MODELS FOR THE GENETIC EPIDEMIOLOGY OF CHRONIC DISEASE

描述（由申请人提供）：这是一个竞争性的延续申请，为期21-25年，该项目旨在开发和实施谱系数据可能性分析的模型和方法，并将所得软件分发给研究界。建议的每个方面都涉及PAP（系谱分析包）。参数遗传分析，PAP的主要优势，应发挥作用，多管齐下的方法需要解决的困难任务的特征和定位基因的复杂疾病，这是由于复杂的相互作用的多个基因和环境因素。在完成本申请中提出的扩展后，非参数连锁分析也将在PAP中可用。虽然有一些其他的软件包用于家庭数据的分析，没有匹配的多功能性的PAP。PAP的扩展比为此目的开发新软件更有效地产生用于多样化遗传分析的软件。此外，人民行动党有着多年来得到良好支持的记录。在上一个供资期间，开发了一个Java图形用户界面，以方便使用PAP。在剩余的一年的资金将完成转换的源代码，从而使PAP非常便携，方便安装，同时保持所有的分析功能。然而，PAP仍然不切实际的多点连锁分析，因为内存和计算机的时间要求。因此，本申请提出通过实施马尔可夫链蒙特卡罗（MCMC）方法来分析多位点标记数据来弥补这一缺点。 MCMC方法的样本，而不是详尽列举，可能的组合基因型在一个谱系，因此影响了巨大的节省计算机时间。随着MCMC产生的概率的可用性，多点连锁分析对于方差分量模型或使用PAP中已有的任何广泛选择的主要基因模型都将变得可行。然而，本申请还提出开发新的疾病风险遗传模型，包括无遗传模式的方法。此外，本申请还建议实施变更，以提高计算速度，允许数据探索，并促进数据处理和分析，同时继续分发和支持PAP。

DESCRIPTION (provided by applicant): This is a competing continuation application for years 21-25 of a project to develop and implement models and methodology for the likelihood analysis of pedigree data and to distribute the resultant software to the research community. Each aspect of the proposal involves PAP (Pedigree Analysis Package). Parametric genetic analysis, the primary strength of PAP, should play a role in the multi-pronged approach needed to tackle the difficult task of characterizing and localizing genes for complex diseases, which result from complicated interactions of multiple genes and environmental factors. Non-parametric linkage analysis will also become available in PAP upon completion of extensions proposed in this application. Although there exist a number of other software packages for the analysis of family data, none matches the versatility of PAP. The extension of PAP more efficiently produces software for diverse genetic analysis than does the development of new software for that purpose. In addition, PAP has a track record for having been wellsupported over the years. During the previous funding period, a graphical user interface in Java was developed to facilitate the use of PAP. During the remaining year of funding the conversion of the source code will be completed, thereby making PAP very portable and convenient to install while maintaining all the analysis versatility. Nevertheless, PAP remains impractical for multipoint linkage analysis, because of the memory and computer time requirements. Therefore, this application proposes to remedy that shortcoming by implementing Markov chain Monte Carlo (MCMC) methods for the analysis of multi-locus marker data. MCMC methods sample, rather than exhaustively enumerate, the possible combinations of genotypes within a pedigree, therefore effecting an enormous saving of computer time. With the availability of MCMCproduced probabilities, multipoint linkage analysis will become feasible for either variance components models or using any of the extensive selection of major gene models already available in PAP. Nevertheless, the current application also proposes to develop new genetic models of disease risk, including a mode-of-inheritance free method. In addition, this application proposes to implement changes to increase computational speed, allow data exploration, and facilitate data handling and analysis, while continuing to distribute and support PAP.