Selection and Association in Coalescent Genealogies

合并谱系中的选择和关联

• 批准号: 6678518
• 负责人: Mary K Kuhner
• 金额: $ 45.45万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-01-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6678518
• 关键词: autosomal dominant trait; biochemical evolution; biotechnology; computer assisted sequence analysis; computer program /software; computer system design /evaluation; gene frequency; gene mutation; genetic mapping; genetic markers; genetic recombination; mathematical model; method development; model design /development; natural selections; nucleic acid sequence; population genetics; quantitative trait loci; statistics /biometry

DESCRIPTION (provided by applicant): This proposal develops software tools, which use Markov Chain Monte Carlo (MCMC) maximum-likelihood methods to infer population parameters from genetic data. We focus specifically on inferring selection and on mapping both known traits and unknown selective influences to specific chromosomal regions. We will develop new techniques for the following: (1) Estimating the presence and strength of natural selection and the degree of dominance, including statistical tests to compare selection hypotheses. (2) Mapping the location of a measured trait, or of a selection effect, relative to markers on a haplotype. (3) Mining the large sample of genealogies produced by MCMC algorithms for information such as the location of recombination hotspots, the time of significant events such as disease-locus mutations, and the overall time distribution of migration, mutation and recombination events. (4) Improving performance of MCMC algorithms via better search strategies and use of multiple computers in parallel. (5) Incorporating analysis of serial samples (samples taken from a population at different times) in order to strengthen estimation of selection and population growth. We will create freely distributed software implementing these methods and will test them using real and simulated data.

描述（由申请人提供）：该提案开发了软件工具，使用Markov Chain Monte Carlo（MCMC）最大样子方法从遗传数据中推断人口参数。我们专门致力于推断选择和映射已知的性状和未知的选择性影响到特定的染色体区域。我们将开发以下新技术： （1）估计自然选择的存在和强度和优势程度，包括统计检验以比较选择假设。 （2）映射相对于单倍型上的标记的测量性状或选择效果的位置。 （3）挖掘由MCMC算法生成的大量家谱，以获取诸如重组热点的位置，重大事件的时间，例如疾病 - 局部性突变以及迁移，突变和重组事件的总体时间分布。 （4）通过更好的搜索策略和使用多个，提高MCMC算法的性能 并行计算机。 （5）为了加强选择和人口增长的估计，结合了串行样本分析（从不同时间采集的样品）。 我们将创建实施这些方法的自由分布式软件，并将使用真实和模拟数据测试它们。