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Background  As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways.    Results  To alleviate this limitation, in this paper, we propose  geQTL , a sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits.  geQTL  can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that  geQTL  can effectively detect both individual and group-wise signals and outperforms the state-of-the-arts by a large margin.    Conclusions  This paper well illustrates that decoupling individual and group-wise associations for association mapping is able to improve eQTL mapping accuracy, and inferring individual and group-wise associations.    Electronic supplementary material  The online version of this article (doi:10.1186/s12859-016-0986-9) contains supplementary material, which is available to authorized users.

Sparse regression models for unraveling group and individual associations in eQTL mapping