Our new post-GWAS analysis method (network density analysis; NDA) reveals new biological features of numerous disease states and traits. It works by examining a coexpression network of transcription start sites (discovered in FANTOM5). We find that transcripts containing GWAS hits for a given trait tend to fall into more dense groupings in the coexpression network than randomly-selected transcripts.
NDA demonstrates that GWAS hits for a given disease tend to be near promoter/enhancer elements with similar expression profiles, which enables us to find more hits, fine map probable causative SNPs, and implicate cell types in pathogenesis. Surprisingly, for some diseases, the underlying variants fall into distinct functional groups, suggesting either dual mechanisms of disease, or distinct disease endotypes.Baillie JK et al. “Shared Activity Patterns Arising at Genetic Susceptibility Loci Reveal Underlying Genomic and Cellular Architecture of Human Disease.” PLOS Computational Biology 14, no. 3 (March 1, 2018): e1005934. https://doi.org/10.1371/journal.pcbi.1005934.
Upload a list of SNPs (or genomic locations) in the format described below. The SNPs should share some common feature, such as putative association with a given phenotype at a permissive p-value threshold (eg. 5e-6), such that it might reasonably be expected that some of the SNPs in the entry set will share an expression profile across the FANTOM5 expression atlas.
chr start end [optional_snp_id]
If you submit a correctly-formatted file using the form above, your job will be entered into our queue for running on the Roslin Institute servers. Very large jobs (those with more than 1000 SNPs, or more than 400 SNPs mapping to FANTOM5 TSS) may take a long time and these will be pushed down the queue during busy periods, and may be cancelled if they are taking too long. Please contact us () if you have a very large job, or download our code below and run it on your own server.
For a full explanation of the network density analysis method, see Baillie JK et al. “Shared Activity Patterns Arising at Genetic Susceptibility Loci Reveal Underlying Genomic and Cellular Architecture of Human Disease.” PLOS Computational Biology 14, no. 3 (March 1, 2018): e1005934. https://doi.org/10.1371/journal.pcbi.1005934..)
|Height||8882 snps searched||471 promoters hit||166 distinct regions mapped||29 significantly-coexpressed regions|
|Total Cholesterol||6421 snps searched||519 promoters hit||128 distinct regions mapped||29 significantly-coexpressed regions|
|Low-density lipoprotein||4644 snps searched||321 promoters hit||92 distinct regions mapped||19 significantly-coexpressed regions|
|High-density lipoprotein||5410 snps searched||450 promoters hit||101 distinct regions mapped||17 significantly-coexpressed regions|
|Triglycerides||4863 snps searched||437 promoters hit||97 distinct regions mapped||23 significantly-coexpressed regions|
|Ulcerative colitis||2162 snps searched||234 promoters hit||83 distinct regions mapped||20 significantly-coexpressed regions|
|Crohn's disease||1924 snps searched||217 promoters hit||70 distinct regions mapped||23 significantly-coexpressed regions|
|Systolic Blood Pressure||417 snps searched||25 promoters hit||13 distinct regions mapped|
|Diastolic Blood Pressure||711 snps searched||26 promoters hit||14 distinct regions mapped|
We are very grateful to recieve funding from the following sources: Wellcome Trust, BBSRC, Intensive Care Society. MRC, NIH.