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Genetic Association Studies of Alzheimer Disease Using Multi-Phenotype Tests and Gene-Based Tests

机译：使用多表型测试和基于基因的测试对阿尔茨海默氏病的遗传关联研究

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摘要

The genome-wide association study (GWAS) approach has identified novel loci for a variety of complex diseases. However, for most of these disorder much of the heritability is not explained by this approach, which focuses on identifying common variants that are associated with disease risk. The unexplained heritability may be due to genetic or phenotypic heterogeneity or the influence of rare variants. The motivation behind this thesis was to uncover the unexplained heritability by applying joint analyses of sets of variants (gene-based association test) and multiple disease-related phenotypes (called multivariate gene-based association test). First, we evaluated multivariate gene-based methods for detecting association of common genetic variants with correlated phenotypes. An extensive simulation study showed that the method combining the MultiPhen and GATES software performed best for most tested scenarios especially when correlations among phenotypes are relatively low. We developed a new multivariate gene-based test using rare variants called VEMPHAS. A simulation study using VEMPHAS showed that this method correctly controls for type I error in all tested scenarios. We applied VEMPHAS to analysis of various phenotypes related to Alzheimer disease (AD) and found suggestive association (P < 4.15 x 10--6) with the gene TRIM22, which has been identified in a previous sequencing study of AD onset in PSEN1/2 mutation carriers. We also developed software with a graphical user interface which is designed for integrating information from different types of data sources including genetic data (from GWAS or sequencing), expression data (from RNA-Seq), and protein structures (from protein data banks). This software has several features including 1) testing associations between genetic variants and gene expressions; 2) locating amino acids, encoded by the variants, in a protein structure; and 3) retrieving genetic locations (chromosome and base pair positions) of amino acids of interest in the protein structure. The last feature can be applied for prioritizing coding variants for gene-based association testing. The methods and strategies developed for this dissertation project can effectively uncover a portion of the remaining heritability of complex diseases that is unexplained by traditional GWAS approaches.
机译：全基因组关联研究（GWAS）方法已经确定了各种复杂疾病的新基因座。但是，对于这些疾病中的大多数，这种方法并未解释大部分遗传力，该方法侧重于确定与疾病风险相关的常见变异。无法解释的遗传力可能是由于遗传或表型异质性或稀有变异的影响。本文背后的动机是通过对变体集（基于基因的关联测试）和多种疾病相关表型（称为多变量基于基因的关联测试）进行联合分析来揭示无法解释的遗传力。首先，我们评估了基于多元基因的方法来检测常见遗传变异与相关表型的关联。广泛的模拟研究表明，结合MultiPhen和GATES软件的方法在大多数测试情况下效果最佳，尤其是当表型之间的相关性相对较低时。我们使用称为VEMPHAS的罕见变体开发了一种新的基于多变量基因的测试。使用VEMPHAS进行的仿真研究表明，该方法可以在所有测试场景中正确控制I型错误。我们将VEMPHAS应用于与阿尔茨海默氏病（AD）相关的各种表型的分析，并发现与TRIM22基因具有暗示性关联（P <4.15 x 10--6），该基因已在先前对PSEN1 / 2中AD发作的测序研究中确定。突变携带者。我们还开发了带有图形用户界面的软件，该软件旨在集成来自不同类型数据源的信息，包括遗传数据（来自GWAS或测序），表达数据（来自RNA-Seq）和蛋白质结构（来自蛋白质数据库）。该软件具有以下功能：1）测试遗传变异与基因表达之间的关联； 2）在蛋白质结构中定位由变体编码的氨基酸； 3）检索蛋白质结构中目标氨基酸的遗传位置（染色体和碱基对位置）。最后一个功能可用于为基于基因的关联测试确定编码变体的优先级。为该论文项目开发的方法和策略可以有效地揭示传统GWAS方法无法解释的一部分复杂疾病的遗传力。

著录项

  • 作者

    Chung, Jaeyoon.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Bioinformatics.;Genetics.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 184 p.
  • 总页数 184
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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