GENOMIZER: an integrated analysis system for genome-wide association data

Abstract

Genome-wide association analysis appears to be a promising way to identify heritable susceptibility factors for complex human disorders. However, the feasibility of large-scale genotyping experiments is currently limited by an incomplete marker coverage of the genome, a restricted understanding of the functional role of given genomic regions, and the small sample sizes used. Thus, genome-wide association analysis will be a screening tool to facilitate subsequent gene discovery rather than a means to completely resolve individual genetic risk profiles. The validation of association findings will continue to rely upon the replication of "leads" in independent samples from either the same or different populations. Even under such pragmatic conditions, the timely analysis of the large data sets in question poses serious technical challenges. We have therefore developed public-domain software, GENOMIZER, that implements the workflow of an association experiment, including data management, single-point and haplotype analysis, "lead" definition, and data visualization. GENOMIZER (www.ikmb.uni-kiel.de/genomizer) comes with a complete user manual, and is open-source software licensed under the GNU Lesser General Public License. We suggest that the use of this software will facilitate the handling and interpretation of the currently emerging genome-wide association data.

Systematic Association Mapping Identifies NELL1 as a Novel IBD Disease Gene

“…Genome-wide data analysis was carried out using an updated version of GENOMIZER [74]. Association hits that passed the quality criteria were extracted using the “GenomizerHits” tool (http://www.ikmb.uni-kiel.de/genomizer/).…”

Heterozygous carriage of the alpha1-antitrypsin PiZ variant increases the risk to develop liver fibrosis

“…Moreover, genotyping was performed for the PNPLA3 variant rs738409 (hcv7241), the TM6SF2 variant rs58542926 (hcv89463510), and the MBOAT7 variant rs641738 (hcv8716820). All process data was logged and administered using a database-driven LIMS (22).…”

Solutions for biomedical grid computing—Case studies from the D-Grid project Services@MediGRID

“…An important part of the biomedical research will be the rapid technological progress and the decrease of genotyping and high throughput sequencing (NGS = next-generation sequencing) costs. The quantity of large and complex datasets and the analysis of these data require sophisticated software solutions and a sufficient capacity of computation [68].…”