NOVEL ALGORITHMS FOR GENETIC MAPPING

遗传图谱的新算法

• 批准号: 2209158
• 负责人: Daniel E Weeks
• 金额: $ 22.96万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1996-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2209158
• 关键词: computer program /software; computer system design /evaluation; gender difference; genetic mapping; genetic markers; genetic models; genetic recombination; human data; linkage mapping; parallel processing

Even though map construction using CEPH pedigrees or radiation hybrids can be fairly rapid when marker density is low, it is extremely time consuming to validate the map by using local permutations of loci to judge the likelihood of alternative maps. When marker density is high and when models are used that incorporate genotyping errors, mutations, sex-differences, and interference, current algorithms on serial processors are extremely slow. This is particularly so for constructing consensus maps. Our experience suggests an acute need for improved algorithms, and parallel and incremental algorithms have great promise in this regard. Finally, the dense marker maps generated by the Genome Project will ultimately be used to map loci involved in complex phenotypes. However, mapping complex phenotypes on pedigrees of arbitrary structure involves very difficult computations which will require the development of parallel and incremental algorithms, such as the ones proposed here. Therefore we plan to create new faster computational techniques that will permit more realistic and sophisticated analyses of larger data sets with less time and effort than are now possible. The specific aims are: 1. To develop parallel computational techniques for multilocus linkage analysis and radiation hybrid mapping based on both large and fine grain parallelism. The resulting software will automatically partition the computational tasks of gene mapping across an appropriate selection of computers. 2. To use techniques of incremental processing to reduce the computational overhead for repeated likelihood calculations. 3. To apply the same techniques of parallel and incremental processing as described in Aims 1 and 2 to the locus ordering problem. This will include new parallel algorithms for locus ordering as well as more efficient implementations of well-known heuristic algorithms. 4. To explore genetic chaisma interference using marker data generated by our collaborators. We plan to modify our parallel likelihood algorithms described above to allow for interference. 5. To explore differences in male and female recombination fractions using marker data generated by our collaborators. This analysis may also lead to novel models of sex-differences. 6. To create an accessible national computing resource for geneticists. In the long term, the fast computational tools and the novel algorithms developed here will enable more rapid and accurate map construction and a deeper understanding of interference and recombination in humans.

即使使用Ceph血统或辐射杂种的地图构造 当标记密度较低时，可能会很快 通过使用基因座的本地排列来验证地图 判断替代地图的可能性。 当标记密度高时 当使用结合基因分型误差的模型时，突变时， 性别差异和干扰，串行的当前算法 处理器非常慢。 构建尤其如此 共识图。 我们的经验表明，需要改善的急需 算法，并行和增量算法有很大的希望 在这方面。 最后，基因组产生的致密标记图 项目最终将用于绘制涉及复杂的基因座 表型。 但是，映射复杂表型 任意结构涉及非常困难的计算 需要开发平行和增量算法，例如 这里提出的。 因此，我们计划更快地创建新的 计算技术将允许更现实和 对更大的数据集的精致分析，比时间和精力少于 现在有可能。 具体目的是： 1。开发用于多焦点链接的平行计算技术 分析和辐射杂交映射基于大和细粒 并行性。 由此产生的软件将自动对 基因映射的计算任务，适当选择 计算机。 2。使用增量处理技术来减少 重复可能计算的计算开销。 3。应用相同的平行和增量处理技术 如AIM 1和2中所述的位点排序问题。 这会 包括新的平行算法用于基因座排序以及更多 有效实施著名的启发式算法。 4。使用产生的标记数据探索遗传chaisma干扰 由我们的合作者。 我们计划修改我们的平行可能性 上述算法允许干扰。 5。探索男性和女性重组分数的差异 使用我们的合作者生成的标记数据。 该分析也可能 导致新颖的性别差异模型。 6。为遗传学家创建一个可访问的国家计算资源。 从长远来看，快速计算工具和新算法 此处开发的将使更快，准确的地图构建和 对人类干扰和重组的更深入了解。