Fine-mapping heritability at known disease loci with correlated markers

使用相关标记精细绘制已知疾病位点的遗传力

• 批准号: 8651765
• 负责人: ALEXANDER GUSEV
• 金额: $ 5.33万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8651765
• 关键词: Accounting; Address; Architecture; Biological; Complex; Data; Data Set; Disease; Disease model; Frequencies; Future; Gene Frequency; Genetic; Genetic Models; Genome; Genomics; Genotype; Haplotypes; Heritability; Heterogeneity; Individual; Inherited; Knowledge; Linkage Disequilibrium; Maps; Measures; Methods; Modeling; Outcome; Pattern; Phenotype; Population Genetics; Population Study; Procedures; Publishing; Research Design; Research Personnel; Risk; Sampling; Siblings; Statistical Methods; Stream; Structure; Techniques; Twin Multiple Birth; Variant; Weather; Work; base; cohort; density; disease phenotype; follow-up; genome wide association study; genome-wide; insight; novel; public health relevance; simulation; success; tool; trait

DESCRIPTION (provided by applicant): Quantification of heritability - the relationship between inherited genetics and phenotype - is an important first step to understanding the overall genetic contributions to complex disease. Recently, techniques using variance-components analysis have allowed researchers to effectively estimate the relationship between common markers and phenotype by leveraging thousands of unrelated individuals. This proposal focuses on local heritability analysis, where heritability is estimated from regions of the genome implicated as causal in genome-wide association studies or otherwise biologically significant. Previous biological work has shown multiple instances where deep re-sequencing of known loci uncovered an abundance of new causal variants, in some instances nearly doubling the amount of explained variance and revealing heterogeneity of causal variants at individual loci. However, these studies have not always been successful, and computationally answering the question of which loci harbor additional underlying variation can prioritize such fine-mapping analysis and guide overall association study-design. This proposal outlines novel statistical methods that use variance-components analysis to make these inferences for fine-mapping. The application of heritability techniques to this domain is novel, and my first aim is to quantify the amount of power this kind of analysis has as compared to standard estimating techniques using one or a handful of significant markers. I will apply these techniques to several diverse disease datasets with known associated loci and quantify the amount of additional variation likely to be hidden at these loci. I will use these findings to prioritize phenotypes and loci for follow-up study, and extrapolate to the expected outcome of larger studies. My second aim deals with a specific phenomenon associated with these techniques, where estimates become biased in the presence of markers that are correlated due to linkage-disequilibrium (LD). As such correlation is ubiquitous in real data and can be highly structured with respect to the disease causing variants, it is vitally important to address this bias. I propose several techniques from the population genetics domain which address correlation and detail further analysis of the impact of this bias on estimates of heritability, as well as down-stream techniques such as risk prediction and mixed-model association. Lastly, I describe an approach for capturing all of the heritability underlying a locus by looking at higher level relationships between individuals. Rather than estimate only over the markers that have been typed, I will attempt to infer the total amount of sharing between individuals by looking at combinations of markers. I will explore the demographic and cohort parameters that yield power to this technique and compare total heritability inferences to the other procedures described previously.

描述（由申请人提供）：遗传力的量化-遗传遗传学和表型之间的关系-是理解复杂疾病的整体遗传贡献的重要第一步。最近，使用方差分量分析的技术使研究人员能够有效地估计共同标记和表型之间的关系，利用成千上万的无关个体。该建议侧重于局部遗传力分析，其中遗传力是从全基因组关联研究中涉及因果关系或其他生物学意义的基因组区域估计的。以前的生物学工作已经显示了多个实例，其中已知基因座的深度重新测序发现了大量新的因果变异，在某些情况下，解释的方差几乎增加了一倍，并揭示了单个基因座的因果变异的异质性。然而，这些研究并不总是成功的，并且通过计算来回答哪些位点具有额外的潜在变异的问题可以优先考虑这种精细映射分析并指导整体关联研究设计。该提案概述了新的统计方法，使用方差分量分析，使这些推论的精细映射。遗传力技术在这一领域的应用是新颖的，我的第一个目标是量化这种分析与使用一个或几个显著标记的标准估计技术相比的能力。我将把这些技术应用于几个不同的疾病数据集与已知的相关基因座，并量化可能隐藏在这些基因座的额外变异的数量。我将使用这些发现来优先考虑后续研究的表型和位点，并外推到更大研究的预期结果。我的第二个目标是处理与这些技术相关的一个特定现象，即在存在由于连锁不平衡（LD）而相关的标记的情况下，估计值会出现偏差。由于这种相关性在真实的数据中普遍存在，并且可以相对于致病变体高度结构化，因此解决这种偏差至关重要。我提出了几种技术，从人口遗传学领域，解决相关性和详细的进一步分析的影响，这种偏见的估计遗传力，以及下游技术，如风险预测和混合模型关联。最后，我描述了一种通过观察个体之间更高层次的关系来捕捉一个基因座的所有遗传性的方法。我将尝试通过观察标记的组合来推断个体之间的共享总量，而不是仅对已键入的标记进行估计。我将探讨人口统计学和队列参数，产生功率，这种技术和比较总遗传力的推断，以其他程序前面所述。