Statistical Methods for Family-Based Association Studies

描述（由申请人提供）：越来越多的证据表明，全基因组关联研究（GWAS）代表了鉴定与常见人类疾病相关的基因的有力方法。GWAS可以使用基于人群的设计或传统的基于家庭的设计。基于家庭的GWAS的最大优势之一是其对人群分层可能影响的鲁棒性，这可能会增加假阳性率。然而，这种鲁棒性也可能导致功率损失。因此，更强大的关联测试，是强大的人口分层需要GWAS下的家庭为基础的设计。另一方面，模拟研究以及对几种常见疾病的遗传结构的研究表明，致病变异包括常见和罕见。新技术允许对大群体个体的部分基因组进行测序，或者在未来，对整个基因组进行测序。用于检测常见变异关联的统计方法在检测罕见变异关联时可能不是最佳的。因此，迫切需要开发强大的统计方法来检测基于家族的序列数据的罕见变异。 该项目将探索新的统计方法和可行的算法，以绘制复杂的疾病基因，以家庭为基础的GWAS。该项目的第一个具体目标是为基于家庭的GWAS开发一种更强大的单标记两阶段联合分析，该分析对人群分层具有鲁棒性。第二个具体目标是开发一种新的关联检验，可以在基于家族的设计下检测罕见变异。第三个具体目标是使用广泛的模拟研究将所提出的方法与现有方法进行比较，并将所提出的方法应用于选定的基于家庭的GWAS数据集。该项目的最后一个具体目标是为新开发的方法开发计算机软件，并免费向科学界发布软件。 这些新的统计方法的发展和用户友好的工具，从这个项目产生的将有助于研究人员在基因组定位的基因，有助于复杂的遗传性状。新的、健全的统计方法将使科学界受益匪浅。 公共卫生相关性：虽然大多数已发表的全基因组关联研究使用基于人群的设计，但基于家族的设计在识别疾病相关基因方面发挥了重要作用。基于家系的设计在质量控制和对人群分层的稳健性方面具有优势。然而，统计方法来分析全基因组关联研究在家庭为基础的设计并没有得到尽可能多的关注为基础的设计方法。该项目将开发新的和强大的统计方法来分析来自基于家族的全基因组关联研究的数据。新开发的统计方法将使所有进行全基因组基于家族的关联研究的研究人员受益，增强基于家族的设计的使用，并加速发现负责复杂疾病的基因。

DESCRIPTION (provided by applicant): There is increasing evidence that genome-wide association studies (GWAS) represent a powerful approach in the identification of genes involved in common human diseases. GWAS may use either population-based designs or traditional family-based designs. One of the biggest advantages of family-based GWAS is its robustness to possible effects of population stratification, which can inflate the false positive rate. However, this robustness can also lead to a loss of power. Thus, more powerful association tests that are robust to population stratification are needed for GWAS under family-based designs. On the other hand, simulation studies as well as studies of the genetic architectures of several common diseases suggest that causal variants include both common and rare. New technologies allow for sequencing of parts of the genome-or, in the future, the whole genome-of large groups of individuals. Statistical methods developed to detect associations of common variants may not be optimal in detecting associations of rare variants. So there is a great need to develop powerful statistical methods to detect rare variants for family-based sequence data. This proposed project will explore novel statistical methods and feasible algorithms to map complex disease genes for family-based GWAS. The first specific aim of this project is to develop a more powerful single-marker two-stage joint analysis for family-based GWAS that is robust to population stratification. The second specific aim is to develop a new association test that can detect rare variants under family-based designs. Using extensive simulation studies to compare the proposed methods with existing methods and applying the proposed methods to selected family-based GWAS data sets are the third specific aim. The last specific aim of this project is to develop computer software for the newly developed methods and release the software to the scientific community at no charge. The developments of these novel statistical methods and the user friendly tools generated from this project will aid researchers in genomic localization of genes that contribute to complex genetic traits. New, sound statistical methods will greatly benefit the scientific community. PUBLIC HEALTH RELEVANCE: Although most published genome-wide association studies used population-based designs, family-based designs have played an important role in identifying disease-associated genes. Family-based designs offer advantages in terms of quality control and robustness to population stratification. However, statistical methods to analyze genome-wide association studies under family-based designs have not received as much attention as methods for population-based designs. This project will develop novel and powerful statistical methods to analyze data from family-based genome-wide association studies. The newly developed statistical methods will benefit all investigators conducting genome-wide family-based association studies, enhance the use of family- based designs, and accelerate the discovery of genes responsible for complex diseases.