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Joint variable selection and network modeling for detecting eQTLs.
Statistical Applications in Genetics and Molecular Biology ( IF 0.8 ) Pub Date : 2020-02-20 , DOI: 10.1515/sagmb-2019-0032
Xuan Cao 1 , Lili Ding 2 , Tesfaye B Mersha 3
Affiliation  

In this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL – Multivariate Spike and Slab Lasso, SSUR – Sparse Seemingly Unrelated Bayesian Regression, and OBFBF – Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).

中文翻译:

用于检测eQTL的联合变量选择和网络建模。

在这项研究中,我们对三种最新的统计方法进行了比较,这些方法用于联合变量选择和协方差估计,并应用了检测表达数量性状基因座(eQTL)和基因网络估计,并引入了一种新的分层贝叶斯方法进行比较。与eQTL中的传统单变量回归方法不同,所有这四种方法都是通过多元回归模型将表型和基因型相关联,该模型将表型之间的依赖性信息纳入其中,并使用贝叶斯多样性调整来避免传统多重测试校正方法所带来的多重测试负担。我们介绍了三种方法的性能(MSSL –多元峰值和板状套索,SSUR –稀疏的看似无关贝叶斯回归,以及OBFBF –目标贝叶斯分数贝叶斯因子),以及通过模拟实验提出的JDAG(通过高斯有向无环图模型进行联合估计)方法以及公开可用的HapMap真实数据(以哮喘为例)。与现有方法相比,JDAG确定了在行稀疏设置下具有更高灵敏度和特异性的网络。JDAG要求在中小型维度上执行较少,但当前不适用于高维度数据。哮喘数据中的eQTL分析显示了许多已知的基因法规,例如STARD3,IKZF3和PGAP3,所有这些均在哮喘研究中报告。拟议方法的代码可从GitHub(https://github.com/xuan-cao/Joint-estimation-for-eQTL)免费获得。JDAG(通过高斯定向无环图模型进行联合估计)方法是通过模拟实验和公开可用的HapMap真实数据(以哮喘为例)进行的。与现有方法相比,JDAG确定了在行稀疏设置下具有更高灵敏度和特异性的网络。JDAG要求在中小型维度上执行较少,但当前不适用于高维度数据。哮喘数据中的eQTL分析显示了许多已知的基因法规,例如STARD3,IKZF3和PGAP3,所有这些均在哮喘研究中报告。拟议方法的代码可从GitHub(https://github.com/xuan-cao/Joint-estimation-for-eQTL)免费获得。JDAG(通过高斯有向无环图模型进行联合估计)方法通过模拟实验和公开可用的HapMap真实数据(以哮喘为例)进行。与现有方法相比,JDAG确定了在行稀疏设置下具有更高灵敏度和特异性的网络。JDAG要求在中小型维度上执行较少,但当前不适用于高维度数据。哮喘数据中的eQTL分析显示了许多已知的基因法规,例如STARD3,IKZF3和PGAP3,所有这些均在哮喘研究中报告。拟议方法的代码可从GitHub(https://github.com/xuan-cao/Joint-estimation-for-eQTL)免费获得。JDAG在行稀疏设置下识别出具有更高灵敏度和特异性的网络。JDAG要求在中小型维度上执行较少,但当前不适用于高维度数据。哮喘数据中的eQTL分析显示了许多已知的基因法规,例如STARD3,IKZF3和PGAP3,所有这些均在哮喘研究中报告。拟议方法的代码可从GitHub(https://github.com/xuan-cao/Joint-estimation-for-eQTL)免费获得。JDAG在行稀疏设置下识别出具有更高灵敏度和特异性的网络。JDAG要求在中小型维度上执行较少,但当前不适用于高维度数据。哮喘数据中的eQTL分析显示了许多已知的基因法规,例如STARD3,IKZF3和PGAP3,所有这些均在哮喘研究中报告。拟议方法的代码可从GitHub(https://github.com/xuan-cao/Joint-estimation-for-eQTL)免费获得。
更新日期:2020-02-20
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