A Scoring Method for MCMC Linkage Analysis

描述（由申请人提供）： 贝叶斯蒙特卡罗马尔可夫链（MCMC）技术在解剖复杂的遗传性状，如癌症方面显示出了希望。癌症是复杂的，因为不止一个基因的失败被认为是导致疾病所必需的，并且遗传遗传因素和环境因素都起作用。Heath（1997）提出并在程序Loki中实现的方法已经能够在真实的和模拟数据集中定位对复杂性状有贡献的基因。Loki进行同步分离和连锁分析，不仅估计数量性状位点（QTL）的位置，还估计许多其他参数，包括QTL的数量、每个QTL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分布，在过去，我们一直专注于QTL连锁在基因组或简单的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其重要性一直很困难，这意味着许多人发现这些方法难以使用，而且尚未充分利用所有估计的信息。 我们建议检查一个评分方法，以产生一个易于解释的分数为初始QTL连锁。这个分数，后验概率比（LOP）的对数，专门设计用于复杂的寡基因性状连锁检测。LOP与lod得分的对比在于，虽然lod得分是在单连锁模型下计算的，但LOP是在大量模型上用蒙特卡洛积分计算的。我们已经在LOP上做了一些非常有前途的概念验证工作，但是需要进一步的研究来完全探索LOP的属性。我们计划探索我们目前的实现如何与不同的家庭结构和不同的特质模型执行此评分。这种探索将导致研究设计的指导方针和结果解释的规则，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，我们相信这将导致更多的疾病相关基因的识别。

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.