Practical issues in imputation-based association mapping.
Practical issues in imputation-based association mapping.
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DOI：
10.1371/journal.pgen.1000279
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发表时间：
2008-12
期刊：
影响因子：
4.5
通讯作者：
Stephens, Matthew
中科院分区：
文献类型：
--
作者：
Guan, Yongtao;Stephens, Matthew
基于归因的关联方法为检测未分型变异与表型的关联以及整合使用不同基因分型平台的多项研究结果提供了一个强大的框架。在此，我们考虑在实际应用这些方法时出现的几个问题，包括：（i）影响归因准确性的因素，包括参考面板的选择；（ii）归因准确性对检测关联能力的影响；（iii）贝叶斯方法和频率学派方法在检测归因基因型与表型关联方面的相对优势；（iv）如何快速准确地计算用于检测归因单核苷酸多态性（SNP）的贝叶斯因子。我们发现基于归因的方法对归因准确性具有稳健性，并且即使平均归因准确性较差，也能提高检测关联的能力。我们解释了通过标准似然比检验对SNP进行关联排序如何能得到与使用一种不自然先验假设的贝叶斯方法相同的结果——具体而言，即难以归因的SNP往往具有更大的效应——并评估了使用不做此假设的贝叶斯方法所获得的能力。在贝叶斯框架内，我们发现通过简单地用一个点估计值——它们的后验均值——替代未知基因型，就可以对完整分析进行良好的近似。与已发表的基于抽样的方法相比，这种近似大大降低了计算成本，并且我们提出的方法在计算资源非常有限（例如，一台台式计算机）的情况下在全基因组范围内是可行的。这种近似还有助于整合不同研究的信息，仅使用每个SNP的汇总数据。此处讨论的方法在软件包BIMBAM中实现，可从http://stephenslab.uchicago.edu/software.html获取。
基因型归因正在成为一种比较和整合使用不同SNP基因分型平台的多项关联研究结果的流行方法。其基本思想是利用由于未分型和已分型SNP之间的相关性，每项研究中未分型SNP的基因型通常可以从已分型SNP的基因型中以较高的准确性推断（“归因”）出来这一事实。在本文中，我们考虑在实际应用这些方法时出现的几个问题，包括影响归因准确性的因素、在检测归因SNP与表型之间的关联时考虑归因不确定性的重要性、归因准确性如何影响能力以及当研究小组之间只能共享单个SNP的汇总数据时如何整合不同研究的结果。
Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption—specifically, that difficult-to-impute SNPs tend to have larger effects—and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate—their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html. Genotype imputation is becoming a popular approach to comparing and combining results of multiple association studies that used different SNP genotyping platforms. The basic idea is to exploit the fact that, due to correlation among untyped and typed SNPs, genotypes of untyped SNPs in each study can be inferred (“imputed”) from the genotypes at typed SNPs, often with high accuracy. In this paper, we consider several issues that arise when applying these methods in practice, including factors affecting imputation accuracy, the importance of taking account of imputation uncertainty when testing for association between imputed SNPs and phenotype, how imputation accuracy affects power, and how to combine results across studies when only single-SNP summary data can be shared among research groups.
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影响因子：
30.8
作者：
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