IMPROVEMENT OF MAPPING ACCURACY BY UNIFYING LINKAGE AND ASSOCIATION ANALYSIS

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。众所周知，系谱/家系数据记录了在邻近基因座上等位基因的创始单倍型共存的信息，以及从亲本到后代的共传递，这些信息揭示了遗传结构的不同但互补的概况。无论是传统的连锁分析，假设连锁平衡或家庭为基础的关联测试（FBAT）只捕捉部分信息，导致效率低下。例如，在不存在等位基因关联的情况下，FBAT将不能检测甚至非常紧密的连锁，而违反连锁平衡的假设将导致有偏差的估计和连锁作图的效率降低。使用数据增强技术和EM算法，我们开发了一种基于可能性的方法，该方法将连锁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，所提出的方法预计具有更高的估计准确性和功效。Monte Carlo模拟支持这些理论预期，并表明我们的新方法：（1）比FBAT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关联存在时的偏差和较低的精度;（3）能够单独识别紧密连锁。新方法还具有理论优势，它可以最大限度地提取统计信息，从而提高定位精度和功率，因为它集成了基于多位点群体的关联和基于家系的连锁分析到一个连贯的框架。此外，该方法是数值稳定和计算效率，相比现有的参数化方法，使用单纯形算法或牛顿型方法，以最大限度地提高高阶多维似然函数。

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.