MODELS FOR THE GENETIC EPIDEMIOLOGY OF CHRONIC DISEASE

说明(由申请人提供)：这是一个21-25年项目的竞争性延续申请，目的是开发和实施系谱数据可能性分析的模型和方法，并将结果软件分发给研究界。提案的每一个方面都涉及PAP(系谱分析包)。参数遗传分析是PAP的主要优势，它应该在多管齐下的方法中发挥作用，以解决复杂疾病的基因特征和定位的困难任务，这些复杂疾病是多基因和环境因素复杂相互作用的结果。在完成本申请中提出的扩展后，非参数链接分析也将在PAP中可用。尽管有许多其他用于家庭数据分析的软件包，但没有一个能与PAP的多功能性相提并论。与为此目的开发新软件相比，PAP的扩展更有效地产生用于多样化基因分析的软件。此外，人民行动党多年来一直得到很好的支持，这是有记录的。在上一个供资期间，开发了一个Java图形用户界面，以方便PAP的使用。在剩余的供资年期间，将完成源代码的转换，从而使PAP非常便携和便于安装，同时保持所有分析的多功能性。然而，由于内存和计算机时间的要求，PAP对于多点连锁分析仍然是不切实际的。因此，本申请建议通过实施马尔科夫链蒙特卡罗(MCMC)方法来分析多基因座标记数据，以弥补这一缺陷。 MCMC方法对谱系内可能的基因组合进行抽样，而不是详尽地列举，因此极大地节省了计算机时间。随着MCMC产生的概率的出现，多点连锁分析将变得可行，可以用于方差分量模型或使用PAP中已有的大量主基因模型中的任何一个。然而，目前的申请还建议开发新的疾病风险遗传模型，包括一种无遗传模式的方法。此外，该应用程序建议实施更改以提高计算速度，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.