Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies.
Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies.
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DOI：
10.1371/journal.pgen.1000130
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发表时间：
2008-07-25
期刊：
影响因子：
4.5
通讯作者：
Balding, David J.
中科院分区：
文献类型：
--
作者：
Hoggart, Clive J.;Whittaker, John C.;De Iorio, Maria;Balding, David J.
一次检测一个单核苷酸多态性（SNP）并不能完全发挥全基因组关联研究识别多个致病变异的潜力，这在许多复杂疾病中是一种合理的情况。我们表明，由于随机搜索方法的发展，对全基因组研究中的整套SNP进行同时分析以确定最能预测疾病结果的子集现在是可行的。我们使用了一种受贝叶斯启发的惩罚最大似然方法，其中每个SNP都可以被考虑对疾病风险有相加、显性和隐性贡献。对回归系数获得了后验众数估计，每个回归系数都被赋予一个在零处有尖锐众数的先验。一个非零的系数估计被解释为对应一个显著的SNP。我们研究了两种先验分布，并表明与单SNP检测相比，正态 - 指数 - 伽马先验导致了更好的SNP选择。我们还推导出了I型错误的一个明确近似值，避免了使用置换程序的需要。除了全基因组分析外，我们的方法非常适合用于对从重新测序和/或填补获得的非常密集的SNP集合进行精细定位。它可以适应定量以及病例 - 对照表型、协变量调整，并且可以扩展以搜索相互作用。在这里，我们使用多达50万个SNP的模拟病例 - 对照数据集、一个30万个SNP的真实全基因组数据集以及一个基于序列的数据集展示了我们方法的功效和经验I型错误，每个数据集都可以在台式工作站上在几个小时内分析完成。
与疾病状态的关联检测通常一次检测一个SNP，忽略所有其他已基因分型的SNP的影响。我们开发了一种计算高效的方法来同时分析所有SNP，无论是在全基因组关联（GWA）研究中，还是在基于重新测序和/或填补的精细定位研究中。该方法选择一个最能预测疾病状态的SNP子集，同时控制所选SNP的I型错误。这比标准的单SNP方法有很多优势，因为当与疾病状态相关的其他SNP已经包含在模型中时，来自特定SNP的信号可以更清晰地被评估。因此，与单SNP分析相比，由于剩余变异减少，功效提高且假阳性率降低。定位也大大改善。我们通过全基因组关联模拟研究、一个真实的全基因组数据集以及一个基于序列的精细定位模拟研究展示了相对于广泛使用的单SNP阿米蒂奇趋势检验的这些优势。
Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation. Tests of association with disease status are normally conducted one SNP at a time, ignoring the effects of all other genotyped SNPs. We developed a computationally efficient method to simultaneously analyse all SNPs, either in a genome-wide association (GWA) study, or a fine-mapping study based on re-sequencing and/or imputation. The method selects a subset of SNPs that best predicts disease status, while controlling the type-I error of the selected SNPs. This brings many advantages over standard single-SNP approaches, because the signal from a particular SNP can be more clearly assessed when other SNPs associated with disease status are already included in the model. Thus, in comparison with single-SNP analyses, power is increased and the false positive rate is reduced because of reduced residual variation. Localisation is also greatly improved. We demonstrate these advantages over the widely used single-SNP Armitage Trend Test using GWA simulation studies, a real GWA dataset, and a sequence-based fine-mapping simulation study.
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