Statistical inference in genome-wide association and sequencing studies

描述（由申请人提供）：尽管全基因组关联研究成功地鉴定了与常见和复杂疾病相关的数百个基因座，但在这些高维数据中的统计推断仍然存在重大挑战。具体而言，新兴的全基因组测序研究产生的罕见变异可能解释了“缺失的遗传力”，但对传统的逐基因座方法提出了挑战。基因-环境相互作用的研究并没有取得很多成功，可能是由于现有分析方法的局限性。中间结果介导的遗传效应是一个新兴的感兴趣的话题，可能会导致疾病的预防或治疗。然而，现有的统计方法推断调解效果，一直不发达。 在本提案中，我们计划建立新的统计方法来应对这些挑战。方法学研究的动机是，但不限于，全基因组关联研究和妇女健康倡议（WHI）的测序项目，包括“基因组学和随机试验网络”（GARNET），“基因和环境的人口结构”（PAGE）和“外显子组测序项目”（ESP）。本提案的特点是PI和合作研究者确实正在进行这些研究，因此，将立即应用所提出的方法创新来解决感兴趣的科学问题。 最近提出了一些用于罕见变异分析的统计方法。现有的方法都没有考虑到中性变体的存在，即，对性状没有功能影响的等位基因。在上述基因组测试中包含中性变体肯定会削弱效力。在这个提议中，我们提出了一类有限混合模型，它显式地梳理出中性变体以提高功效。 识别基因-环境相互作用的主要挑战是由于样本量有限和相互作用的幅度通常较小而缺乏功效。降维，如基于基因集的推理，是减少假设检验和丰富弱遗传效应的关键。我们将开发一套基于基因集的两阶段过滤程序，用于检测基因与环境的相互作用。我们还将开发一个具有L1罚分的多变量稀疏基因集测试框架，以在基因或途径中组装弱遗传效应。 通过中间结果推断遗传效应对疾病的介导作用的困难在于如何控制未知的混杂因素。目前的方法利用“孟德尔随机化”，等位基因的随机分离，并使用已知的遗传风险等位基因作为工具变量来推断因果关系。现有框架的局限性，主要是过度限制性的假设和无法模拟二元结果的因果关系，阻碍了这种推断的适用性。我们将修改最初在计量经济学中发展的工具变量框架，以更好地适应遗传研究。

DESCRIPTION (provided by applicant): Despite the success of genome-wide association studies to identify over hundreds of loci that are associated with common and complex diseases, significant challenges remain for statistical inference in these high- dimensional data. Specifically, rare variants generated by emerging genome-wide sequencing studies may explain the "missing heritability", but pose a challenge to the traditional locus-by-locus approach. Studies of gene-environment interactions have not generated many successes, possibly due to limitations of existing analytical methods. Mediation of genetic effects by intermediate outcomes is an emerging topic of interest that may lead to disease prevention or treatment. The existing statistical methods for inferring mediation effect, however, have been underdeveloped. In this proposal, we plan to build novel statistical methods to address these challenges. The methodological research is motivated by, but not limited to, the genome-wide association studies and the sequencing project in the Women's Health Initiative (WHI), including the "Genomics and Randomized Trials Network" (GARNET), "Population Architecture of Genes and Environment" (PAGE) and the "Exome Sequencing Project" (ESP). The feature of this proposal is that the PI and co-investigators are indeed conducting these studies, thus methodological innovations proposed will be applied immediately to address scientific questions of interest. A number of statistical methods for rare variant analysis have been proposed recently. None of the existing methods accounts for the presence of neutral variants, i.e., alleles which do not have functional influence on the trait. Inclusion of neutral variants in the aforementioned gene-set tests certainly dilutes power. In this proposal, we propose a class of finite mixture models that explicitly teases out neutral variants to improve power. The main challenge in identifying gene-environment interactions is lack of power due to limited sample size and typically small magnitude of interactions. Dimension reduction, such as gene-set based inference, is critical to reduce the amount of hypothesis tests and enrich weak genetic effects. We will develop a suite of gene-set based, two-stage filtering procedures for detecting gene-environment interaction. We will also develop a multivariate sparse gene-set testing framework with a L1 penalty to assemble weak genetic effects in a gene or a pathway. The difficulty in inferring mediation of genetic effects on diseases by intermediate outcomes is how to control for unknown confounders. Current approaches exploit "Mendelian Randomization", the random segregation of alleles, and use known genetic risk alleles as instrumental variables to infer causality. Limitations of the existing framework, mainly on overly restrictive assumptions and inability to model the causal effect on binary outcomes, have impeded applicability of such inference. We will revamp the instrumental variable framework originally developed in econometrics to fit better to genetic studies.