High-dimensional Statistical Genetic Approach for Family-based Orofacial Clefts

基于家族的口颌面裂的高维统计遗传学方法

• 批准号: 8227059
• 负责人: Qing Lu
• 金额: $ 22.49万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8227059
• 关键词: Accounting; Address; Attention; Basic Science; Cleaved cell; Cleft lip with or without cleft palate; Clinical; Clinical Research; Clinical Sciences; Collaborations; Complex; Data; Data Set; Development; Disease; Environmental Risk Factor; Equation; Ethnic group; Family; Family Study; Family member; Future; Genes; Genetic; Genetic Research; Genetic Risk; Genotype; Goals; Health Benefit; Healthcare; Individual; International; Laboratories; Lead; Medicine; Methods; Modeling; Performance; Phenotype; Play; Population; Population Control; Prevention strategy; Research; Research Personnel; Research Project Grants; Risk; Role; Sampling; Senior Scientist; Societies; Staging; Stratification; Translating; Translational Research; Translations; Work; base; clinical practice; experience; gene environment interaction; genetic association; genetic variant; genome wide association study; improved; maternal cigarette smoking; molecular marker; novel; novel strategies; orofacial; population based; simulation; success

DESCRIPTION (provided by applicant): Although family studies were the basis for genetic risk prediction before the advent of modern molecular markers, they have been much less developed for risk prediction of complex diseases using high-dimensional data. Family studies offer many ideal features for large-scale risk prediction research. It provides robust protection against confounding bias when dealing with samples from multiple ethnic groups (i.e., population stratification). Aside from that, Family studies could take into account family information (i.e., genotype and phenotype information from family members) for improved risk prediction. Despite these advantages, they have been used infrequently in recent risk prediction research. The goals of this application are to develop a statistical genetic approach for high-dimensional family-based risk prediction, and to build a family-based risk prediction model by applying the proposed approach to the International Consortium of Orofacial Clefts genome-wide association study dataset. The central hypothesis is that the proposed approach, which considers a large number of genetic and environmental predictors, family information and population substructure, will outperform an existing generalized estimating equations based genotype scoring approach (GEE-GS), and will lead to a robust and accurate family-based risk prediction model for orofacial clefts. The proposed research will be initiated by an early-stage new investigator, who has assembled a research team of senior scientists, including Robert C. Elston, Jeffrey C. Murray and Brian Schutte. The team has developed novel statistical genetic approaches for risk prediction research, and has been active in orofacial clefts genetic and clinical research. In the proposed research project, the research team will turn its attention to family-based orofacial clefts risk prediction. The planned specific aims are to: 1) Develop a robust clustered likelihood ratio approach for high-dimensional family-based risk prediction and compare its performance with the GEE-GS approach through extensive simulation studies; and 2) Build a high-dimensional orofacial clefts risk prediction model by simultaneously considering a large number of genetic and environmental predictors, their interactions, and family information. If successful, the new approach will facilitate high-dimensional family- based risk prediction studies in general. The orofacial clefts risk prediction study will also lead to a novel risk prediction model that can be further replicated and evaluated through application to independent populations. PUBLIC HEALTH RELEVANCE: Risk prediction capitalizing on emerging genetic findings, environmental risk factors and family information holds great promise for improved healthcare and personalized medicine. The proposed research by a new early-stage investigator will develop a quantitative method for high-dimensional family-based risk prediction, and will then use it to form a novel orofacial clefts risk prediction model. The success of the project will advance high-dimensional family-based risk prediction research in general and benefit translational research aimed at developing more effective and affordable prediction and prevention strategies for orofacial clefts.

描述（申请人提供）：尽管在现代分子标记出现之前，家族研究是遗传风险预测的基础，但在使用高维数据进行复杂疾病风险预测方面，它们的发展还远远不够。家庭研究为大规模风险预测研究提供了许多理想的特征。在处理来自多个种族群体的样本（即人口分层）时，它可以提供强有力的保护，防止混杂偏差。除此之外，家庭研究可以考虑家庭信息（即家庭成员的基因型和表型信息）以改进风险预测。尽管有这些优点，但它们在最近的风险预测研究中很少使用。该应用程序的目标是开发一种用于高维基于家庭的风险预测的统计遗传学方法，并通过将所提出的方法应用于国际口颌面裂联盟全基因组关联研究数据集来构建基于家庭的风险预测模型。中心假设是，所提出的方法考虑了大量的遗传和环境预测因素、家庭信息和人口子结构，将优于现有的基于广义估计方程的基因型评分方法（GEE-GS），并将产生一个稳健且准确的基于家庭的口颌裂风险预测模型。拟议的研究将由一名处于早期阶段的新研究员发起，他组建了一个由资深科学家组成的研究团队，包括罗伯特·C·埃尔斯顿、杰弗里·C·穆雷和布莱恩·舒特。该团队开发了用于风险预测研究的新型统计遗传学方法，并一直活跃于口颌裂遗传学和临床研究。在拟议的研究项目中，研究团队将把注意力转向基于家庭的口面部裂风险预测。计划的具体目标是： 1) 开发一种稳健的聚类似然比方法，用于高维家庭风险预测，并通过广泛的模拟研究将其性能与 GEE-GS 方法进行比较； 2）通过同时考虑大量遗传和环境预测因子、它们的相互作用以及家庭信息，构建高维口颌裂风险预测模型。如果成功，新方法将总体上促进基于家庭的高维度风险预测研究。口面裂风险预测研究还将产生一种新的风险预测模型，可以通过应用于独立人群来进一步复制和评估。 公共卫生相关性：利用新出现的遗传发现、环境风险因素和家庭信息进行风险预测，为改善医疗保健和个性化医疗带来了巨大希望。一位新的早期研究人员提出的研究将开发一种基于高维家庭的风险预测的定量方法，然后用它来形成一种新型的口面部裂风险预测模型。该项目的成功将总体上推进基于家庭的高维风险预测研究，并有利于旨在开发更有效和负担得起的口颌裂预测和预防策略的转化研究。