Analyzing nonsyndromic orofacial clefts GWAS data with case-parent trio design

使用病例亲子三人组设计分析非综合征性口面裂 GWAS 数据

• 批准号: 10760966
• 负责人: Zhongxue Chen
• 金额: $ 6.04万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-06 至 2023-09-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10760966
• 关键词: Analyzing; nonsyndromic; orofacial; clefts; GWAS

PROJECT SUMMARY Case-parent trio (CPT) design is widely used in genome-wide association studies (GWASs) for childhood diseases, including nonsyndromic orofacial cleft birth defects (cleft lip and cleft palate). To detect associated genetic variants, the transmission disequilibrium test (TDT)—which is known to be robust against the subpopulation stratification—is usually applied to CPT data. However, several limitations associated with TDT- based methods may result in serious power loss and failure to detect true signals; this problem shows up in current nonsyndromic orofacial cleft GWASs, as only a small portion of associated genetic variants have been identified so far. Substantially more powerful advanced statistical approaches are therefore desperately needed. We propose some cutting-edge statistical methods that circumvent the limitations of current tests and use extra information from the data to substantially gain detection power. By applying our proposed methodology to nonsyndromic orofacial cleft GWAS data with CPT design, we expect to discover many novel genetic risk factors. The long-term goal of this project is to help researchers better understand the underlying mechanisms of these conditions by using cutting-edge statistical approaches to identify genetic components of nonsyndromic orofacial cleft birth defects, leading to more refined diagnostic capabilities and ultimately better targeted therapies. The overall objective of this application is to develop robust and powerful statistical approaches and apply them to the orofacial cleft birth defects data to discover novel genetic risk factors. The central hypothesis is that powerful statistical approaches can be developed if useful information from the data is obtained and fully and efficiently used; meanwhile, through use of these powerful cutting-edge statistical methods, more disease genetic variants will be discovered. The rationale for the proposed research is that successful completion will enable us to obtain more useful genetic information for nonsyndromic orofacial clefts. The central hypothesis will be tested and the objective attained by pursuing the following specific aims: 1) Develop, test, and validate powerful and robust statistical approaches for nonsyndromic orofacial cleft GWAS with case-parent trio design; 2) Identify novel disease genetic components by applying the proposed powerful statistical approaches to the orofacial cleft birth defects data. The contribution of this project will be significant because the findings will advance knowledge of developmental biology and create new opportunities for clinical and translational research in nonsyndromic orofacial cleft birth defects. This proposed research is innovative because the proposed novel statistical approaches are more powerful than existing methods in that they harness more information from the data and use it more efficiently.

项目摘要 病例-父母三人组（CPT）设计在儿童全基因组关联研究（GWASs）中被广泛应用 疾病，包括非综合征性口面裂出生缺陷（唇腭裂）。检测相关的 遗传变异，传递不平衡测试（TDT）-这是已知的是强大的对 亚群分层-通常应用于CPT数据。然而，与TDT相关的一些限制- 基于的方法可能会导致严重的功率损耗和无法检测到真实信号;这个问题出现在 目前的非综合征性口面裂GWAS，因为只有一小部分相关的遗传变异已被 到目前为止，识别。因此，迫切需要更强大的先进统计方法。 我们提出了一些尖端的统计方法，这些方法规避了当前测试的局限性，并使用了额外的 从数据中提取信息以基本上获得检测功率。通过将我们提出的方法应用于 非综合征性口面裂GWAS数据与CPT设计，我们期望发现许多新的遗传危险因素。 该项目的长期目标是帮助研究人员更好地了解这些疾病的潜在机制。 使用尖端的统计方法来识别非综合征性口面的遗传成分来治疗疾病 这将有助于提高对先天性裂畸形的诊断能力，并最终实现更好的靶向治疗。的 该应用程序的总体目标是开发强大的统计方法，并将其应用于 口面裂出生缺陷数据，以发现新的遗传风险因素。核心假设是， 如果能够从数据中充分有效地获得有用的信息， 与此同时，通过使用这些强大的尖端统计方法， 会被发现建议进行研究的理由是，成功完成研究将使我们能够获得 非综合征性口面裂的基因信息中心假设将得到检验， 通过追求以下具体目标实现的目标：1）开发、测试和验证功能强大且健壮的 非综合征性口面裂GWAS的病例-父母三人设计的统计方法; 2）确定新的 疾病的遗传成分，通过应用拟议的强大的统计方法，口面裂出生 缺陷数据。该项目的贡献将是重大的，因为研究结果将促进对 发展生物学，为非综合征性疾病的临床和转化研究创造新的机会。 口面裂先天缺陷这项研究是创新的，因为提出的新的统计 这些方法比现有的方法更强大，因为它们从数据中利用了更多的信息， 更有效地使用它。