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

描述（由申请人提供）：虽然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，家族研究的发展要少得多。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时，它提供了针对混杂偏倚的强大保护（即，人口分层）。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。本申请的目标是开发一种用于基于高维家族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面裂联盟全基因组关联研究数据集来构建基于家族的风险预测模型。中心假设是，所提出的方法，它考虑了大量的遗传和环境预测因子，家庭信息和人口子结构，将优于现有的广义估计方程为基础的基因型评分方法（GEE-GS），并会导致一个强大的和准确的基于家庭的口面裂的风险预测模型。这项拟议中的研究将由一位早期的新研究者发起，他已经组建了一个由资深科学家组成的研究团队，其中包括罗伯特·C。作者：Jeffrey 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学方法，并积极参与口面裂遗传和临床研究。在拟议的研究项目中，研究小组将把注意力转向以家庭为基础的口面裂风险预测。计划的具体目标是：1）开发一个强大的聚类似然比方法的高维家庭为基础的风险预测，并通过广泛的模拟研究，比较其性能与GEE-GS方法;和2）建立一个高维口面裂风险预测模型，同时考虑大量的遗传和环境的预测因子，它们的相互作用，和家庭信息。如果成功的话，新的方法将促进高维的基于家庭的风险预测研究。口面裂风险预测研究还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.