The human brain has many heritable features (Kremen et al., 2010
; Peper et al., 2007
; Thompson et al., 2001
). However, the genetic variants underlying these high heritability estimates are, for the most part, unknown. Genome-wide association studies (GWAS) are one way to identify common variants influencing heritable traits in large-scale population studies. GWAS have been used to identify associations between single-nucleotide polymorphisms (SNPs) and a host of different traits implicated in numerous diseases (Cichon et al., 2009
; McCarthy et al., 2008
). Meta-analysis has proven to be critical to our understanding of the true effects that specific genetic variants have on these traits, as most common variants have small effects. In general, individual studies – which typically assess a few hundred to a thousand individuals – are underpowered to reliably detect associations. Recently, we initiated the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium (Stein et al., 2012
) . The primary goal of the ENIGMA consortium is to expedite meta-analysis of large datasets and create a forum for collaboration in the field of imaging genetics. The effort is modeled on other highly successful consortia in psychiatric genetics, which have discovered genetic loci associated with bipolar illness, schizophrenia, and ADHD (Neale et al., 2010
; Ripke et al., 2011
; Sklar et al., 2011
) , offering new leads for research at the molecular and systems levels. In several ongoing projects, the ENIGMA consortium has been analyzing the genetic influences on neuroimaging traits with data from over 20 research groups and tens of thousands of subjects. This presents a useful resource for the imaging, neuropsychiatric, and cognitive genetics communities to discover genes that influence the brain. It also facilitates the confirmation, replication and understanding of effects of promising genetic variations and pathways.
A method for user-friendly visualization and navigation of the genetic regions containing important associations is essential for demonstrating significant findings and distributing these results. For initiatives such as ENIGMA, analyzing very large amounts of GWAS data, it is critical that researchers are able to quickly and efficiently examine the strength of the association at any desired genetic loci. Specifically, researchers may want to look up the evidence of genetic association between a gene they are interested in, and various brain measures examined by ENIGMA. As such, a visualization utility may prove to be one of the most useful methods to facilitate interpretation of ENIGMA results. Conventional publication formats for GWAS make it difficult for those not closely involved in the study to access and browse the results. Top hits are usually summarized in tables, which lose a great deal of the data that is available at other loci across the genome. Additionally, conventional formats rely on non-visual methods for data presentation, often consisting of lists of SNP numbers and probabilities of association that are very hard to digest. Exiting online data visualization tools – SzGene, AlzGene, LocusZoom, Ricopili – for genetic association studies (Allen et al., 2008
; Bertram et al., 2007
; Pruim et al., 2010
; Ripke & Thomas, 2011
) are available but with some limitations. All of these existing tools are static in nature, and the SzGene/AlzGene databases are based primarily on candidate-gene studies only. Additionally, these tools are not available for imaging genetics data. To this end, we developed EnigmaVis, an online interactive tool for visualizing unbiased genome-wide association results from ENIGMA.