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: 8712922
• 负责人: Po-Ru Loh
• 金额: $ 5.15万
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8712922
• 关键词: Accounting; Address; Adopted; Algorithms; Architecture; Calibration; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Disease; Disease Association; Economic Inflation; Genetic; Genetic Models; Genetic Risk; Genomics; Genotype; Heritability; Individual; Linear Algebra; Linkage Disequilibrium; Medical; Medical Genetics; Methodology; Methods; Modeling; Morphologic artifacts; Participant; Pattern; Phenotype; Population; Population Genetics; Sampling; Series; Simulate; Site; Statistical Methods; Stratification; Structure; Techniques; Testing; Time; Training; Variant; base; cohort; cost; experience; genetic variant; genome wide association study; genome-wide; improved; interest; novel; open source; 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体系结构的理解，并保持了鉴定因果变异的基因组基因座的希望，并实现了准确的遗传风险预测。但是，由于大多数医学兴趣的特征都受到多种遗传因素的影响，每个遗传因素仅解释了一小部分的遗传力，因此需要数十万个个体的同类大小来提供检测这些难以捉摸的关联所需的统计能力。该提案旨在开发快速有力的统计方法，以解决建模这样的大规模数据集时出现的关键挑战：纠正研究参与者之间在人口分层或神秘相关性的同时，在维持计算障碍的同时，纠正了这些挑战。当前的关联测试方法的现状使用线性混合模型同时对所有标记的影响进行对样品结构的影响。但是，现有的混合模型技术在计算上很昂贵，还假定所有标记都具有非零效应。该建议旨在通过开发和实施一个新的良好的混合模型统计量来扩展混合模型方法，该模型统计量可以非常快速地计算并根据更现实的遗传体系结构进行量身定制。第一个具体目的是开发一种新的方法，该方法分析了连锁不平衡模式以校准混合模型结合测试评分，从而区分了由于样本结构与感知到的通货膨胀所致，这实际上是许多因果基因座的真正结果，从而区分了测试统计量的全基因组膨胀。该方法将保护假阳性关联的另一种危险，免受过度保守的校准的混淆或功率损失。第二个目的是开发一种快速算法，该算法对数值线性代数采用现代迭代方法，以将混合模型关联测试的计算复杂性降低到数据大小中的线性。随着研究大小的增加，从稀有或小效应变体中解锁关联，这一进步将使混合模型分析能够保持可行。第三个目的是将方法扩展到建模遗传体系结构，其中大多数标记没有疾病关联 - 正如人们所认为的那样 - 从而提高了统计能力。所有这些技术将在模拟中验证，在发布给科学界的软件中实施，并应用于实际GWAS数据集以搜索具有重要意义的其他关联。