Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA Studies

描述(申请人提供)：高通量测序数据集的内在涌入为确定复杂疾病的疾病易感基因及其途径提供了独特的机会，但在统计分析方面也是一个挑战。通过高通量测序研究记录的许多基因座将是罕见的，为统计分析提供的动力不足。对于与病例和对照无关的研究，已经提出了一些崩溃的方法。然而，这种方法并不适用于基于家庭的研究，这些研究的设计非常适合于稀有变异分析。它们对稀有变异具有更高的统计能力，并且对种群混合具有健壮性。对于基于总体的设计，如果变异很少，则不存在调整此类混淆分析的统计方法。然而，对于基于家族的设计的折叠方法的构建，必须估计基因座之间的连锁不平衡(LD)，这对于罕见的变种来说不是一项微不足道的任务。在基于群体的设计中，这个问题可以通过使用排列测试来避免，这种测试随机分配表型，但保持受试者中的基因数据固定。这在基于族的设计中是不可能的。在这项拨款申请中，我们将开发一种分析方法来解决基于家庭的设计中的LD估计问题。这将使基于家庭的设计能够构建罕见的变型测试。序列分析的主要目标是识别DSL。单基因座关联检验的意义由遗传效应大小和等位基因频率决定。由于与真正的DSL处于LD中的非DSL的等位基因频率可能高于DSL，但具有较小的观察到的遗传效应大小，因此该测试的重要性不能用于识别DSL。为了区分真实的DSLs和患有DSLs的SNPs，我们将开发统计方法来评估受试者之间多个基因座上LD模式的差异。这种方法将被建议用于无关个体的设计和基于家庭的研究。新的分析方法将整合到我们的软件包中。新的方法将支持在人类基因组中寻找疾病位点，这将导致更好地了解复杂疾病的途径，并最终达到治疗的目的。 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位点所需的信息。然而，为了将这些丰富的信息转化为疾病部位的发现，需要新的统计分析方法。虽然目前的分析方法仍然有效，但它们并没有优化设计来研究罕见的变异和序列数据。我们将开发统计工具，这些工具对稀有变异数据的混淆具有很强的抵抗力，并可以在测序数据中确定疾病基因座的位置。这一重要信息将为寻找疾病途径及其治疗提供支持。

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.