Investigating the medical phenome of speech-language traits: risk, resilience, and opportunities for intervention

调查言语特征的医学现象：风险、恢复力和干预机会

• 批准号: 10799383
• 负责人: Srishti Nayak
• 金额: $ 17.5万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10799383
• 关键词: Acceleration; Accounting; African American population; All of Us Research Program; Articulation; Awareness; Behavior; Biomedical Research; Clinical; Code; Cognitive; Collection; Communication; Communication impairment; Communities; Complex; Data; Databases; Development; Developmental Stuttering; Diagnosis; Disease; Disease Outcome; Early Intervention; Early treatment; Education; Electronic Health Record; Epidemiology; Gene Expression; Generations; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genomics; Health; Health Status; Human Genetics; ICD-9; Individual; Individual Differences; Intervention; Knowledge; Language; Language Development; Language Development Disorders; Language Disorders; Language Pathology; Laws; Learning Disabilities; Link; Maps; Measures; Medical; Mental Health; Mental disorders; Mission; National Institute on Deafness and Other Communication Disorders; Nature; Neurodegenerative Disorders; Organism; Outcome; Participant; Personal Satisfaction; Persons; Phenotype; Population; Predisposition; Public Health; Research; Rest; Risk; Risk Factors; Risk Marker; Speech; Stuttering; Symptoms; Techniques; Testing; United States National Institutes of Health; Work; autism spectrum disorder; biobank; bioinformatics tool; clinical phenotype; clinical risk; comorbidity; developmental disease; disability; diverse data; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic data; health equity; health inequalities; improved; insight; language impairment; language outcome; machine learning algorithm; medical examination; multidisciplinary; novel; phenome; physical conditioning; polygenic risk score; population health; portability; precision medicine; resilience; resilience factor; risk prediction; spelling; trait; transcriptome

Project Summary Speech-language impairments have been clinically and genetically linked with risks for mental health disorders, poor physical health, and poor educational outcomes. However, large-scale studies of speech-language deficits primarily remain the purview of research on learning disabilities and developmental disorders (e.g., articulation and fluency disorders; developmental language disorder; autism), underestimating the health impact of speech and language traits for all individuals. This project aims to investigate the overall health impact of speech and language difficulties, disorders, and risk. Specifically, we use unbiased, data-driven, computational approaches to examine the medical "phenome", or entire collection of health and disease outcomes (phenotypes) found in the All of Us Electronic Health Records (EH Rs). These phenome-wide association studies (PheWAS) have great potential to discover comorbidities, shared genetic risk for multiple health conditions, and identify targets for early intervention and therapies. In Aim 1 studies, we characterize associations between speech-language difficulties or disorders, and the entirety of the medical phenome. In Aim 2A and 28 studies, we investigate how genetic predispositions for speech-language abilities that have not been measured in All of Us participants (e.g., spelling; phonemic awareness), are associated with the entire medical phenome. In Aim 2A, we focus on genetic risk markers that are derived directly from common genetic variants associated with speech-language phenotypes; and in Aim 28 we build on this by additionally incorporating aspects of the transcriptome (e.g., gene expression), which improves portability of genetic risk predictions across multiple ancestry, and particularly in African Americans - a community who are highly underrepresented in biomedical research, and both historically and presently subject to systemic health inequity. Our approach, which is agnostic to specific clinical phenotypes, symptoms, diagnoses, or disorders, will reveal population-level health and disease outcomes associated with speech-language traits in populations that are usually underrepresented in biomedical research. This project directly responds to the call to make discoveries using high-quality All of Us data; advances the NIDCD mission to improve the lives of people with communication disorders; and enhances diversity in genomics research through the use of diverse data, and through opportunities for diversifying the workforce. By combining precision medicine techniques with a health equity and community-engaged focus, findings from this project will address critical health needs for subsets of individuals with certain clinical and/or genetic risk factors related to speech and language, as well as entire communities in whom communication traits/disorders have been understudied.

项目摘要 语言障碍在临床和遗传上与精神健康障碍、身体健康状况差和教育成果差的风险有关。然而，大规模的语言缺陷研究主要仍然是学习障碍和发育障碍（例如，发音和流畅性障碍;发育性语言障碍;自闭症），低估了言语和语言特征对所有个体的健康影响。该项目旨在调查言语和语言困难、障碍和风险对整体健康的影响。具体而言，我们使用无偏见的，数据驱动的，计算方法来检查医学“表型组”，或在我们所有人电子健康记录（EH Rs）中发现的健康和疾病结果（表型）的整个集合。这些全表型关联研究（PheWAS）具有很大的潜力，可以发现合并症，多种健康状况的共同遗传风险，并确定早期干预和治疗的目标。在目标1研究中，我们描述了言语语言困难或障碍与整个医学表型之间的关联。在Aim 2A和28研究中，我们调查了尚未在All of Us参与者中测量的语言能力的遗传倾向（例如，拼写;音素意识）与整个医学表型相关联。在Aim 2A中，我们专注于直接来源于与语言表型相关的常见遗传变异的遗传风险标志物;在Aim 28中，我们通过额外纳入转录组的各个方面（例如，基因表达），这提高了跨多个祖先的遗传风险预测的可移植性，特别是在非裔美国人中-一个在生物医学研究中代表性极低的社区，并且在历史上和现在都受到系统性健康不平等的影响。我们的方法对特定的临床表型、症状、诊断或疾病是不可知的，它将揭示与生物医学研究中通常代表性不足的人群中的言语语言特征相关的人群水平的健康和疾病结果。该项目直接响应了使用高质量的All of Us数据进行发现的号召;推进NIDCD改善沟通障碍患者生活的使命;通过使用多样化数据和通过使劳动力多样化的机会来增强基因组学研究的多样性。通过将精准医学技术与健康公平和社区参与的重点相结合，该项目的研究结果将解决具有与语言和语言相关的某些临床和/或遗传风险因素的个人子集的关键健康需求，以及沟通特征/障碍未得到充分研究的整个社区。