Abstract
Motivation For over a decade, genome-wide association studies (GWAS) have been an important method for mapping genetic variation underlying complex traits. With the ever-increasing volume of data generated however, new tools are needed to integrate the vast array of GWAS results and perform further analyses in order to maximize their utility for biological discovery. Here we present the Complex-Traits Genetics Virtual Lab (CTG-VL) — https://genoma.io.
Results CTG-VL integrates several key components: (i) publicly available GWAS summary statistics; (ii) a suite of analysis tools; (iii) visualization functions; and (iv) data sets for genomic annotations. The platform also makes available results from gene-, pathway- and tissue-based analyses of >1,500 complex traits for assessing pleiotropy at the genetic variant through to these higher levels. Here we demonstrate the platform by re-analysing GWAS summary statistics of back pain (N=509,070). Using analysis tools in CTG-VL we identified 59 genes, of which 20 are across 10 loci outside the original GWAS signals — including NTRK1 — which is important for the development of pain-mediating sensory neurons. Further, we found enrichment for a number of central nervous system regions in back pain, and evidence for a potential causal relationship with height (OR = 1.06 per cm; 95%CI = 1.04 – 1.08). Using CTG-VL’s database, we show biological pathways associated with back pain are also associated with other traits such as self-reported temperament (‘highly strung’), walking and mood swings.
Conclusions CTG-VL is a freely available online web application to further harness GWAS data for research reproducibility, collaboration and translation.
Footnotes
↵† Co-last authors
We now describe the additional functions integrated into the CTG-VL. Previously version 0.35-alpha was described now is 0.39-alpha. We have also updated the case study.
