpchevska et al., but\nconsistent with Eshel et al.2. We found this discrepancy is likely attributable to limited\ncell-type coverage used in Trpchevska et al.1 (see detailed response below). We have\nadded these points in our revised Discussion section (page11, 12).\nAs suggested by the reviewer, we have now updated our meta-analysis to incorporate\nthe GWAS summary statistics from De Angelis et al3. after removing the UK Biobank\nsamples from this dataset (page 15). Unfortunately, the summary statistics from\nIvarsdottir et al4. are no longer accessible at the link provided, and those from Praveen\net al.5 is not public available. We therefore can not include them in our meta-analysis.\nWith the updated meta-analysis, we detected 140 independent SNPs, of which 44 were\nnovel after comprehensive comparison with existing studies (Figure R1).\nIn addition, we have now provided details on the phenotype harmonization, ancestry\nrepresentation and technical compatibility in Methods section (page 16, 17;\nSupplementary Table 1).\nFigure R1 Manhattan plot of the cross-ancestry GWAS meta-analysis. The horizontal\ngrey line denotes the genome-wide significance threshold (P < 5×10-8). In total, 44 novel\nindependent risk loci are highlighted in red, with their nearest annotated genes shown\nin black text.\n2. The use of SBayesRC for polygenic risk estimation and fine-mapping is appropriate\nand reflects current best practices. The application of SMR and HEIDI to integrate GWAS\nand eQTL/mQTL data is well executed but the tissue used (primarily blood) is not\nsuitable for extrapolating on cochlear mechanisms. Despite the fact authors\nacknowledge this limitation, blood eQTL/mQTL analysis does not yield relevant\nfindings. Authors should remove these results, and should mention the limitations of\nnon-existing human cochlear eQTL resources in the discussion.\nRe: We agree with the reviewer that the lack of human cochlear eQTL/mQTL resources\nlimits the mechanistic interpretation, and we have clarified this as a limitation in the\nrevised discussion (page 14). Nevertheless, we note that the use of blood-derived\neQTLs/mQTLs as a proxy tissue is a widely used practice when disease-relevant tissues\nare unavailable. For example, Jansen et al. used blood eQTLs to prioritize candidate\ngenes for Alzheimer’s disease, Han et al. integrated primary open-angle glaucoma\n(POAG) GWAS with blood pQTLs to identify putative casual proteins6,7. Similar to these\nstudies, we present our blood-based eQTL/mQTL analyses as preliminary findings,\nwhile highlighting the need for generating human cochlear multi-omics QTL datasets to\nvalidate and extend these results.\n3. The LDSC-SEG approach is informative but incomplete. The authors expand the list of\nimplicated cochlear cell types compared to Trpchevska et al. However, the\ninterpretation of enrichment in the stria vascularis versus sensory epithelium remains\nunclear. Recent work by Eshel et al. argues that cochlear epithelial cells, not the stria\nvascularis, are the primary site of GWAS signal enrichment. There, Eshel et al. use a\ndifferent scRNAseq resource and different GWAS studies but non systematically. Thus,\nthe GWAS and scRNAseq dataset matter. Here is the opportunity to seal this debate with\nthis 1.5 million sample size. First, authors should use both LDSC and Magma as\nTrpchevska et al., but also scDRS as used by Eshel et al. Only those cell types\ncorroborated by at least two of these methods would be valid signals. Second, for\nreplication purposes, authors should use the scRNAseq datasets that Trpchevska et al.\nhave used, as well as that from Eshel et al. Since some of these signals were borderline\nsignificant, the new GWAS megasample, tested against the 3 scRNAseq datasets\nincluding Jean et al., would validate or not the suggestions from the past studies. Only\nadult mouse data should be used for comparison sake between the 3 scRNAseq studies.\nEnsure the method used is the exact same Trpchevska has reported, for sake of\nreplication. The spatial transcriptomics at E16.5 from the mouse embryo is not relevant\nto humans with ARHL when compared to GTeX – please remove, it does not add\nsignificantly novel information.\nRe: We thank the reviewer for this important and constructive comment. In response,\nwe have substantially revised our cell type-specific enrichment analyses. Specifically, in\naddition to the dataset of Jean et al., we incorporated the additional scRNA-seq datasets\nused by Eshel et al. and Trpchevska et al., and systematically applied three different\nmethods (LDSC-SEG, MAGMA, and scDRS) to integrate our GWAS with these scRNA-seq\nresources1,2. For all analyses, we strictly followed the procedures reported by\nTrpchevska et al. to ensure methodological consistency and enable direct replication.\nAcross these analyses, we found that hair cells and supporting cells showed the most\nconsistent and robust enrichment, suggesting that they are the primary contributors to\nthe genetic basis of ARHL (Figure R2). This result is in agreement with Eshel et al., but\ndifferent from those reported from Trpchevska et al., who emphasized the top\nenrichment in the stria vascularis. To investigate this discrepancy, we repeated\nheritability enrichment analysis using other two scRNA-seq datasets used in Trpchevska\net al. (Milon et al. and Ranum et al.)8,9, and successfully replicated the reported\nenrichment in the stria vascularis by Trpchevska et al. (Figure R3). However, this\nenrichment did not persist when larger-scale datasets with more comprehensive cell-\ntype coverage were included. We found that this discrepancy is most likely explained by\nthe limitation of cell type coverage in the datasets used by Trpchevska et al. 8,9.\nSpecifically, cell-type specificity was computed as,\n𝑇𝑃𝑀\ng,c\n𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =\ng,c ∑𝑛 𝑇𝑃𝑀\ni=1 g,i\nwhere g denotes a gene and c is a cell type. This metric quantifies, for each gene, how\nspecific its expression is to a given cell type relative to all other cell types profiled. When\nthe coverage of profiled cell types is limited, specificity scores can become inflated,\nleading to spurious enrichment. Our findings therefore underscore the importance of\nusing datasets with comprehensive cell-type coverage to achieve reliable inference of\ncell-type-trait associations.\nWe have included these results and clarifications in revised Results and Discussions\n(page 10, 11, 13; Figure 6c; Supplementary Figure 14 and Figure 15; Supplementary\nTable 17-19).\nFigure R2 Cell-type-specific heritability enrichment across three methods using\ncomprehensive scRNA-seq datasets. Each row corresponds to one method. The first\ncolumn shows results from the scRNA-seq dataset from Jean et al. (P8-20), and the\nremaining columns show results from the scRNA-seq dataset used by Eshel et al..\nSignificance was controlled at FDR < 0.05 using the Benjamini-Hochberg (BH)\nprocedure. The dashed line shows -log10(P), where P is the dataset specific nominal\nthreshold (i.e., the raw P of the least BH-significant test). If absent, no tests passed the\nBH-FDR < 0.05 threshold.\nFigure R3 Cell-type-specific heritability enrichment across three methods using scRNA-\nseq dataset used by Trpchevska et al.. Each column corresponds to one method. The\nupper panel shows results based on the dataset from Milon et al. and lower panel shows\nresults based on the dataset from Ranum et al.. Significance was controlled at FDR < 0.05\nusing the Benjamini-Hochberg (BH) procedure. The dashed line shows -log10(P), where\nP is the dataset specific nominal threshold (raw P of the least BH-significant test). If\nabsent, no tests passed the BH-FDR < 0.05 threshold.\nFor the used spatial transcriptomics dataset, we agree with the reviewer that embryonic\nmouse data are not ideal for studying ARHL. However, given the current lack of human\nembryonic datasets, a surrogate model is necessary. Prior studies (e.g., Breschi et al.10)\nhave shown that mouse remains a reliable model with broadly conserved human-mouse\ngene expression profiles. Moreover, the gsMap method has successfully recapitulated\nknown organ-trait associations11 using mouse embryos at E16.5. Accordingly, we\npresent these analyses as preliminary results. We acknowledge the limitations of relying\non mouse embryonic data and highlight that human embryonic ST datasets are required\nto validate and extend these findings. We included this limitation in our revised\nDiscussion section (page 14).\n4. Of important note, the manuscript relies on the Jean et al. single-cell cochlear dataset,\nwhich, while comprehensive, uses 10X Genomics technology with limited sequencing\ndepth. This restricts the ability to resolve rare or transcriptionally subtle cell types, such\nas subtypes of Type 1 auditory neurons, which have been missed by Jean et al. There\nwere also batch to batch differences in the Jean et al. study that are indicative of\nweaknesses in the validity of their sequencing. While Trpchevska et al. relied on the\nscRNAseq data from Millon et al., which also was performed on 10X genomics, the\noutput successfully identified the 3 subtypes of Type 1 auditory neurons. This is why the\ncorroboration, also through 3 distinct scRNAseq datasets, is needed. The authors should\ndiscuss the results from this analysis and the possibility that technical or reference\nbiases that may influence the observed enrichments (which is why the use of the former\ndatasets for replication and validation is needed).\nRe: We fully acknowledge the limitations of the Jean et al.12 scRNA-seq dataset, including\nthe shallow sequencing depth inherent to the 10X Genomics platform, which may hinder\nthe detection of rare or transcriptionally subtle cell types populations such as subtype of\nType I auditory neurons, as well as the reported batch-to-batch variation.\nTo address this concern, and in line with the reviewer’s suggestion, we expanded our\nanalyses to also incorporate the scRNA-seq datasets used by Trpchevska et al. and Eshel\net al., in addition to Jean et al., thereby enabling replication and validation across three\nindependent resources. We further applied three different enrichment methods (LDSC-\nSEG, MAGMA, scDRS) to strengthen the robustness of our findings. By corroborating\nresults across multiple datasets and analytical methods, we were able to minimize\ndataset-specific biases and increase confidence in the enrichment results (see results\nshowed in response above).\nWe have included in discussion (page 14) about how potential technical biases (e.g.,\nsequencing depth, batch effects) and reference-based limitations could influence the\nobserved enrichments, highlighting the importance of validating enrichment signals\nacross diverse datasets.\n5. Discuss the value of finding missense variations (found in familial cases, thus being\nrare), and identified here as common variants? Are these variants less penetrant in the\nstudied population vs familial cases from more isolated consanguineous populations?\nWhat is the importance of such findings in a GWAS?\nRe: We thank the reviewer for this comment. Several missense variants previously\nreported in familial hearing loss were also identified in our GWAS, consistent with\nevidence that 5%-10% of GWAS signals are coding variants. While these genes are\nprimarily implicated in monogenic non-syndromic hearing loss, their reduced\npenetrance in the general population likely reflects the polygenic architecture of age-\nrelated hearing loss, as well as the influence of genetic modifiers and environmental\nfactors.\nWe think these findings are valuable because they both provide independent support for\ngenes previously implicated in familial hearing loss and highlight shared biological\nmechanisms between monogenic and polygenic forms of hearing loss. We have included\nthis discussion in the revised Discussion (page 12).\n6. Authors should include sex stratified analyses as supplemental, with summary stats\nfor the two sexes as well.\nRe: We thank the reviewer for this suggestion. Among all GWAS datasets included in our\nmeta-analysis, only De Angelis et al. provide sex-stratified summary statistics. As other\ndatasets lack results for sex-stratified analysis, we are unable to perform comprehensive\nsex-stratified meta-analyses. We have clarified this limitation in the revised Discussion\nsection and highlighted the need for future studies with sex-stratified GWASs to better\nassess potential sex-specific effects (page 14).\n7. The absence of X-chromosome analysis should be discussed in the limitations – what\nwould be expected from this analysis?\nRe: We thank the reviewer for this comment. We agree that X-chromosome analyses\nwould be helpful to reveal sex-specific or dosage-related genetic effects not captured by\nautosomal variants13, and may provide additional biological insights. However, as most\nGWAS datasets included in our meta-analysis did not provide the X-chromosome\nspecific results, we were unable to perform this analysis. We have acknowledged this as\na limitation and added it to the revised Discussion (page 14).\nMinor comments:\nThe manuscript would benefit from more detailed descriptions of quality control,\nphenotype harmonization, and ancestry-specific analyses.\nRe: We agree with the reviewer and have now added more details on the quality control,\nphenotype harmonization and ancestry representation in the revised Methods (page 16,\n17) and Supplementary Table 1.\nFor loci claimed as novel, the authors should provide a comprehensive comparison with\nall recent GWAS, including those not included in the meta-analysis, to avoid\noverstatement of novelty.\nRe: We thank the reviewer for raising this point. In the revised analysis, we compared\nour lead SNPs with those reported by Ivarsdottir et al., Kalra et al., Praveen et al., De\nAngelis et al., Wells et al. and Trpchevska et al. 1,3,5,14,15, to ensure a comprehensive\nassessment and avoid overstatement of novelty. Based on this comprehensive\ncomparison, we identified 44 novel loci not previously reported by these studies. We\nhave included these clarifications in revised manuscript (page 18).\nThe link to the summary statistics from the present study should be made available to\nthe reviewers and opened to public once publication is accepted.\nhttps://zenodo.org/records is not sufficient for us to access the summary stats from the\npresent meta-analysis. https://umgear.org is too vague – please specify the full link or\nprovide more indications to access this dataset within umgear. Access to the codes:\nhttps://github.com/Crazzy-Rabbit/project_hearing_loss/ was not possible. Please allow\nthe reviewer to verify the codes.\nRe: We apologize for incomplete or missing link. We have now made the GWAS\nsummary statistic publicly available at https://zenodo.org/records/17141085 and the\nsingle-cell RNA-seq data of mouse cochlea available at\nhttps://umgear.org/p?s=7fd80bf5. The GitHub repository with all analysis code is now\npublicly released at https://github.com/Crazzy-Rabbit/project_hearing_loss/. These\nresources are all referenced in the Data Availability and Code Availability sections of our\nrevised manuscript (page 23).\nCorrected FDR values for significance in Figure 6 seems wrong. Please check. Shouldn’t\nit be: p < 0,00139 ?\nRe: In Figure 6, the reported values are FDR values calculated using the Benjamini-\nHochberg procedure, with significance defined as FDR < 0.05. The threshold suggested\nby the reviewer (P < 0.00139) reflects a Bonferroni correction. While both approaches\nare commonly used to control multiple testing, we chose FDR as it provides greater\ndiscovery power. We have clarified this point and the definition of statistical significance\nin the revised manuscript and Figure 6 legend.\nI’d recommend one supplemental excel file with each supplementary table on separate\nsheets.\nRe: As suggested by the reviewer, we have now combined all supplementary tables into\na single supplemental Excel file, with each table placed on a separate sheet.\nThe use of AI for text and grammar should be acknowledged in the manuscript.\nRe: We have now added an acknowledgment in the revised manuscript that the AI tool\nGrammarly was used to assist with grammar correction.\nReviewer #2:\nIn this study, Shi and colleagues conducted the largest cross-ancestry genome-wide\nassociation study (GWAS) to date on age-related hearing loss (ARHL), and applied a\nrange of cutting-edged quantitative genetics approaches for post-GWAS analyses. There\nare some interesting results that will be of interest to the readership. However, I have\na few comments below that I think could enhance the quality of the manuscript.\n1. Please include Cochran’s Q statistic or I2 value for each SNP to quantify the\nheterogeneity in the cross-ancestry meta-analysis GWAS.\nRe: We thank the reviewer for this suggestion. In the revised manuscript, we have\ncalculated Cochran’s Q statistic and corresponding I2 values for each SNP in the cross-\nancestry meta-analysis. We have included these results in Supplementary Table 2.\n2. In addition to comparing effect sizes between EUR and non-EUR populations for the\nhighlighted SNPs, it is also valuable to examine their MAF across different populations.\nRe: We thank the reviewer for this suggestion. In addition to comparing effect sizes\nbetween EUR and non-EUR populations, we have now examined the effect allele\nfrequency (EAF) of the identified SNPs. The results showed a moderately high cross-\nancestry concordance in EAF across ancestries (Figure R4). We have included this result\nin the revised Results section (page 5) and Supplementary Figure 4.\nFigure R4 Pairwise comparisons of lead SNP effect allele frequency between European\nand three other ancestry groups, East Asian (a), African (b) and Admixed American (c).\nEach dot shows the effect size of a lead SNP, and the error bars show the 95% confidence\ninterval of the estimated effect sizes. Abbreviations: EUR, European ancestry; EAS, East\nAsian ancestry; AMR, Admixed American ancestry; AFR, African ancestry.\n3. Page #6: What is the sample size of the de-meta-analysed GWAS after excluding the\nUKB cohort? The substantial reduction in power may make it less representative of the\noriginal cross-ancestry meta-analysis, potentially introducing bias into the BADGERS\nanalysis. I recommend using Popcorn to estimate cross-population genetic correlations\nas an additional sensitivity check for BADGERS, which can account for potential sample\noverlap during estimation.\nRe: We thank the reviewer for this comment. After excluding the UK Biobank samples,\nthe de-meta-analysis included 342,295 cases and 730,385 controls (total N = 1,072,680).\nAs suggested by the reviewer, we used Popcorn to estimate cross-population genetic\ncorrelations, which yielded an estimate of 0.98 (se = 0.0026), indicating very high\nconcordance between meta-analysis and de-meta-analysis. This result suggests that\ndespite the reduction in sample size, the underlying genetic architecture before and\nafter de-meta-analysis remain consistent, and the BADGERS results are less likely to be\nbiased.\nTo further assess robustness, we applied LDSC to estimate the genetic correlations\nbetween our ARHL GWAS meta-analysis (without de-meta-analysis) and the 12 non-\nauditory phenotypes identified by BADGERS using the de-meta results. These\ncorrelations remained strong and statistically significant (Supplementary Table 5),\nfurther supporting the reliability of the BADGERS findings. We have added these results\nin our revised Results section (page 6, 7; Supplementary Table 5).\n4. To the best of my knowledge, GSMR cannot reliably distinguish between correlated\nand uncorrelated pleiotropy from true causality, although it uses the HEIDI-outlier\nmethod to exclude instrumental SNPs that are likely pleiotropic to some extent. I\nrecommend conducting additional sensitivity analyses (e.g., two sample MR, CAUSE,\nLHC-MR, etc) to re-assess the significant GSMR findings and help identify potential\nconfounding effects due to pleiotropy.\nRe: We agree with the reviewer that GSMR alone cannot fully distinguish correlated or\nuncorrelated pleiotropy from true causality. To address this, we performed additional\nsensitivity analyses using different MR approaches, including IVW, PRESSO, Robust,\nSimple Median and RAPS16–19. The results from these complementary methods were\nlargely consistent with our GSMR findings, supporting the robustness of the causal\ninference (Figure R5). We have included these results in revised manuscript (page 7;\nSupplementary Figure 6; Supplementary Table 7).\nFigure R5 Causal effect estimates from multiple Mendelian Randomization (MR)\nmethods using ARHL as outcome. Each panel represents one exposure trait; the y-axis\nshows the MR methods used, and the x-axis shows the corresponding effect estimates\nwith 95% confidence intervals for ARHL as the outcome. Statistical significance (P <\n0.05) is indicated by a solid circle.\n5. For the results generated by S-LDSC, were they adjusted using the baseline model?\nIf not, I recommend re-running the analysis with baseline model adjustment.\nRe: In our study, the S-LDSC results were indeed adjusted using the baseline model. In\naddition, we have performed complementary analyses using MAGMA and scDRS, which\nwere all included in our revised manuscript to further support these functional\nenrichment findings.\nMinor:\n6. Page #4: Please state how many independent SNP loci from the study are replicated\ncompared to Trpchevska et al.\nRe: In our updated meta-analysis, which additionally incorporated the GWAS summary\nstatistic from De Angelis et al3, we identified 140 lead SNPs after LD clumping. Of these,\nreached genome-wide significance (P < 5×10-8), and all of them reached the Bonferroni\ncorrected significance (P < 0.00049). We have now included these results in revised\nResults section (page 4, 5).\n7. Page #4: Please provide the genetic correlation between the cross-ancestry meta-\nanalysis GWAS and the FinnGen replication GWAS.\nRe: We thank the reviewer for this comment. As suggested by the reviewer, we\ncalculated the genetic correlation between the cross-ancestry meta-analysis GWAS and\nthe FinnGen replication GWAS using Popcorn. The estimated genetic correlation was\n0.63 (se = 0.12), indicating a moderate level of concordance. We have included these\nresults to the Results section (page 4).\n8. Page #6: Please remove the extra space in “Fig.4 a”\nRe: We thank the reviewer for pointing this error out. The extra space in “Fig. 4a” has\nbeen removed in the revised manuscript (page 6).\n9. Some quantitative genetics methods (e.g., SMR, S-LDSC) appear to have limited ability\nto account for LD patterns across different populations, which may represent a\nlimitation of these analyses.\nRe: We agree that methods such as SMR and S-LDSC have limited ability to fully account\nfor LD differences across populations. We have now added a statement in the revised\nDiscussion to acknowledge this limitation (page 14).\nReviewer #3:\nThis manuscript presents a large-scale, multi-ancestry GWAS meta-analysis of age-\nrelated hearing loss incorporating ~1.5 million individuals. The authors identify 98\ngenome-wide significant loci (37 novel), perform fine-mapping and functional follow-up\nusing multi-omics data, and explore cell-type heritability enrichment using spatial and\nsingle-cell transcriptomic data. The scale, multi-ancestry approach to GWAS, and\nintegrative analyses are well thought-out and the paper is well-written. However, there\nare methodological concerns that should be addressed.\nMajor Comments\n1. The manuscript defines a locus as novel if it is not previously reported and is\nlocated >500 kb from any known lead SNP. However, this approach risks both false\npositive (loci >500kb from previous hit that are actually in strong long-range LD) and\nfalse negatives (loci within 500kb but not in LD with known hits may be genuinely\nnovel). It would strengthen the authors’ argument to account for LD structure in\nadditional to physical distance.\nRe: We thank the reviewer for this constructive suggestion. In the revised analysis, we\nre-defined novel loci by considering both physical distance and LD structure.\nSpecifically, we classified a SNP as novel only if it was located more than 500kb away\nfrom any previously reported variants with r2 < 0.1, or if it was within 500 kb of known\nloci but showed stronger LD independence (r² < 0.05). We have clarified this updated\ndefinition in the revised Methods section (page 18).\n2. The replication sample is FinnGen. This is a genetically isolated European population.\nGiven the stated emphasis on multi-ancestry discovery in this project, the use of a\nEuropean-only replication cohort raises concerns about generalizability. Additionally,\nmany downstream analyses use 1000 Genomes 3 European LD reference panels (e.g.,\nLDSC, GSMR). This introduces potential biases when applied to the non-European\nancestries represented in the discovery meta-analysis.\nRe: We thank the reviewer for this comment. Although our study incorporates GWAS\ndata from multiple ancestries, individuals of European ancestry account for ~ 77% of\nthe total sample (Figure R6). Given this predominant European contribution, and the\nlack of large-scale cross-ancestry replication cohorts and ancestry-specific LD reference\npanels, we used FinnGen for replication and the 1000 Genomes European samples as LD\nreference for downstream analyses. We note that using a European-only replication\ncohort may result in a downward bias in replication rates, rather than inflated\nestimates. This strategy is consistent with those commonly used in cross-ancestry GWAS\nfor other complex traits20,21. We acknowledge that this approach may introduce biases\nand limit generalizability, and have added this limitation in the Discussion section (page\n13, 14).\nFigure R6 Overview of GWAS summary statistics used in the cross-ancestry meta-\nanalysis of ARHL. The figure summarizes the contribution cohorts, ancestry\ncomposition, and sample sizes for each dataset included.\n3. The authors examine genetic correlations with 1,738 traits in UK Biobank but do not\nexplain how these traits were selected. Were they filtered for heritability, relevance, or\nredundancy?\nRe: The 1,738 traits included in our genetic correlation analysis were selected from all\nUK Biobank traits based on nominally significant heritability (P < 0.05) estimated using\nLDSC, as described in BADGERS22. We have now included this clarification in the revised\nMethods section (page 19).\n4. The manuscript reports fine-mapping results using GWFM showing 1,042 variants in\n157 credible sets and identifies only 26 SNPs with PIP >0.9. This resolution appears\nmodest given the large sample size of the study. It would be beneficial to show\nfine-mapping diagnostics and compare to another rigorously benchmarked fine-mapping\nmethod like Susie to help contextualize performance.\nRe: We thank the reviewer for this suggestion. In our updated meta-analysis with\nincluding additional GWAS summary data3, we identified 1,108 SNPs in 165 credible sets\nand identifies 22 SNPs with PIP > 0.9 after GWFM. We agree that the resolution may\nappear modest compared to other complex traits with similar sample sizes. However,\nthis is likely due to the highly polygenic architecture of ARHL, which limits fine-mapping\npower.\nTo evaluate the robustness of our fine-mapping results, we re-analyzed all loci using the\nSuSiE method with the same GWAS summary statistics and LD reference. In the\ndiagnostic analysis, SuSiE estimated a mean lambda of 0.006 across loci (Figure R7),\nindicating good concordance between the LD reference and our ARHL GWAS data.\nFurther fine-mapping analysis with SuSiE identified 9 SNPs with PIP > 0.9, of which 7\noverlapped with the 22 SNPs identified by our GWFM analysis. Among the 22 fine-\nmapped variants identified by GWFM, 7 achieved a SuSiE PIP > 0.9, and 50% had PIPs >\n0.5 and 87% had PIPs a> 0.1, confirming the consistency and reliability of our findings.\nWe have added these results in our revised Results section (page 8; Supplementary\nFigure 10; Supplementary Table 11).\nFigure R7 Concordance between the LD reference and ARHL GWAS. The histogram\nshows the distribution of λ values estimated from SuSiE diagnostics.\n5. The authors use ST data from mouse embryos to infer spatial enrichment of ARHL\nheritability, but ARHL is a late-onset disease. Further justification of this embryonic data\nas a suitable proxy for this human condition is needed.\nRe: We thank the reviewer for this comment. We agree that embryonic mouse spatial\ntranscriptomics (ST) data are not ideal for studying ARHL. However, in the absence of\nadult human ST datasets, a proxy model is necessary. Similar strategies have been used\nfor other late-onset complex traits, such as male pattern baldness (MPB), where mouse\nembryonic ST data revealed risk gene enrichment in facial epithelial cells that spatially\ncluster during the formation of hair follicles and express canonical follicle marker genes\n(KRT15, KRT5, and KRT17)11. This example illustrates that embryonic ST data can\nprovide valuable insights into the developmental origins of late-onset diseases.\nNevertheless, we acknowledge the limitations of using mouse embryonic data as a proxy\nfor ARHL and emphasize the need of generating adult human ST datasets, particularly\ninner-ear ST datasets, to validate and extend these findings. We have included this\nclarification in our revised Discussion section (page 13, 14).\nMinor Comments\n1. The LDSC intercept is reported (1.0095) and it suggests minimal inflation due to\nconfounding. However, given the extremely large sample size of the analysis, it would be\nhelpful to also report lambda 1000 (the genomic inflation factor standardized to 1000\ncases/controls) to further evaluate inflation relative to scale.\nRe: As suggested by the reviewer, in addition to reporting the LDSC intercept, we have\nnow calculated lambda 1000, which was estimated to be 1.0006. This value further\nconfirms that there is minimal inflation due to confounding in our GWAS meta-analysis.\nWe have included this result in our revised Results section (page 4).\n2. The link to the GitHub code for this project is broken.\nRe: We thank the reviewer for pointing this error out. The GitHub code is now publicly\navailable at https://github.com/Crazzy-Rabbit/project_hearing_loss/. We have also\nupdated this in the Code Availability section of our revised manuscript (page 23).\n3. In supplemental table 5, there is a row with NaN values (noisy workplace). What does\nthis indicate?\nRe: The NaN values in Supplementary Table 5 indicate that the GSMR analysis could not\nbe performed due to an insufficient number of valid SNP instruments (fewer than the\nrequired 10 SNPs after the clumping with P < 5×10⁻⁸and LD r2 < 0.05), resulting in no\nestimated values. We have clarified this in the revised table legend (Supplementary\nTable 6).\n4. Please define beta hat in Figure 4b.\nRe: We thank the reviewer for pointing out the missing definition. In Figure 4b, 𝑏̂\n𝑥𝑦\ndenotes the estimated effect size from exposures (y-axis) on outcome (ARHL) obtained\nfrom the GSMR analysis. We have added this definition to the revised figure legend for\nclarity (page 32).\nReferences\n1. Trpchevska, N. et al. Genome-wide association meta-analysis identifies 48 risk\nvariants and highlights the role of the stria vascularis in hearing loss. The American\nJournal of Human Genetics 109, 1077–1091 (2022).\n2. Eshel, M., Milon, B., Hertzano, R. & Elkon, R. The cells of the sensory epithelium, and\nnot the stria vascularis, are the main cochlear cells related to the genetic\npathogenesis of age-related hearing loss. The American Journal of Human Genetics\n111, 614–617 (2024).\n3. De Angelis, F. et al. Sex differences in the polygenic architecture of hearing problems\nin adults. Genome Medicine 15, 36 (2023).\n4. Ivarsdottir, E. V. et al. The genetic architecture of age-related hearing impairment\nrevealed by genome-wide association analysis. Commun Biol 4, 706 (2021).\n5. Praveen, K. et al. Population-scale analysis of common and rare genetic variation\nassociated with hearing loss in adults. Commun Biol 5, 540 (2022).\n6. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional\npathways influencing Alzheimer’s disease risk. Nat Genet 51, 404–413 (2019).\n7. Han, X. et al. Large-scale multitrait genome-wide association analyses identify\nhundreds of glaucoma risk loci. Nat Genet 55, 1116–1125 (2023).\n8. Milon, B. et al. A cell-type-specific atlas of the inner ear transcriptional response to\nacoustic trauma. Cell Rep 36, 109758 (2021).\n9. Ranum, P. T. et al. Insights into the Biology of Hearing and Deafness Revealed by\nSingle-Cell RNA Sequencing. Cell Rep 26, 3160-3171.e3 (2019).\n10. Breschi, A., Gingeras, T. R. & Guigó, R. Comparative transcriptomics in human and\nmouse. Nat Rev Genet 18, 425–440 (2017).\n11. Song, L., Chen, W., Hou, J., Guo, M. & Yang, J. Spatially resolved mapping of cells\nassociated with human complex traits. Nature 641, 932–941 (2025).\n12. Jean, P. et al. Single-cell transcriptomic profiling of the mouse cochlea: An atlas for\ntargeted therapies. Proceedings of the National Academy of Sciences 120,\ne2221744120 (2023).\n13. Gorlov, I. P. & Amos, C. I. Why does the X chromosome lag behind autosomes in GWAS\nfindings? PLoS Genet 19, e1010472 (2023).\n14. Ivarsdottir, E. V. et al. The genetic architecture of age-related hearing impairment\nrevealed by genome-wide association analysis. Commun Biol 4, 1–13 (2021).\n15. Kalra, G. et al. Biological insights from multi-omic analysis of 31 genomic risk loci for\nadult hearing difficulty. PLoS Genet 16, e1009025 (2020).\n16. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis\nwith multiple genetic variants using summarized data. Genet Epidemiol 37, 658–665\n(2013).\n17. Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal\npleiotropy in causal relationships inferred from Mendelian randomization between\ncomplex traits and diseases. Nat Genet 50, 693–698 (2018).\n18. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in\nMendelian Randomization with Some Invalid Instruments Using a Weighted Median\nEstimator. Genet Epidemiol 40, 304–314 (2016).\n19. Zhao, Q., Wang, J., Hemani, G., Bowden, J. & Small, D. S. Statistical inference in two-\nsample summary-data Mendelian randomization using robust adjusted profile score.\nThe Annals of Statistics 48, 1742–1769 (2020).\n20. Han, X. et al. Large-scale multitrait genome-wide association analyses identify\nhundreds of glaucoma risk loci. Nat Genet 55, 1116–1125 (2023).\n21. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium.\nElectronic address: andrew.mcintosh@ed.ac.uk & Major Depressive Disorder\nWorking Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-\nwide study of depression identifies 697 associations implicating cell types and\npharmacotherapies. Cell 188, 640-652.e9 (2025).\n22. Yan, D. et al. BADGERS: biobank wide trait association. eLife 12, RP91360 (2024)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for the effort in making this analysis clear, as the one from Jean et al. is much more homogeneous and\nunified for the paper, than the several datasets used in Eshel et al.\nThe results using Jean et al. show that 2 out 3 enrichment methods show both hair cells, support cells, but also basal and\nroot cells from the stria as major contributors to hearing loss.\nAs mentioned above in my former comment, 2 out 3 methods are sufficient for demonstrating a crucial role in hearing loss. If\nnot all cells of the stria are implied, basal and root cells are consistently shown in the Jean et al. dataset. The authors find\nsupportive evidence that both papers Trpchevska et al. and Eshel et al. that were conflicting each other, were correct.\nPlease modify the statements in the manuscript accordingly, in order not to minimize the importance of basal and root cells\nfrom the stria in the pathogenesis of hearing loss.\nAs lines are not numbered, it is hard to point exaclty to where the changes have to be done. Please check thoroughly.\n--\n(pages 11 in results and in discussion, and in abstract; page 13 “rather than the stria vascularis” – “However, this enrichment\ndid not persist when larger-scale datasets with more comprehensive cell-type coverage were included”). It did persist in\nMAGMA and scDRS – this supports the role of the stria basal and root cells.\n--\nFinally, the lack of inclusion of two other GWAS datasets is a pitty. I personally contacted the lead authors, but none\nresponded. The icelandic dataset is indeed missing from the Decode database, and Regeneron only released fractionated\nsummary statistics and not the pooled one. Very sad the journals and reviewers had missed this key information in these\npublications. The discussion needs to state somehow that attempts to gather the summary stats from these two other studies\nfailed, which is an hindrance to the advances of science in general, and that it could have provided a complete analysis of\nthe published literature. Yet, the compiled analysis from this study enables to demonstrate that the conflicting results from\nTrpchevska and Eshel are now solved.", "reviewer_2": "The authors fully addressed my previous concerns.", "reviewer_3": "The author has adequately addressed all my previous comments."}, "author_response": "Response to Reviewers’ Comments\nWe thank all three reviewers for their positive comments on our revised manuscript. In\nthis revision, we have responded to the additional comments from reviewer #1 point-\nby-point below (in blue) and have highlighted the relevant changes (in yellow) in the\nmanuscript files.\nReviewer #1:\nI thank the authors for the effort in making this analysis clear, as the one from Jean et al.\nis much more homogeneous and unified for the paper, than the several datasets used in\nEshel et al.\nRe: We thank the reviewer for acknowledging the improvements made in our revised\nmanuscript. We appreciate the additional comments and have made changes below.\nThe results using Jean et al. show that 2 out 3 enrichment methods show both hair cells,\nsupport cells, but also basal and root cells from the stria as major contributors to hearing\nloss.\nRe: We thank the reviewer for this insightful comment. We have revised the text to\nexplicitly state that, in the Jean et al. dataset, two out of three enrichment methods\n(MAGMA and scDRS) consistently identified not only hair cells and supporting cells but\nalso the basal and root cells of the stria vascularis as import contributors to hearing loss.\nWe have clarified this in the revised Results (page 10): “Across these analyses, hair cells\nand supporting cells consistently showed the strongest enrichment, suggesting they are\nthe primary contributors to the genetic architecture of ARHL (Fig. 6c; Supplementary\nTable 17). Moreover, significant enrichment signals were also observed in the basal and\nroot cell compartments of the stria vascularis in both MAGMA and scDRS analyses,\nhighlighting a potential complementary role of these non-sensory epithelial cells in\nhearing loss pathogenesis.”\nAs mentioned above in my former comment, 2 out 3 methods are sufficient for\ndemonstrating a crucial role in hearing loss. If not all cells of the stria are implied, basal\nand root cells are consistently shown in the Jean et al. dataset. The authors find supportive\nevidence that both papers Trpchevska et al. and Eshel et al. that were conflicting each\nother, were correct. Please modify the statements in the manuscript accordingly, in order\nnot to minimize the importance of basal and root cells from the stria in the pathogenesis\nof hearing loss. As lines are not numbered, it is hard to point exaclty to where the changes\nhave to be done. Please check thoroughly.\n--\n(pages 11 in results and in discussion, and in abstract; page 13 “rather than the stria\nvascularis” – “However, this enrichment did not persist when larger-scale datasets with\nmore comprehensive cell-type coverage were included”). It did persist in MAGMA and\nscDRS – this supports the role of the stria basal and root cells.\n--\nRe: We thank the reviewer for raising this important point. We have checked the whole\nmanuscript thoroughly and revised accordingly (including Abstract, Results and\nDiscussion) to reflect that basal and root cells of the stria vascularis consistently show\nenrichment in the Jean et al. dataset and to ensure that their contribution is not missed.\nOur changes included:\nAbstract (page 2): “Moreover, analyses incorporating spatial and single-cell\ntranscriptomic identified the inner ear as a crucial site of ARHL, emphasizing the\nimportance of hair cells, supporting cells, basal and root cells of the stria vascularis to its\npathogenesis.”\nResults (page 11): “These findings reconcile the apparent discrepancy between Eshel et\nal., who emphasized hair cells and supporting cells, and Trpchevska et al., who highlighted\nbasal and root cells of the stria vascularis, by revealing their shared contribution to\nhearing loss. Collectively, these results provide robust evidence that ARHL heritability is\nsignificantly enriched in hair cells, supporting cells, and basal and root cells of the stria\nvascularis, reinforcing their collective roles in hearing loss pathogenesis.”\nDiscussion (page 11): “Third, our cell type enrichment analyses highlighted hair cells and\nsupporting cells as the most strongly enriched cell types, while also revealing significant\nenrichment in basal and root cells of the stria vascularis, thereby reconciling the findings\nof Eshel et al. and Trpchevska et al.”\nDiscussion (page 13): “These findings reconcile the previously conflicting results of Eshel\net al. and Trpchevska et al., providing a unified view of the cellular basis of ARHL\nheritability.”\nDiscussion (page 13): “When large-scale datasets with broader cell-type coverage were\nanalyzed, enrichment was observed not only in basal and root cells of the stria vascularis\nbut also in hair cells and supporting cells, indicating that the differences between studies\nlikely result from variation in dataset composition rather than conflicting biological\ninterpretations.”\nFinally, the lack of inclusion of two other GWAS datasets is a pitty. I personally contacted\nthe lead authors, but none responded. The icelandic dataset is indeed missing from the\nDecode database, and Regeneron only released fractionated summary statistics and not\nthe pooled one. Very sad the journals and reviewers had missed this key information in\nthese publications. The discussion needs to state somehow that attempts to gather the\nsummary stats from these two other studies failed, which is an hindrance to the advances\nof science in general, and that it could have provided a complete analysis of the published\nliterature. Yet, the compiled analysis from this study enables to demonstrate that the\nconflicting results from Trpchevska and Eshel are now solved.\nRe: We thank the reviewer for this comment. In response, we have added a statement in\nthe Discussion explicitly describing our efforts to obtain these two additional GWAS\ndatasets, which were not successful. We have clarified this in Discussion (page 15):\n“Finally, although we collected almost all available GWAS summary statistics for ARHL,\nthe dataset from Ivarsdottir et al.1 is currently missing from the Decode database, and that\nfrom Praveen et al.2 is not publicly accessible. This lack of data accessibility limited our\nability to perform a fully comprehensive meta-analysis, highlighting the need for broader\ndata sharing to accelerate progress in the field.”\nReviewer #2:\nThe authors fully addressed my previous concerns.\nRe: We thank the reviewer for the comments on all versions of our manuscript.\nReviewer #3:\nThe author has adequately addressed all my previous comments.\nRe: We thank the reviewer again for the time taken to read our revised manuscript and\nfor providing critical and constructive comments.\nReference\n1. Ivarsdottir, E. V. et al. The genetic architecture of age-related hearing impairment\nrevealed by genome-wide association analysis. Commun Biol 4, 706 (2021).\n2. Praveen, K. et al. Population-scale analysis of common and rare genetic variation\nassociated with hearing loss in adults. Commun Biol 5, 540 (2022)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Thank you very much, the manuscript is a fantastic contribution to the field. Many thanks for the endeavors in addressing my\ncomments. Congratulations!"}, "author_response": ""}]

Decision letter