Selection and Association in Coalescent Genealogies

合并谱系中的选择和关联

• 批准号: 6773813
• 负责人: Mary K Kuhner
• 金额: $ 46.77万
• 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/6773813
• 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）：本提案开发软件工具，使用马尔可夫链蒙特卡罗（MCMC）最大似然方法从遗传数据中推断种群参数。我们专注于推断选择，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。我们将开发以下方面的新技术：