Fast and powerful extensions of mixed model methods for GWAS

描述(由申请人提供)：全基因组关联研究(GWAS)提高了我们对许多复杂疾病的遗传结构的理解，并有望识别因果变异的基因组位置，并使准确的遗传风险预测成为可能。然而，由于大多数具有医学价值的特征都受到多种遗传因素的影响，每个因素只解释了一小部分遗传性，因此数十万人规模的队列规模将是必要的，以提供检测这些难以捉摸的关联所需的统计能力。这一建议旨在开发快速而强大的统计方法，以解决在对这种大规模数据集进行建模时出现的关键挑战：纠正来自人口分层的微妙混淆或研究参与者之间的隐晦相关性，同时保持计算易操作性。当前技术水平的关联测试方法使用线性混合模型来同时对所有标记的影响进行建模，同时考虑到样本结构。然而，现有的混合模型技术在计算上是昂贵的，并且还假设所有的标记都具有非零效应。这一建议旨在通过开发和实施一种新的经过良好校准的混合模型统计量来扩展混合模型方法，该统计量可以非常快速地计算并适合更现实的遗传结构。第一个具体目标是开发一种新的方法，分析连锁不平衡模式来校准混合模型关联测试分数，区分由于样本结构导致的测试统计数据的全基因组膨胀和感知的膨胀，这实际上是许多因果基因座的真实结果。这种方法将防止由于过于保守的校准造成混淆或断电而产生的假阳性关联的替代危险。第二个目标是开发一种将现代迭代方法应用于数值线性代数的快速算法，以将混合模型关联测试的计算复杂性降低到数据大小为线性的情况。这一进展将使混合模型分析在研究规模增加时仍然可行，解锁与罕见或小效应变种的关联。第三个目标是将该方法扩展到对大多数标记与疾病没有关联的遗传结构进行建模--正如人们普遍认为的那样--从而提高统计能力。所有这些技术都将在模拟中得到验证，在向科学界发布的软件中实施，并应用于真实的GWAS数据集，以寻找更多达到重要意义的关联。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have improved our understanding of the genetic architectures of many complex diseases and hold the promise of identifying genomic loci of causal variants and enabling accurate genetic risk prediction. However, because most traits of medical interest are influenced by a multitude of genetic factors, each of which explain only a small fraction of heritability, cohort sizes on the scale of hundreds of thousands of individuals will be necessary to provide the statistical power required to detect these elusive associations. This proposal aims to develop fast and powerful statistical methods addressing key challenges that arise in modeling such large-scale data sets: correcting for subtle confounding from population stratification or cryptic relatedness among study participants while maintaining computational tractability. The current state of the art approach to association testing uses linear mixed models to simultaneously model the effects of all markers while accounting for sample structure. Existing mixed model techniques are computationally expensive, however, and also assume that all markers have nonzero effects. This proposal aims to extend mixed model methods by developing and implementing a new well-calibrated mixed model statistic that can be computed very quickly and tailored to more realistic genetic architectures. The first specific aim is to develop a novel method that analyzes linkage disequilibrium patterns to calibrate mixed model association test scores, distinguishing genome-wide inflation of test statistics due to sample structure from perceived inflation that is actually the true result of many causal loci. This method will safeguard against the alternative dangers of false positive associations from confounding or power loss from overly conservative calibration. The second aim is to develop a fast algorithm that applies modern iterative methods for numerical linear algebra to reduce the computational complexity of mixed model association testing to linear in the data size. This advance will enable mixed model analysis to remain feasible as study sizes increase, unlocking associations from rare or small-effect variants. The third aim is to extend the method to model genetic architectures in which most markers have no disease association - as is widely believed - thereby improving statistical power. All of these techniques will be validated in simulation, implemented in software released to the scientific community, and applied to real GWAS data sets to search for additional associations that reach significance.