Fine-mapping heritability at known disease loci with correlated markers

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--是理解复杂疾病的全部遗传因素的重要的第一步。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共同标记和表型之间的关系。这项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被认为是因果关系的基因组区域或其他生物学意义上的区域来估计的。以前的生物学工作已经表明，对已知基因座进行深度重新测序发现了大量新的因果变异，在某些情况下，解释的变异量几乎翻了一番，并揭示了个别基因座因果变异的异质性。然而，这些研究并不总是成功的，通过计算回答哪些基因座存在额外的潜在变异的问题可以优先进行这种精细定位分析，并指导总体关联研究设计。这项建议概述了新的统计方法，这些方法使用方差成分分析来做出这些推论，以进行精细绘图。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重要标记的标准估计技术相比所具有的力量。我将把这些技术应用于几个具有已知相关基因的不同疾病数据集，并量化这些基因座可能隐藏的额外变异量。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一个特定现象，其中估计在存在由于连锁不平衡(LD)而相关的标记时变得有偏差。由于这种相关性在真实数据中普遍存在，并且可以与引起疾病的变种高度结构化，因此解决这种偏差至关重要。我提出了几种来自群体遗传学领域的技术，这些技术解决了相关性，并详细分析了这种偏差对遗传率估计的影响，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级别的关系来捕捉一个基因座潜在的所有遗传性的方法。我不会只估计已经输入的标记，而是尝试通过查看标记的组合来推断个人之间共享的总数量。我将探索为这项技术带来力量的人口统计学和队列参数，并将总遗传率推断与前面描述的其他程序进行比较。

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.