A critical problem in genetics is to discover how gene expression is regulated within cells. Two major tasks of regulatory association learning are : (i) identifying SNP-gene relationships, known as eQTL mapping, and (ii) determining gene-gene relationships, known as gene network estimation. To share information between these two tasks, we focus on the unified model for joint estimation of eQTL mapping and gene network, and propose a $L_{1-2}$ regularized multi-task graphical lasso, named $L_{1-2}$ GLasso. Numerical experiments on artificial datasets demonstrate the competitive performance of $L_{1-2}$ GLasso on capturing the true sparse structure of eQTL mapping and gene network. $L_{1-2}$ GLasso is further applied to real dataset of ADNI-1 and experimental results show that $L_{1 -2}$ GLasso can obtain sparser and more accurate solutions than other commonly-used methods.

翻译：遗传学的一个关键问题是如何在细胞内管理基因表达方式。监管协会学习的两个主要任务是:(一) 确定SNP-gene关系,称为eQTL映像,和(二) 确定基因-gene关系,称为基因网络估计。为了在这两项任务之间分享信息,我们侧重于联合估计eQTL映像和基因网络的统一模型,并提议一个名为$L ⁇ 1-2}$GLasso的正规化多任务图形laso。 人工数据集的数值实验显示了$L ⁇ 1-2}Gasso在捕捉eQTL映像和基因网络的真正稀薄结构方面的竞争性表现。$L ⁇ 1-2}GLasso美元被进一步应用于ADNI-1和实验结果的真实数据集,表明$L ⁇ 1-2}GLasso$GLasso能够获得比其他常用方法更稀薄和更准确的解决方案。