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为识别复杂疾病及其途径的疾病易感性位点提供了独特的机会，也为统计分析带来了挑战。高通量测序研究记录的许多基因座非常罕见，无法为统计分析提供足够的依据。对于不相关的病例和对照的研究，已经提出了许多折叠方法。然而，这种方法并不适用于基于家族的研究，这些研究在设计上非常适合稀有变异分析。它们对罕见变异具有更高的统计功效，并且对群体混合具有鲁棒性。对于基于人群的设计，如果变异很少，则不存在调整此类混杂分析的统计方法。然而，为了构建基于家族的设计的折叠方法，必须估计基因座之间的连锁不平衡（LD），这对于罕见变异来说是一项不小的任务。在基于群体的设计中，可以通过利用随机分配表型的排列测试来避免这个问题，但保持受试者的遗传数据固定。这在基于家庭的设计中是不可能的。在本次拨款申请中，我们将开发一种分析方法来解决基于系列的设计中的 LD 估计问题。这将使基于系列的设计构建罕见变体测试成为可能。序列分析的主要目标是 DSL 的识别。单基因座关联测试的重要性由遗传效应大小和等位基因频率定义。由于与真正 DSL 处于 LD 中的非 DSL 可能具有比 DSL 更高的等位基因频率，但观察到的遗传效应大小较小，因此测试的显着性不能用于识别 DSL。为了区分真正的 DSL 和 DSL 中的 LD 中的 SNP，我们将开发统计方法来评估受试者之间跨多个基因座的 LD 模式的差异。这种方法将被提议用于设计不相关的个​​人和基于家庭的研究。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位点，这将有助于更好地了解复杂疾病的途径并最终实现治疗。 公共卫生相关性：测序数据包含识别复杂疾病和表型的因果遗传位点所需的信息。然而，为了将这些丰富的信息转化为疾病位点的发现，需要新的统计分析方法。虽然当前的分析方法仍然有效，但它们并未针对罕见变异和序列数据进行最佳设计。我们将开发统计工具，这些工具能够抵抗罕见变异数据中的混淆，并且能够识别测序数据中疾病位点的位置。这一重要信息将支持寻找疾病途径及其治疗方法。