IMPROVEMENT OF MAPPING ACCURACY BY UNIFYING LINKAGE AND ASSOCIATION ANALYSIS

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。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。传统的假设连锁平衡的连锁分析或基于家庭的关联检验(FBAT)只捕捉到部分信息，导致效率低下。例如，在不存在等位基因关联的情况下，FBAT甚至无法检测到非常紧密的连锁，而违反连锁平衡假设将导致估计偏差和连锁作图效率降低。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。相对于连锁分析或关联分析，该方法有望具有更高的估计精度和能力。蒙特卡罗模拟支持这些理论预期，并表明我们的新方法：(1)比FBATS或经典连锁分析更强大；(2)无论是否存在关联，都可以无偏地估计遗传参数，从而弥补了传统连锁分析在存在关联的情况下的偏差和较低的精度；(3)能够单独识别紧密连锁。新方法还具有理论上的优势，即它可以最大限度地提取统计信息，从而提高映射的精度和能力，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，与使用单纯形算法或牛顿型方法最大化高阶多维似然函数的现有参数方法相比，该方法具有数值稳定和计算效率高的特点。

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 (FBATs) capture only partial information, leading to inefficiency. For example, FBATs will fail to detect even very tight linkage in the case where no allelic association exists, while a violation of the assumption of linkage equilibrium will result in biased estimation and reduced efficiency in linkage mapping. 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 is expected to have greater estimation accuracy and power. Monte Carlo simulations support these theoretical expectations and demonstrate that our new methodology: (1) is more powerful than either FBATs or classic linkage analysis; (2) can unbiasedly estimate genetic parameters regardless of whether association exists, thus remedying the bias and less precision of traditional linkage analysis in the presence of association; and (3) is capable of identifying tight linkage alone. The new approach also 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, the method is numerically stable and computationally efficient, as compared to existing parametric methods that use the simplex algorithm or Newton-type methods to maximize high-order multidimensional likelihood functions.