GPU Accelerated Browser for Neuroimaging Genomics.
Bob Zigon, Huang Li, Xiaohui Yao, Shiaofen Fang, Mohammad Al Hasan, Jingwen Yan, Jason H Moore, Andrew J Saykin, Li Shen, Alzheimer’s Disease Neuroimaging Initiative
Author Information
Bob Zigon: Beckman Coulter, Indianapolis, IN, 46268, USA. robert.zigon@beckman.com.
Huang Li: Department of Computer Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
Xiaohui Yao: Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
Shiaofen Fang: Department of Computer Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
Mohammad Al Hasan: Department of Computer Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
Jingwen Yan: Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
Jason H Moore: Department of Biostatistics, Epidemiology, Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Andrew J Saykin: Department of Radiology and Imaging Sciences, IU School of Medicine, Indianapolis, IN, 46202, USA.
Li Shen: Department of Biostatistics, Epidemiology, Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ORCID
Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration.
