Fine-mapping from summary data with the "Sum of Single Effects" model.
Fine-mapping from summary data with the "Sum of Single Effects" model.
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用“单一效应总和”模型对汇总数据进行精细映射。
DOI：
10.1371/journal.pgen.1010299
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发表时间：
2022-07
期刊：
影响因子：
4.5
通讯作者：
中科院分区：
文献类型：
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作者：
在最近的工作中，Wang等人介绍了“单效应总和”（SuSiE）模型，并表明它提供了一种简单有效的方法来从个体水平数据中精细定位遗传变异。在这里，我们提出了新的方法来拟合SuSiE模型的汇总数据，例如，从关联研究和连锁不平衡（LD）值估计从一个合适的参考面板的单SNP的Z-分数。为了开发这些新方法，我们首先描述了一个简单的，通用的策略，用于扩展任何个人级别的数据方法来处理汇总数据。其关键思想是用基于汇总数据的类似可能性取代通常的回归可能性。我们表明，现有的精细映射方法，如FINEMAP和CAVIAR也（隐式）使用这种策略，但以不同的方式，所以这提供了一个共同的框架，了解不同的方法进行精细映射。我们调查其他常见的实际问题，在精细映射与汇总数据，包括Z分数和LD估计之间的不一致所造成的问题，我们开发诊断，以确定这些不一致。我们还提出了一个新的细化过程，提高了模型拟合在一些数据集，从而提高了整体的可靠性SuSiE精细映射结果。在一系列模拟数据集的精细映射方法的详细评估表明，SuSiE应用于汇总数据是有竞争力的，在速度和准确性，最好的可用的精细映射方法的汇总数据。精细定位的目标是识别因果影响某些感兴趣性状的遗传变异。精细定位具有挑战性，因为遗传变异可能由于称为连锁不平衡（LD）的现象而高度相关。目前最成功的方法，精细映射框架的问题作为一个变量选择问题，在这里，我们专注于这样一个方法的基础上的“单效应之和”（SuSiE）模型。本文的主要贡献是扩展SuSiE以处理摘要数据，当完整的基因型和表型数据不可用时，通常可以访问摘要数据。在扩展SuSiE的过程中，我们开发了一个新的数学框架，有助于解释现有的汇总数据的精细映射方法，为什么它们工作得很好（或不好），以及在什么情况下。在模拟中，我们表明，SuSiE应用于汇总数据是有竞争力的最佳可用的精细映射方法的汇总数据。我们还展示了不同的因素，如LD估计的准确性，可以影响质量的精细映射。
In recent work, Wang et al introduced the “Sum of Single Effects” (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data. The goal of fine-mapping is to identify the genetic variants that causally affect some trait of interest. Fine-mapping is challenging because the genetic variants can be highly correlated due to a phenomenon called linkage disequilibrium (LD). The most successful current approaches to fine-mapping frame the problem as a variable selection problem, and here we focus on one such approach based on the “Sum of Single Effects” (SuSiE) model. The main contribution of this paper is to extend SuSiE to work with summary data, which is often accessible when the full genotype and phenotype data are not. In the process of extending SuSiE, we developed a new mathematical framework that helps to explain existing fine-mapping methods for summary data, why they work well (or not), and under what circumstances. In simulations, we show that SuSiE applied to summary data is competitive with the best available fine-mapping methods for summary data. We also show how different factors such as accuracy of the LD estimates can affect the quality of the fine-mapping.
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影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
14.9
作者：
Li Y;Kellis M
通讯作者：
Kellis M
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium
影响因子：
30.8
作者：
通讯作者：
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