Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA Studies

GWA 研究中心理健康表型分析的新统计方法

• 批准号: 8246862
• 负责人: CHRISTOPH LANGE
• 金额: $ 40.66万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-14 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8246862
• 关键词: Address; Admixture; Affect; Alzheimer' s Disease; Applications Grants; Case-Control Studies; Chromosome Mapping; Communities; Complex; Computer software; Data; Data Set; Development; Disease; Disease Pathway; Disease susceptibility; Effectiveness; Family; Gene Frequency; Genetic; Genetic Research; Goals; Grant; Hand; Human Genome; Hybrids; Individual; Laboratories; Lead; Linkage Disequilibrium; Location; Mainstreaming; Maps; Mental Health; Mental disorders; Methodology; Methods; Minor; Pathway interactions; Pattern; Phenotype; Population; Population Analysis; Process; Randomized; Research; Research Personnel; Schizophrenia; Sequence Analysis; Signal Transduction; Techniques; Technology; Testing; Time; Translating; Validation; Variant; base; case control; cost; design; disease phenotype; genetic association; genome wide association study; genome-wide; genotyping technology; large scale production; novel; novel strategies; population based; simulation; success; tool

DESCRIPTION (provided by applicant): The immanent influx of high-throughout sequencing datasets poses both a unique opportunity to identify the disease susceptibility loci for complex disease and their pathways and a challenge in terms of the statistical analysis. Many of the loci that are recorded by high-throughput sequencing studies will be rare, providing insufficient power for the statistical analysis. For studies with unrelated cases and controls, a number of collapsing approaches has been suggested. However, such methodology does not exist for family-based studies which are by design well suited for rare-variant analysis. They have higher statistical power for rare variants and are robust against population admixture. For population-based designs, statistical approaches that adjust the analysis for such confounding do not exist if the variants are rare. However, for the construction of collapsing method for family-based designs, the linkage disequilibrium (LD) between the loci has to be estimated which is a non-trivial task for rare variants. In population-base designs, this issue can be avoid by utilizing permutation tests that randomly assign the phenotype, but keep the genetic data in a subject fixed. This is not possible in family-based designs. In this grant application, we will develop an analytical approach to the LD-estimation problem in family-based designs. This will enable the construction of rare variant tests for family-based designs. The major goal of sequence-analysis is the identification of the DSLs. The significance of single-locus association tests is defined by the genetic effect size and the allele frequency. Since non-DSLs that are in LD with the true DSL can have higher allele frequencies than the DSL, but have smaller, observed genetic effect sizes, the significance of the test cannot be used to identify DSLs. In order to distinguish the true DSLs from SNPs that are in LD with the DSLs, we will develop statistical approaches that assess differences in LD-pattern across multiple loci between subjects are required. Such methodology will be proposed for designs of unrelated individuals and family-based studies. The new analysis approaches will be integrated in our software packages. The new approaches will support the search for disease loci in the human genome which will lead to a better understanding of the pathways for complex diseases and ultimately to their treatment. PUBLIC HEALTH RELEVANCE: Sequencing data contains the information that is needed to identify the causal genetic loci for complex diseases and phenotypes. However, to translate this wealth of information into the discovery of disease loci, novel statistical analysis approaches are required. While the current analysis methodology remains valid, they are not optimally designed to look at rare variants and sequence data. We will develop statistical tools that are robust against confounding in rare variant data and that can identify the locations of the disease loci in sequencing data. This important information will support the search for disease pathways and their cure.

描述（由申请人提供）：高通量测序数据集的内在涌入既是识别疾病易感性基因座的复杂疾病及其途径的独特机会，又是统计分析方面的挑战。通过高通量测序研究记录的许多基因座很少见，为统计分析提供了不足的功率。对于无关病例和对照的研究，已经提出了许多崩溃的方法。但是，这种方法对于基于家庭的研究并不存在，这些研究非常适合稀有变化分析。它们具有较高的稀有变体统计能力，并且对种群混合具有鲁棒性。对于基于人群的设计，如果这种变体很少见，则不存在调整这种混杂分析的统计方法。但是，为了构建基于家庭设计的崩溃方法，必须估计基因座之间的连锁不平衡（LD），这是稀有变体的一项非平凡任务。在人口基础设计中，可以通过利用随机分配表型的置换测试来避免此问题，但将遗传数据保留在固定的受试者中。这在基于家庭的设计中是不可能的。在此赠款应用程序中，我们将在基于家庭的设计中针对LD估计问题开发一种分析方法。这将使基于家庭设计的稀有变体测试构建。序列分析的主要目标是鉴定DSL。单位液关联测试的重要性由遗传效应大小和等位基因频率定义。由于具有真实DSL的LD中的非DSL比DSL具有更高的等位基因频率，但具有较小的，观察到的遗传效应大小，因此不能使用测试的重要性来识别DSL。为了将真正的DSL与DSL中的LD区分开，我们将开发统计方法，以评估受试者之间多个基因座的LD-Pattern差异。这种方法将针对无关的个体和基于家庭的研究的设计提出。新的分析方法将集成到我们的软件包中。新方法将支持在人类基因组中寻找疾病基因座的搜索，这将使人们更好地了解复杂疾病的途径，并最终获得治疗。 公共卫生相关性：测序数据包含确定复杂疾病和表型的因果遗传基因座所需的信息。但是，为了将这些信息转化为疾病基因座的发现，需要采用新颖的统计分析方法。尽管当前的分析方法仍然有效，但它们并非最佳设计来查看稀有变体和序列数据。我们将开发统计工具，这些工具可与稀有变体数据混淆，并可以在测序数据中识别疾病基因座的位置。这些重要信息将支持搜索疾病途径及其治愈方法。