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Statistical methods for genetic association mapping of complex traits with related individuals.

机译：与相关个体进行的复杂性状遗传关联映射的统计方法。

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摘要

We develop statistical methods to address both dependent and partially-observed data and apply these methods to problems in haplotype-based association analysis of complex traits in related individuals. We consider a general setting in which the complete data are dependent with marginal distributions following a generalized linear model. We form a vector Z whose elements are conditional expectations of the elements of the complete-data vector, given selected functions of the incomplete data. Assuming that the covariance matrix of Z is available, we form an optimal linear estimating function based on Z, which we solve by an iterative method. This approach allows us to address key difficulties in the haplotype frequency estimation and testing problems in related individuals: (1) dependence that is known but can be complicated; (2) data that are incomplete for structural reasons, as well as possibly missing, with different amounts of information for different observations; (3) the need for computational speed in order to analyze large numbers of markers; (4) a well-established null model, but an alternative model that is unknown and is problematic to fully specify in related individuals. We apply the method to test for association of haplotypes with alcoholism in the GAW 14 COGA data set.;We then address the problem of testing for association with untyped variants. Current genome-wide association studies only genotype a subset of all genomic variants. However, many of the untyped variants can be well-predicted from typed variants, with information on the joint distribution of typed and untyped variants available from an external reference panel such as HapMap. Incorporation of such external information can allow one to perform tests of association between untyped variants and phenotype, thereby making more efficient use of the available genotype data. We introduce a new method, ATRIUM, to test for association between untyped SNPs and phenotype, based on genome screen data in case-control samples in which some individuals are related. ATRIUM uses information from an external reference panel to specify a direction in which to perform a 1-df test of association with an untyped SNP. It properly accounts for uncertainty in haplotype information based on unphased genotype data and for dependence of genotypes across related individuals. We demonstrate that ATRIUM is robust in that it maintains the nominal type I error rate even when the external reference sample is not well-matched to the case-control sample. We apply the method to detect association between type 2 diabetes and variants on chromosome 10 in the Framingham SHARe data.
机译：我们开发了统计方法来处理依赖和部分观察的数据，并将这些方法应用于相关个体中基于单体型的复杂性状的关联分析中的问题。我们考虑一个通用设置，在该通用设置中，完整数据依赖于遵循广义线性模型的边际分布。给定不完整数据的选定函数，我们形成向量Z，其元素是完整数据向量元素的条件期望。假设Z的协方差矩阵可用，我们形成基于Z的最优线性估计函数，并通过迭代方法求解。这种方法使我们能够解决相关个体在单体型频率估计和测试中的关键难题：（1）已知但可能很复杂的依赖性； （2）由于结构原因而不完整的数据，以及可能丢失的数据，且针对不同观测值的信息量不同； （3）为了分析大量标记而需要计算速度； （4）公认的空模型，但是未知的替代模型，很难在相关个人中完全指定。我们在GAW 14 COGA数据集中应用该方法来测试单倍型与酒精中毒的关联。然后，我们解决了与未类型化变体关联的测试问题。当前的全基因组关联研究仅对所有基因组变异的子集进行基因分型。但是，许多类型错误的变量都可以从类型变量中得到很好的预测，而且可以从外部参考面板（例如HapMap）中获得有关类型变量和非类型变量的联合分布的信息。掺入这种外部信息可以允许人们测试未类型的变体和表型之间的关联，从而更有效地利用可用的基因型数据。我们引入一种新方法ATRIUM，根据与某些个体相关的病例对照样本中的基因组筛查数据，测试未分型的SNP与表型之间的关联。 ATRIUM使用来自外部参考面板的信息来指定执行与无类型SNP关联的1-df测试的方向。它适当地考虑了基于非分阶段基因型数据的单倍型信息的不确定性以及相关个体之间基因型的依赖性。我们证明了ATRIUM的强大之处在于，即使外部参考样本与病例对照样本不完全匹配，它也可以保持I型标称错误率。我们应用该方法来检测2型糖尿病与Framingham SHARe数据中10号染色体上的变异之间的关联。

著录项

  • 作者

    Wang, Zuoheng.;

  • 作者单位

    The University of Chicago.;

  • 授予单位 The University of Chicago.;
  • 学科 Biology Biostatistics.;Statistics.;Biology Genetics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 88 p.
  • 总页数 88
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;遗传学;统计学;
  • 关键词

  • 入库时间 2022-08-17 11:37:43

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