Finding alternative expression quantitative trait loci by exploring sparse model space.

ABSTRACT: Sparse modeling, a feature selection method widely used in the machine-learning community, has been recently applied to identify associations in genetic studies including expression quantitative trait locus (eQTL) mapping. These genetic studies usually involve high dimensional data where the number of features is much larger than the number of samples. The high dimensionality of genetic data introduces a problem that there exist multiple solutions for optimizing a sparse model. In such situations, a single optimization result provides only an incomplete view of the data and lacks power to find alternative features associated with the same trait. In this article, we propose a novel method aimed to detecting alternative eQTLs where two genetic variants have alternative relationships regardin

SUBMITTER: Wang Z 

PROVIDER: S-EPMC4010169 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature