Selection and Association in Coalescent Genealogies

描述(申请人提供)：这项提案开发了软件工具，使用马尔可夫链蒙特卡罗(MCMC)最大似然方法从遗传数据中推断种群参数。我们特别专注于推断选择，并将已知性状和未知的选择影响映射到特定的染色体区域。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度，包括比较选择假说的统计检验。 (2)相对于单倍型上的标记，定位测量到的性状或选择效应的位置。 (3)从MCMC算法产生的大样本家谱中挖掘重组热点的位置、疾病基因突变等重大事件发生的时间以及迁移、突变和重组事件的总体时间分布等信息。 (4)通过更好的搜索策略和多个子集的使用来提高MCMC算法的性能 并行的计算机。 (5)结合序列样本(取自不同时期的种群样本)的分析，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，并将使用真实数据和模拟数据对其进行测试。

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.