MODELS FOR THE GENETIC EPIDEMIOLOGY OF CHRONIC DISEASE

描述（由申请人提供）：这是一个项目21-25年的竞争性延续申请，用于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社区。提案的每个方面都涉及PAP（谱系分析包）。参数遗传分析是PAP的主要优势，应该在解决复杂疾病的基因表征和定位所需的多管齐下的方法中发挥作用，这些疾病是由多个基因和环境因素的复杂相互作用造成的。在完成本应用程序中提出的扩展后，非参数链接分析也将在PAP中可用。虽然有许多其他软件包用于分析家庭数据，但没有一个比得上PAP的多功能性。PAP的扩展比开发用于该目的的新软件更有效地生产用于多种遗传分析的软件。此外，人民行动党多年来一直得到良好的支持。在上一个供资期间，开发了一个Java图形用户界面，以便利PAP的使用。在剩余一年的资金支持期间，将完成源代码的转换，从而使PAP非常易于移植和安装，同时保持所有分析的通用性。然而，由于内存和计算机时间的要求，PAP对于多点联动分析仍然是不切实际的。因此，本应用程序建议通过实现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。

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.