Fast and powerful extensions of mixed model methods for GWAS

GWAS 混合模型方法的快速而强大的扩展

• 批准号: 9186420
• 负责人: Po-Ru Loh
• 金额: $ 4.24万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9186420
• 关键词: Accounting; Address; Adopted; Algorithms; Architecture; Calibration; Communities; Complex; Computational Technique; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Disease; Economic Inflation; Gaussian model; Genetic; Genetic Models; Genetic Risk; Genomics; Genotype; Heritability; Individual; Linear Algebra; Linkage Disequilibrium; Mathematics; Medical; Medical Genetics; Methodology; Methods; Modeling; Modernization; Morphologic artifacts; Participant; Pattern; Phenotype; Population Genetics; Sampling; Series; Site; Statistical Methods; Statistical Models; Stratification; Structure; Techniques; Testing; Time; Training; Variant; base; cohort; cost; experience; genetic linkage analysis; genetic variant; genome wide association study; genome-wide; improved; interest; novel; open source; population stratification; public health relevance; simulation; statistics; trait

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.

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，并有望鉴定致病变异的基因组位点，实现准确的遗传风险预测。然而，由于大多数具有医学意义的性状都受到多种遗传因素的影响，而每种遗传因素只能解释遗传力的一小部分，因此需要数十万个体规模的队列规模来提供检测这些难以捉摸的关联所需的统计功效。该提案旨在开发快速而强大的统计方法，以解决在建模此类大规模数据集时出现的关键挑战：纠正研究参与者之间的人口分层或神秘相关性的微妙混淆，同时保持计算的易处理性。关联检验的现有技术方法使用线性混合模型来同时模拟所有标记物的效应，同时考虑样品结构。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具有非零效应。该提案旨在通过开发和实施一种新的校准良好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针对更现实的遗传结构进行定制。第一个具体的目标是开发一种新的方法，分析连锁不平衡模式，校准混合模型关联测试分数，区分全基因组的通货膨胀的测试统计数据，由于样本结构，从感知通货膨胀，实际上是许多因果位点的真实结果。该方法将防止混淆导致的假阳性关联或过度保守校准导致的功率损失的替代危险。第二个目标是开发一种快速算法，该算法应用数值线性代数的现代迭代方法，将混合模型关联测试的计算复杂度降低到数据大小的线性。这一进展将使混合模型分析随着研究规模的增加而保持可行性，从而从罕见或小效应变异中解锁关联。第三个目标是将该方法扩展到对大多数标记没有疾病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现，并应用于真实的GWAS数据集，以搜索达到重要性的其他关联。