Last week, the manuscript describing algorithms and implementation of the OmicABEL package became available online via f1000research, and was assigned doi:10.12688/f1000research.4867.1. Publishing in f1000research - "The first Open Science journal for life scientists" - is a natural step for the GenABEL project, which practices open source/methods/science ideology. In f1000research, manuscripts are published immediately, followed by post-publication open (non-anonymous) review. If two reviewers approve the manuscript, it is then indexed in PubMed, Scopus and Web-Of-Science. [Please click on 'READ MORE' below for complete story]
The manuscript considers the problem of mixed-model based GWAS for an arbitrary number of traits, and demonstrates that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. These optimal algorithms are implemented in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. The OmicABEL tool described in this manuscript is available under the GNU GPL v.3 (the de-facto standard license of the GenABEL project for statistical genomics).
We are very much looking forward to receiving open feedback from the reviewers!
Fabregat-Traver D, Sharapov SZ, Hayward C et al. High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software [v1; ref status: awaiting peer review, http://f1000r.es/40b] F1000Research 2014, 3:200 (doi: 10.12688/f1000research.4867.1)