IMPROVEMENT OF MAPPING ACCURACY BY UNIFYING LINKAGE AND ASSOCIATION ANALYSIS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 众所周知，系谱/家系数据记录了在创始人单倍型中附近的等位基因共存的信息，以及从父母到后代的共同传递信息，揭示了不同但互补的遗传结构特征。无论是假设连锁均衡的传统连锁分析，还是基于家庭的关联检验，都只捕捉到部分信息，导致效率低下。利用数据增强技术和EM算法，我们开发了一种基于似然的方法，该方法将连锁分析和关联分析嵌入到一般系谱数据的统一框架中。相对于连锁分析和关联分析，该方法具有更高的估计精度和计算能力。新方法的理论优势在于，它可以最大限度地提取统计信息，从而提高作图精度和作图能力，因为它将基于多位点群体的关联和基于系谱的连锁分析整合到一个连贯的框架中。此外，广泛的模拟研究，包括对遗传分析研讨会第15期的数据的应用，证明了这一基于模型的链接和关联分析的一般似然框架的实际优势。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests capture only partial information, leading to inefficiency. Using a data augmentation technique and the EM algorithm, we developed a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach has greater estimation accuracy and power. The new approach holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association and pedigree-based linkage analysis into a coherent framework. Furthermore, extensive simulation studies, including application to the data of Genetic Analysis Workshop 15, demonstrated the practical advantage of this general likelihood framework for model-based linkage and association analysis.