Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA Studies

描述（由申请人提供）：高通量测序数据集的迅速涌入既为鉴定复杂疾病的疾病易感性基因座及其途径提供了独特的机会，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将是罕见的，为统计分析提供的功效不足。对于不相关的病例和对照的研究，已经提出了一些崩溃的方法。然而，这种方法不存在于基于家族的研究中，这些研究的设计非常适合于罕见变异分析。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，如果变异罕见，则不存在针对此类混杂因素调整分析的统计方法。然而，为了构建基于家系设计的折叠方法，必须估计位点之间的连锁不平衡（LD），这对于罕见变异来说是一项重要的任务。在基于群体的设计中，这个问题可以通过利用随机分配表型的排列测试来避免，但保持受试者的遗传数据固定。这在基于族的设计中是不可能的。在这项资助申请中，我们将开发一种分析方法来解决基于家族的设计中的LD估计问题。这将使罕见的变异测试的家庭为基础的设计建设。序列分析的主要目的是鉴定DSL。单位点关联检验的显着性由遗传效应大小和等位基因频率定义。由于与真正DSL在LD中的非DSL可以具有比DSL更高的等位基因频率，但是具有更小的观察到的遗传效应大小，因此测试的显著性不能用于识别DSL。为了区分真正的DSL与具有DSL的LD中的SNP，我们将开发评估受试者之间多个基因座的LD模式差异的统计方法。这种方法将被提议用于无关个体和基于家族的研究的设计。新的分析方法将集成到我们的软件包中。这些新方法将支持在人类基因组中寻找疾病位点，从而更好地了解复杂疾病的途径，并最终治疗这些疾病。

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.