IMPROVEMENT OF MAPPING ACCURACY BY UNIFYING LINKAGE AND ASSOCIATION ANALYSIS

通过统一链接和关联分析来提高绘图精度

• 批准号: 7420645
• 负责人: MING LI
• 金额: $ 1.48万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7420645
• 关键词: bias; family; family genetics; genetics

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.

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。子弹和调查员（PI）可能已经从其他NIH来源获得了主要资金，因此可以在其他清晰的条目中代表。列出的机构适用于该中心，这不一定是调查员的机构。众所周知，附近基因座等位基因创始人单倍型的共存以及从父母到后代的共晶的共存信息，这些信息揭示了遗传结构的不同但互补的特征。假定连锁平衡或基于家庭的关联测试（FBAT）的常规连锁分析仅捕获部分信息，从而导致效率低下。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联系，而违反链接平衡的假设将导致估计估计和链接映射的效率降低。使用数据增强技术和EM算法，我们开发了一种基于似然的方法，该方法将链接和关联分析嵌入到统一的一般谱系数据框架中。相对于链接或关联分析，预计所提出的方法具有更大的估计准确性和功率。蒙特卡洛模拟支持这些理论期望，并证明我们的新方法：（1）比FBAT或经典的链接分析更强大； （2）无论关联是否存在，都可以公然估计遗传参数，从而弥补存在在缔合的情况下传统连锁分析的偏见和较少的精度； （3）能够单独识别紧密的联系。新方法还具有理论上的优势，即它可以最大程度地提取统计信息，从而提高映射准确性和功率，因为​​它将基于多焦点的种群的关联和基于谱系的链接分析整合到连贯的框架中。此外，与现有的参数方法相比，该方法在数值上是稳定且在计算上有效的，这些方法使用了使用单纯算法或牛顿型方法来最大程度地提高高阶多维可能性函数。