Statistical Method for Identifying Genetic Modifiers of Conotruncal Heart De

鉴定圆锥干心脏 De 遗传修饰的统计方法

• 批准号: 8706228
• 负责人: Tao Wang
• 金额: $ 16.61万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8706228
• 关键词: 22q11.2; Accounting; Biological; Cardiovascular system; Characteristics; Child; Collaborations; Communities; Complex; Computer software; Congenital Abnormality; Congenital Heart Defects; Data; Data Set; Defect; Disease; Failure; Family; Freedom; Funding; General Population; Genes; Genetic; Genetic Heterogeneity; Goals; Heart; Heritability; Logistic Regressions; Malignant Neoplasms; Medicine; Modeling; Parents; Pathway interactions; Patients; Phenotype; Risk; Role; Staging; Statistical Methods; Syndrome; Testing; Triad Acrylic Resin; United States National Institutes of Health; Variant; base; cohort; college; data mining; disease characteristic; disorder subtype; genetic association; genetic variant; genome wide association study; genome-wide; improved; insight; malformation; novel; open source; programs; public health relevance; simulation; statistics; stem; tool; trait

DESCRIPTION (provided by applicant): Genetic heterogeneity is one of the major reasons for failure to identify genetic associations of complex diseases. Often, patients with complex diseases have various phenotypic characteristics and can be grouped into variable subtypes, possibly reflecting differences in underlying genetic mechanisms. Existing approaches either ignore genetic heterogeneity among patients, or lack parsimony with a large number of degrees of freedom in test statistics. There is a lack of statistical approaches that can efficiently combie association evidence from varied disease subtypes while accounting for genetic heterogeneity. As such, we propose to test genetic association using a novel three-stage polynomial logistic regression model, which takes genetic heterogeneity among disease subtypes into account while reducing large number of parameters for testing genetic association. We plan to apply the proposed approach to a real dataset from a collaboration study with the goal to find genetic associations of structural cardiovascular malformations in 22q11DS children. We expect that the proposed project will yield a new powerful statistical approach and the corresponding software for identifying genetic associations of complex diseases, and has the potential to identify novel genetic variants, genes and pathways, providing an insight into biological mechanisms of congenital heart defects.

描述（由申请人提供）：遗传异质性是未能确定复杂疾病遗传关联的主要原因之一。通常，患有复杂疾病的患者具有各种表型特征，并且可以被分组为可变的亚型，这可能反映了潜在遗传机制的差异。现有的方法要么忽略了患者之间的遗传异质性，或缺乏大量的自由度在测试统计的吝啬。缺乏统计方法，可以有效地结合不同疾病亚型的关联证据，同时考虑遗传异质性。因此，我们建议使用一种新的三阶段多项式逻辑回归模型来测试遗传关联，该模型考虑了疾病亚型之间的遗传异质性，同时减少了大量用于测试遗传关联的参数。我们计划将所提出的方法应用于一项合作研究的真实的数据集，目的是找到22 q11 DS儿童结构性心血管畸形的遗传关联。我们预计，拟议的项目将产生一个新的强大的统计方法和相应的软件，用于识别复杂疾病的遗传关联，并有可能识别新的遗传变异，基因和途径，提供对先天性心脏病的生物学机制的见解。