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

描述（由申请人提供）：尽管在现代分子标记出现之前，家庭研究是遗传风险预测的基础，但在使用高维数据预测复杂疾病的风险方面，家庭研究还远远不够发达。家庭研究为大规模风险预测研究提供了许多理想的特征。当处理来自多个种族群体的样本（即人口分层）时，它提供了强大的防止混淆偏差的保护。除此之外，家庭研究可以考虑家庭信息（即来自家庭成员的基因型和表型信息），以提高风险预测。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。本申请的目的是建立一种基于家庭的高维风险预测的统计遗传学方法，并将该方法应用于国际口腔面部裂协会全基因组关联研究数据集，建立基于家庭的风险预测模型。研究的中心假设是，该方法考虑了大量的遗传和环境预测因子、家庭信息和群体亚结构，将优于现有的基于广义估计方程的基因型评分方法（GEE-GS），并将导致一个鲁棒和准确的基于家庭的唇腭裂风险预测模型。拟议中的研究将由一位处于早期阶段的新研究员发起，他已经组建了一个由资深科学家组成的研究小组，包括罗伯特·c·埃尔斯顿、杰弗里·c·默里和布莱恩·舒特。该团队开发了用于风险预测研究的新型统计遗传方法，并活跃于口腔面部唇裂的遗传和临床研究。在拟建的研究项目中，研究团队将把重点放在基于家庭的唇腭裂风险预测上。计划的具体目标是：1)开发一种基于高维家庭的风险预测的鲁棒聚类似然比方法，并通过广泛的模拟研究将其与GEE-GS方法的性能进行比较；2)同时考虑大量遗传和环境预测因子及其相互作用和家族信息，构建高维的口面部裂风险预测模型。如果成功，新方法将促进基于家庭的高维风险预测研究。该风险预测研究也将导致一种新的风险预测模型，可以通过应用于独立人群进一步复制和评估。

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.