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）：该提案开发软件工具，使用马尔可夫链蒙特卡罗（MCMC）最大似然方法从遗传数据推断群体参数。我们专注于推断选择和映射已知的性状和未知的选择性影响特定的染色体区域。我们将为以下方面开发新技术： (1)估计自然选择的存在和强度以及优势程度，包括比较选择假设的统计检验。 (2)定位：相对于单倍型上的标记，定位测量的性状或选择效应的位置。 (3)挖掘MCMC算法产生的大样本系谱信息，如重组热点的位置，重大事件（如疾病位点突变）的时间，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 计算机并行 (5)对系列样本（在不同时间从种群中采集的样本）进行重复分析，以加强对选择和种群增长的估计。 我们将创建实现这些方法的免费分发软件，并使用真实的和模拟数据对其进行测试。