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Modeling genetic correlation in microsatellite frequencies associated with covariates and population substructure.

机译:在与协变量和种群子结构相关的微卫星频率中建立遗传相关性模型。

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摘要

The survival of an endangered species can depend on how accurately the population structure of that species is identified. By determining the substructuring of a species, wise management can be facilitated. A popular tool for the detection and estimation of population structure is information extracted from genetic code.; In this dissertation I develop a new method that models genetic correlation structure and relates it to a covariate. Two sources of structure are isolated: (1) substructuring of a population into genetically distinct substocks, and (2) genetic correlation within a sub stock corresponding to a measurable covariate. My modeling approach is based on match probabilities for different types of allele pairs, and on marginalization of a beta-binomial probability model. My statistical model can be fit by adapting GAM fitting methodologies. Hypotheses can be tested using permutation methods.; In order to evaluate the performance of my method I examine diverse simulations. I consider both one-population and two-population cases. In the one-population case, within-stock correlation attributable to a covariate is the influential factor, while for the two-population case both within-substock correlation and population substructuring can be detected. In these studies, I analyze the influence of various simulation parameters and compare the performance of my method with other related methods. Generally, my method is shown to have good power to detect all but the tiniest effect sizes in datasets limited to a small number of loci and samples.; I also examine the performance of my method by applying it to real data. The two examples I consider pertain to the Bering-Chukchi-Beaufort Seas stock of bowhead whales and to black-tailed prairie dogs living in northern Colorado. In both cases application of my method is found to corroborate results from previous research.; The dissertation concludes with a discussion of some of the strengths and weaknesses of my approach, and some consideration of potential future research.
机译:濒临灭绝物种的生存可能取决于该物种的种群结构鉴定的准确性。通过确定物种的亚结构,可以促进明智的管理。用于检测和估计种群结构的一种流行工具是从遗传密码中提取的信息。在本文中,我开发了一种新的方法来对遗传相关结构进行建模并将其与协变量相关联。结构的两个来源是隔离的:(1)将种群分为遗传上不同的子种群,(2)子种群内与可测协变量相对应的遗传相关性。我的建模方法基于不同类型等位基因对的匹配概率,以及基于β二项式概率模型的边际化。我的统计模型可以通过调整GAM拟合方法来拟合。假设可以使用置换方法进行测试。为了评估我的方法的性能,我研究了各种模拟。我考虑一人一案和两人一案。在单种群情况下,可归因于协变量的种群内相关性是影响因素,而在两种群情况下,子种群内相关性和种群子结构均可检测。在这些研究中,我分析了各种仿真参数的影响,并将本方法与其他相关方法的性能进行了比较。通常,我的方法被证明具有很好的检测能力,可以检测到数据集中除最小位点和样本外的最小效应量。我还将方法应用于实际数据来检查其性能。我认为这两个例子是关于白头鲸,白鲸,波弗特海的弓头鲸种群以及居住在科罗拉多州北部的黑尾土拨鼠。在这两种情况下,都发现我的方法的应用证实了先前研究的结果。论文最后讨论了我的方法的优点和缺点,并考虑了潜在的未来研究。

著录项

  • 作者

    Ozaksoy, Isin.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Biology Genetics.; Statistics.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 141 p.
  • 总页数 141
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遗传学;统计学;
  • 关键词

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