利用听力测量数据进行精准表型分型及感音神经性听力损失的基因发现

文献信息

中文摘要

重要性： 作为定量性状的听力敏感性背后的遗传结构对于推进听力健康领域的精准医学至关重要。尽管已开展大量全基因组关联研究（GWAS），但已确定的强效遗传驱动因素很少。
目的： 比较使用纯音平均值（PTA）进行精准表型分型与使用诊断编码方法进行常规表型分型在检测与感音神经性听力损失（SNHL）相关的遗传变异方面的敏感性。
设计、场所和参与者： 在这项遗传关联研究中，针对SNHL使用了两种不同的表型分型方法进行了2项独立的GWAS：（1）诊断编码对照和（2）使用PTA数据的精准表型分型。使用了来自单一三级中心生物样本库、具有欧洲遗传 ancestry 人群的全基因组测序数据。对于基于诊断编码的对照GWAS，病例和对照参与者主要使用与SNHL听力损失相关的国际疾病分类第九版（ICD-9）和国际疾病分类及相关健康问题统计分类第十版（ICD-10）诊断和操作编码来定义。对于精准表型分型GWAS，听力状态被定义为连续变量，使用听力图中较好耳的标准3频率PTA。数据分析于2025年8月至2026年4月进行。
主要结局和测量指标： 使用GWAS汇总统计量计算基于单核苷酸变异（SNV）的PTA和ICD表型遗传力估计值；使用功能注释将遗传风险位点映射到候选基因；评估与现有GWAS的遗传相关性；并计算多基因风险评分以在来自美国国立卫生研究院All of Us研究计划的独立队列中进行测试。
结果： 总体而言，第一项GWAS纳入了10 164例病例参与者（平均[SD]年龄，63.9[21.3]岁；5118[50.4%]女性）和51 305例对照参与者（平均[SD]年龄，51.1[21.0]岁；28 784[56.1%]女性）；第二项GWAS纳入了16 057名有PTA的参与者（平均[SD]年龄，55.5[21.8]岁；9147[57.0%]女性）。基于PTA的GWAS识别出3个全基因组显著位点，映射到4个基因：EML6、SPTBN1、ARHGEF28和EYA4（基于SNV的遗传力估计值[h2]=11.78%；SE=2.84%）。基于ICD的GWAS未识别出显著位点（h2=2.90%；SE=0.77%）。发现基于PTA的GWAS与3个听力相关能力性状之间存在全基因组相关性（例如，基于PTA的GWAS与助听器使用：r=0.985；SE=0.163；P=1.56×10-9）。在All of Us队列中，PTA衍生的多基因风险评分与自我报告的耳聋显著相关（比值比[OR]，1.09；95%CI，1.07-1.12），而ICD衍生的评分则不相关（OR，1.00；95%CI，0.97-1.02）。
结论与意义： 在这项遗传关联研究中，使用听力测量PTA进行精准表型分型相比基于诊断编码的方法改善了SNHL的遗传发现。更高的遗传力估计值、全基因组显著位点的识别以及更强的多基因风险估计表明，定量听力测量能更好地捕捉遗传结构，支持听力健康领域的精准医学。

英文摘要

IMPORTANCE: The genetic architecture underlying hearing sensitivity as a quantitative trait is critical for advancing precision medicine in hearing health. Despite extensive genome-wide association study (GWAS) efforts, few strong genetic drivers have been identified.
OBJECTIVE: To compare the sensitivity for detecting genetic variants associated with sensorineural hearing loss (SNHL) using precision phenotyping with pure-tone averages (PTAs) with common phenotyping using diagnostic coding approaches.
DESIGN, SETTING, AND PARTICIPANTS: In this genetic association study, 2 separate GWASs were conducted using different phenotyping approaches for SNHL: (1) diagnostic code control and (2) precision phenotyping with PTA data. Whole-genome sequencing data from a population with European genetic ancestry collected at a single tertiary center biobank was used. For the control GWAS based on diagnostic codes, case and control participants were defined using primarily International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis and procedure codes related to SNHL hearing loss. For the precision phenotyping GWAS, hearing status was defined as a continuous variable, using the standard 3-frequency PTA of the better hearing ear from the audiogram. Data analysis was conducted from August 2025 to April 2026.
MAIN OUTCOMES AND MEASURES: GWAS summary statistics were used to calculate estimates of single-nucleotide variant (SNV)-based heritability for PTA and ICD phenotypes; to map the genetic risk loci to candidate genes using functional annotation; to evaluate genetic correlations with existing GWASs; and to calculate polygenic risk scores for testing in an independent cohort from the National Institutes of Health's All of Us Research Program.
RESULTS: Overall, 10 164 case participants (mean [SD] age, 63.9 [21.3] years; 5118 [50.4%] female) and 51 305 control participants (mean [SD] age, 51.1 [21.0] years; 28 784 [56.1%] female) were included in the first GWAS; 16 057 participants with PTA (mean [SD] age, 55.5 [21.8] years; 9147 [57.0%] female) were included in the second GWAS. PTA-based GWAS identified 3 genome-wide significant loci mapped to 4 genes: EML6, SPTBN1, ARHGEF28, and EYA4 (SNV-based heritability estimate [h2] = 11.78%; SE = 2.84%). ICD-based GWAS identified no significant loci (h2 = 2.90%; SE = 0.77%). Genome-wide correlations between the PTA-based GWAS and 3 hearing-related ability traits were found (eg, PTA-based GWAS and hearing aid use: r = 0.985; SE = 0.163; P = 1.56 × 10-9). In the All of Us cohort, PTA-derived polygenic risk scores were significantly associated with self-reported deafness (odds ratio [OR], 1.09; 95% CI, 1.07-1.12), whereas ICD-derived scores were not (OR, 1.00; 95% CI, 0.97-1.02).
CONCLUSIONS AND RELEVANCE: In this genetic association study, precision phenotyping using audiometric PTAs improved genetic discovery for SNHL compared with diagnostic code-based approaches. Higher heritability estimates, identification of genome-wide significant loci, and stronger polygenic risk estimation indicate that quantitative hearing measures better capture genetic architecture, supporting precision medicine in hearing health.