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遗传结构的理解，并有望识别因果变异的基因组位点，并实现准确的遗传风险预测。然而，由于大多数与医学相关的特征都受到众多遗传因素的影响，而每一个遗传因素只能解释一小部分遗传因素，因此，为了提供检测这些难以捉摸的关联所需的统计能力，几十万人的队列规模是必要的。本提案旨在开发快速而强大的统计方法，以解决建模此类大规模数据集时出现的关键挑战：在保持计算可追溯性的同时，纠正来自人口分层或研究参与者之间的神秘相关性的微妙混淆。目前最先进的关联测试方法使用线性混合模型同时模拟所有标记的影响，同时考虑样本结构。然而，现有的混合模型技术在计算上是昂贵的，并且还假设所有标记都具有非零效应。本提案旨在通过开发和实现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分析连锁不平衡模式的新方法，以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实际上是许多因果关系的真实结果的感知膨胀。这种方法将防止混淆引起的误报关联或过度保守校准造成的功率损失的替代危险。第二个目标是开发一种快速算法，应用现代数值线性代数的迭代方法，将混合模型关联测试的计算复杂度降低到数据大小线性。随着研究规模的增加，这一进展将使混合模型分析保持可行性，解锁罕见或小影响变量的关联。第三个目标是将该方法扩展到大多数标记与疾病没有关联的遗传结构模型中——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，在发布给科学界的软件中实现，并应用于真实的GWAS数据集，以搜索具有重要意义的其他关联。

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.