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Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering
American Journal of Human Genetics ( IF 8.1 ) Pub Date : 2021-12-02 , DOI: 10.1016/j.ajhg.2021.11.004
Douglas M Shaw 1 , Hannah P Polikowsky 1 , Dillon G Pruett 2 , Hung-Hsin Chen 1 , Lauren E Petty 1 , Kathryn Z Viljoen 3 , Janet M Beilby 3 , Robin M Jones 2 , Shelly Jo Kraft 4 , Jennifer E Below 1
Affiliation  

Developmental stuttering is a speech disorder characterized by disruption in the forward movement of speech. This disruption includes part-word and single-syllable repetitions, prolongations, and involuntary tension that blocks syllables and words, and the disorder has a life-time prevalence of 6–12%. Within Vanderbilt’s electronic health record (EHR)-linked biorepository (BioVU), only 142 individuals out of 92,762 participants (0.15%) are identified with diagnostic ICD9/10 codes, suggesting a large portion of people who stutter do not have a record of diagnosis within the EHR. To identify individuals affected by stuttering within our EHR, we built a PheCode-driven Gini impurity-based classification and regression tree model, PheML, by using comorbidities enriched in individuals affected by stuttering as predicting features and imputing stuttering status as the outcome variable. Applying PheML in BioVU identified 9,239 genotyped affected individuals (a clinical prevalence of ∼10%) for downstream genetic analysis. Ancestry-stratified GWAS of PheML-imputed affected individuals and matched control individuals identified rs12613255, a variant near CYRIA on chromosome 2 (B = 0.323; p value = 1.31 × 10−8) in European-ancestry analysis and rs7837758 (B = 0.518; p value = 5.07 × 10−8), an intronic variant found within the ZMAT4 gene on chromosome 8, in African-ancestry analysis. Polygenic-risk prediction and concordance analysis in an independent clinically ascertained sample of developmental stuttering cases validate our GWAS findings in PheML-imputed affected and control individuals and demonstrate the clinical relevance of our population-based analysis for stuttering risk.



中文翻译:

表型风险分类可实现发育性口吃的表型插补和基因发现

发育性口吃是一种言语障碍,其特征是言语向前运动受阻。这种混乱包括部分单词和单音节的重复、延长以及阻碍音节和单词的无意识紧张,这种疾病的终生患病率为 6-12%。在范德比尔特大学电子健康记录 (EHR) 关联的生物存储库 (BioVU) 中,92,762 名参与者中只有 142 人 (0.15%) 被识别出具有诊断 ICD9/10 代码,这表明很大一部分口吃患者没有诊断记录在电子病历内。为了识别 EHR 中受口吃影响的个体,我们构建了一个 PheCode 驱动的基于基尼杂质的分类和回归树模型 PheML,使用受口吃影响的个体中丰富的合并症作为预测特征,并将口吃状态作为结果变量。在 BioVU 中应用 PheML 确定了 9,239 名基因型受影响个体(临床患病率约为 10%),用于下游遗传分析。PheML 估算的受影响个体和匹配对照个体的祖先分层 GWAS 鉴定出 rs12613255,这是欧洲祖先分析中 2 号染色体上CYRIA附近的变异体(B = 0.323;p 值 = 1.31 × 10 -8)和 rs7837758(B = 0.518;p 值 = 1.31 × 10 -8 )。 p 值 = 5.07 × 10 -8 ),这是在非洲血统分析中在 8 号染色体上的ZMAT4基因内发现的内含子变异。在独立的临床确定的发育性口吃病例样本中进行的多基因风险预测和一致性分析验证了我们在 PheML 估算的受影响个体和对照个体中的 GWAS 研究结果,并证明了我们基于人群的口吃风险分析的临床相关性。

更新日期:2021-12-02
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