Chen Wenan, Larrabee Beth R, Ovsyannikova Inna G, Kennedy Richard B, Haralambieva Iana H, Poland Gregory A, Schaid Daniel J
Division of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905.
Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
Genetics. 2015 Jul;200(3):719-36. doi: 10.1534/genetics.115.176107. Epub 2015 May 6.
Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.
最近开发的两种精细定位方法CAVIAR和PAINTOR，相较于其他精细定位方法展现出了更好的性能。它们还具有仅使用边际检验统计量和单核苷酸多态性（SNP）之间相关性的优势。这两种方法都利用了边际检验统计量渐近服从多元正态分布且基于似然性这一事实。然而，它们与贝叶斯精细定位方法（如BIMBAM）之间的关系尚不清楚。在本研究中，我们首先表明CAVIAR和BIMBAM实际上彼此近似等效。这产生了一种在贝叶斯框架中使用边际检验统计量的精细定位方法，我们将其称为CAVIAR贝叶斯因子（CAVIARBF）。贝叶斯框架的另一个优势在于它能够同时回答关联问题和精细定位问题。我们还通过模拟比较了CAVIARBF与其他方法在不同数量因果变异情况下的表现。结果表明，CAVIARBF和BIMBAM的性能均优于PAINTOR和其他方法。与BIMBAM相比，CAVIARBF具有仅使用边际检验统计量的优势，并且运行时间约为BIMBAM的四分之一到五分之一。我们将不同方法应用于具有相同表型的两个独立队列。结果显示，CAVIARBF、BIMBAM和PAINTOR选择了相同的前3个SNP；然而，CAVIARBF和BIMBAM在两个队列中选择排名前10的SNP时具有更好的一致性。软件可在https://bitbucket.org/Wenan/caviarbf获取。
