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确定了数百个与常见和复杂疾病相关的基因座，但在这些高维数据中进行统计推断仍然存在重大挑战。具体地说，由新兴的全基因组测序研究产生的罕见变异可能解释了“缺失遗传性”，但对传统的逐个位点的方法构成了挑战。基因-环境相互作用的研究没有产生太多的成功，可能是由于现有分析方法的局限性。通过中间结果调节遗传效应是一个可能导致疾病预防或治疗的新出现的感兴趣的话题。然而，现有的推断调解效果的统计方法一直不发达。在这项提案中，我们计划建立新的统计方法来应对这些挑战。方法学研究的动力来自但不限于妇女健康倡议中的全基因组关联研究和测序项目，包括“基因组学和随机试验网络”(Garnet)、“基因与环境的种群结构”(PAGE)和“外显子组测序项目”(ESP)。这项建议的特点是，国际和平研究所和联合调查员确实在进行这些研究，因此，提出的方法创新将立即应用于解决感兴趣的科学问题。近年来，人们提出了许多用于稀有变异分析的统计方法。现有的方法都没有考虑到中性变异的存在，即对性状没有功能影响的等位基因。在前面提到的基因测试中加入中性变种肯定会稀释力量。在这个方案中，我们提出了一类有限混合模型，它显式地梳理出中性变量来提高功率。识别基因-环境相互作用的主要挑战是由于样本量有限，相互作用的幅度通常很小，缺乏动力。降维，如基于基因集的推理，对于减少假设检验的数量和丰富弱遗传效应是至关重要的。我们将开发一套基于基因集的两阶段过滤程序来检测基因与环境的相互作用。我们还将开发一个带有L1惩罚的多变量稀疏基因集测试框架，以在基因或路径中组装微弱的遗传效应。通过中间结果推断遗传效应对疾病的中介作用的困难在于如何控制未知的混杂因素。目前的方法利用了“孟德尔随机化”，即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，主要是过于严格的假设，以及无法对二元结果的因果影响进行建模，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开发的工具变量框架，以更好地适应遗传学研究。 公共卫生相关性：这项建议的重点是开发新的统计方法来分析高通量的基因分型和测序数据，重点关注当前遗传流行病学中的三个突出挑战：罕见变异、基因-环境相互作用和中间结果的中介作用。拟议的方法将确定导致预防和治疗常见疾病的遗传易感性和环境暴露。

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. PUBLIC HEALTH RELEVANCE: The focus of this proposal is to develop novel statistical methods for analysis of high-throughput genotyping and sequencing data, focusing on three outstanding challenges in current genetic epidemiology: rare variants, gene-environment interactions, and mediation by intermediate outcomes. The proposed methods will identify genetic predisposition and environmental exposures that lead to prevention and treatment of common diseases.