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Advances in Pedigree Analysis: Hardy-Weinberg Equilibrium, Strain Imputation, and Maternal Effects.

机译：谱系分析的进展：Hardy-Weinberg平衡，应变归因和母体效应。

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摘要

Genetic studies usually gather participants in one of the three ways: random samples, cases and controls, and pedigrees. Pedigree analysis is computationally demanding and, with the passing of HIPAA, pedigrees are difficult to collect. For these reasons researchers currently favor cases and controls and random samples over pedigrees. However, pedigrees take advantage of familial relationships and vertical inheritance patterns that can avoid some of the confounding and variance inflation that arise when population substructure is present. Pedigree analysis can also test for both linkage and association. In this dissertation I propose to expand the utility of pedigree analysis in three ways: (a) Hardy-Weinberg testing for pedigrees, (b) association testing using imputed strain origins in animals crosses with inbred strains, and (c) testing for prenatal effects using variance component models.;Typically Hardy-Weinberg equilibrium is tested in unrelated individuals using a chi² goodness-of-fit test that compares expected and observed numbers of across genotypes. In this dissertation, a likelihood ratio test for Hardy-Weinberg equilibrium that accommodates a mixture of pedigree and random sample data is proposed. The heterozygous-homozygous test accommodates markers with dominant and recessive alleles and handles the phase ambiguities encountered in combining several linked SNPs (single nucleotide polymorphisms) into a single supermarker. My experience analyzing real and simulated data suggests that the heterozygous-homozygous test has good power and type-one error rates.;Mapping quantitative traits in inbred strains is often simpler than mapping the analogous traits in humans. One of the drawbacks of standard inbred crosses is their reduced genetic diversity. Multiple crosses circumvent these difficulties, but raise substantial computational difficulties. I present a good method for locally imputing the strain origins of each genotyped organism along its genome. Pedigree structure if available can guide imputation. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. A dynamic programming algorithm solves the strain imputation process in one quick pass through the genome of a progeny. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data.;Maternally inherited effects, prenatal effects, and postnatal effects are confounded in traditional family studies. It turns out that these effects can be disentangled by studying families containing children conceived by assisted reproductive technologies (ART). I develop a variance component model to capture these effects. This model is flexible enough to allow any number of family members and degrees of relationship; thus researchers can use both small and extended families simultaneously. Simulations demonstrate that the method has appropriate statistical properties and is robust to model misspecification and missing data.
机译：遗传研究通常以以下三种方式之一收集参与者：随机样本，病例和对照以及系谱。谱系分析对计算要求很高，并且随着HIPAA的通过，谱系很难收集。由于这些原因，研究人员目前更喜欢病例和对照以及随机样本而不是谱系。但是，谱系利用家族关系和垂直继承模式可以避免出现人口子结构时出现的一些混淆和方差膨胀。家谱分析还可以测试链接和关联。在本文中，我建议以三种方式扩展谱系分析的用途：（a）对谱系进行Hardy-Weinberg测试，（b）使用与自交系杂交的动物推算的菌株起源进行关联测试，以及（c）测试产前效应典型地，在不相关的个​​体中，使用chi²拟合优度检验测试了Hardy-Weinberg平衡，该检验比较了预期和观察到的不同基因型数量。本文提出了一种适合家谱和随机样本数据混合的Hardy-Weinberg平衡似然比检验方法。杂合-纯合测试可容纳具有显性和隐性等位基因的标记，并处理将多个连接的SNP（单核苷酸多态性）组合为单个超级标记时遇到的相位模糊性。我对真实数据和模拟数据进行分析的经验表明，杂合-纯合测试具有良好的功效和一型错误率。在近交菌株中映射数量性状通常比绘制人类相似性状更容易。标准近交杂交的缺点之一是遗传多样性降低。多重交叉规避了这些困难，但带来了实质性的计算困难。我提出了一种沿基因组局部估算每个基因型生物的菌株起源的好方法。家谱结构（如果有）可以指导估算。然后，推算出的原点在多变量高斯模型中用作均值效应，以测试性状水平与局部基因组变异之间的关联。一种动态编程算法可快速遍历子代的基因组，从而解决了应变估算过程。在实际示例中，估算精度超过99％，并可以在模拟和真实数据中进行高分辨率映射。传统的家庭研究将母体遗传效应，产前效应和产后效应混为一谈。事实证明，可以通过研究包含由辅助生殖技术（ART）孕育出的孩子的家庭来消除这些影响。我开发了一个方差成分模型来捕获这些影响。这种模式足够灵活，可以允许任何数量的家庭成员和各种关系。因此研究人员可以同时使用小型家庭和大家庭。仿真表明，该方法具有适当的统计属性，并且对于模型错误指定和数据丢失具有鲁棒性。

著录项

  • 作者

    Zhou, Jin.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Biology Biostatistics.;Biology Genetics.;Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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