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: 8525990
• 负责人: ALEXANDER GUSEV
• 金额: $ 4.92万
• 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/8525990
• 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）：遗传性的量化（遗传遗传学和表型之间的关系）是了解复杂疾病的总体遗传贡献的重要的第一步。最近，使用方差成分分析的技术使研究人员能够通过利用数千个不相关的个​​体来有效地估计常见标记和表型之间的关系。该提案侧重于局部遗传力分析，其中遗传力是根据与全基因组关联研究中的因果关系或其他具有生物学意义的基因组区域来估计的。先前的生物学工作已经表明，对已知基因座进行深度重测序发现了大量新的因果变异，在某些情况下，解释的方差量几乎翻倍，并揭示了各个基因座因果变异的异质性。然而，这些研究并不总是成功，通过计算回答哪些基因座包含额外的潜在变异的问题可以优先考虑这种精细作图分析并指导整体关联研究设计。该提案概述了新颖的统​​计方法，这些方法使用方差分量分析来做出这些推论以进行精细映射。遗传性技术在这个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重要标记的标准估计技术相比的功效。我将把这些技术应用于具有已知相关位点的几种不同疾病数据集，并量化可能隐藏在这些位点处的额外变异量。我将利用这些发现来确定后续研究的表型和基因座的优先顺序，并推断出更大规模研究的预期结果。我的第二个目标涉及与这些技术相关的特定现象，即由于连锁不平衡 (LD) 而存在相关标记时，估计值会出现偏差。由于这种相关性在真实数据中普遍存在，并且对于引起疾病的变异可以是高度结构化的，因此解决这种偏差至关重要。我提出了来自群体遗传学领域的几种技术，这些技术解决了这种偏差对遗传力估计的影响的相关性和详细的进一步分析，以及风险预测和混合模型关联等下游技术。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获基因座背后的所有遗传力的方法。我将尝试通过查看标记的组合来推断个体之间共享的总量，而不是仅估计已键入的标记。我将探索为该技术提供动力的人口统计和队列参数，并将总遗传力推断与前面描述的其他程序进行比较。