pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.
pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.
复制标题
pLARmEB：最小角回归与经验贝叶斯的集成，用于多位点全基因组关联研究
DOI：
10.1038/hdy.2017.8
复制
发表时间：
2017-06
期刊：
影响因子：
3.8
通讯作者：
Zhang YM
中科院分区：
文献类型：
--
作者：
Zhang J;Feng JY;Ni YL;Wen YJ;Niu Y;Tamba CL;Yue C;Song Q;Zhang YM
多位点全基因组关联研究（GWAS）已经成为鉴定与复杂性状相关的数量性状核苷酸（QTNs）的最先进的方法。然而，多位点模型在GWAS中的实现仍然是一个难点。在本研究中，我们将最小角回归与经验贝叶斯相结合，在多基因背景控制下进行多位点GWAS。我们使用了一种模型变换算法，对多基因矩阵K的协方差矩阵和环境噪声进行白化。一条染色体上的标记同时包含在多位点模型中，并使用最小角回归选择最可能相关的单核苷酸多态性（snp），而其他染色体上的标记用于计算亲属矩阵作为多基因背景对照。通过经验贝叶斯和似然比检验进一步检测多位点模型中选择的snp与性状的相关性。我们将这种方法称为pLARmEB（基于多基因背景控制的最小角度回归加经验贝叶斯）。仿真研究结果表明，与贝叶斯层次广义线性模型、高效混合模型关联模型（EMMA）和最小角度回归加经验贝叶斯模型相比，pLARmEB在QTN检测和QTN效应估计方面更强大，假阳性率更低，计算时间更短。在模拟实验中，pLARmEB、多位点随机- snp -效应混合线性模型和快速多位点随机- snp -效应EMMA方法对QTN的检测能力基本相等。然而，pLARmEB仅鉴定出48个先前报道的基因，这些基因与拟南芥的7个开花时间相关性状有关。
Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.