A Scoring Method for MCMC Linkage Analysis

MCMC连锁分析的评分方法

• 批准号: 6804053
• 负责人: EDWARD Warwick DAW
• 金额: $ 7.55万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-26 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6804053
• 关键词: computer simulation; environmental exposure; family genetics; functional /structural genomics; genetic models; genetic regulatory element; human data; linkage mapping; lung neoplasms; method development; neoplasm /cancer genetics; phenotype; quantitative trait loci; statistics /biometry

DESCRIPTION (provided by applicant): Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits such as cancer. Cancer is complex both in that failures of more than a single gene are thought to be required to lead to disease and in that both inherited genetic factors and environmental factors play a role. The methods introduced by Heath (1997) and implemented in the program Loki have been able to localize genes contributing to complex traits in both real and simulated data sets. Loki carries out a simultaneous segregation and linkage analysis, estimating not only the location of quantitative trait loci (QTL), but also many other parameters, including the number of QTL, the effects at each QTL, covariate effects (such as environmental exposure), and the segregation patterns of those QTL. These methods can produce posterior probability, distributions for all estimated parameters, and in the past we have focused on the posterior probability distribution of QTL linkage over the genome or simply a particular chromosome to identify regions in which QTL are located. Interpretation of the results of these methods and assessment of their significance has been difficult, meaning that many have found these methods difficult to use and full use of all the information estimated has not been made. We propose to examine a scoring method to produce an easy to interpret score for initial QTL linkage. This score, the Log Of the Posterior placement probability ratio (LOP), designed specifically for complex oligogenic trait linkage detection. LOP contrasts with a lod score in that while a lod score is calculated under a single linkage model, LOP is calculated with Monte Carlo integration over a large number of models. We have done some very promising proof-of-concept work on LOP, but further study is required to completely explore the properties of LOP. We plan to explore how our current implementations of this score perform with different family structures and different trait models. This exploration will result in guidelines for study design and rules for the interpretation of the results, as well as ideas for further improvement of the statistical methods. These guidelines will be used in future studies, which we believe will lead to the identification of additional disease-related genes.

描述(由申请人提供)： 贝叶斯蒙特卡罗马尔可夫链(MCMC)技术在分析癌症等复杂遗传特征方面显示出了良好的前景。癌症是复杂的，因为不止一个基因的缺失被认为是导致疾病的原因，而且遗传因素和环境因素都起到了作用。Heath(1997)介绍的方法和在程序Loki中实现的方法已经能够在真实和模拟的数据集中定位影响复杂性状的基因。LOKI同时进行分离和连锁分析，不仅估计了数量性状座位(QTL)的位置，还估计了许多其他参数，包括QTL的数量、在每个QTL上的效应、协变量效应(如环境暴露)以及这些QTL的分离模式。这些方法可以产生所有估计参数的后验概率和分布，在过去，我们关注的是QTL连锁在基因组或特定染色体上的后验概率分布，以确定QTL所在的区域。解释这些方法的结果和评估它们的意义一直很困难，这意味着许多人发现这些方法很难使用，而且没有充分利用估计的所有信息。 我们建议研究一种评分方法，以产生初始QTL连锁的易于解释的评分。这个分数，后置概率比(LOP)的对数，专门为复杂的寡基因性状连锁检测而设计。LOP与LOD得分的不同之处在于，LOD得分是在单个链接模型下计算的，而LOP是在大量模型上用蒙特卡罗积分计算的。我们已经在LOP上做了一些非常有前景的概念验证工作，但要完全探索LOP的性质还需要进一步的研究。我们计划探索在不同的家庭结构和不同的特质模式下，我们目前实施的这一评分是如何表现的。这一探索将产生研究设计的指导方针和解释结果的规则，以及进一步改进统计方法的想法。这些指南将用于未来的研究，我们相信这将导致识别更多与疾病相关的基因。