VEGAS

Versatile Gene-based Association Study

File input

Upload GWAS results:

Options

Use SNPs from

In addition to the default test, also consider:

Top-x% test with top percent

or Top-SNP test

Email and submit

Email (results to be sent):



About
  • VEGAS is a free program for performing gene-based tests for association using the results from genetic association studies. After reading in SNP association p-values, it will annotate SNPs according to their position in genes, produce a gene-based test statistic, and then use simulation to calculate an empirical gene-based p-value.
  • The online version here uses HapMap populations to estimate patterns of linkage disequlibrium for each gene. The offline version below allows the use of individual genotype data if available.
  • A full description of how VEGAS works is given in our paper.

Input format
  • Upload association results as a text file with two columns (tab or space-delimited, no headers) - SNP rs-number and p-value.
  • See a sample input file. This should take ~20 minutes to complete.
  • Users should not filter their association results by p-value prior to uploading. VEGAS was designed to take into account the effects of all SNPs within a gene when producing a gene-based p-value. This is also true for the top-x% and top-SNP tests.
  • For genome-wide analysis, we recommend users try the offline version below. Running VEGAS with association results for ~2.3million SNPs may take ~12-16 hours to complete.

Output file
  • Once the analysis is complete, an email will be sent to you with a link to download the results file.
  • This is a plain-text file with the columns: Chromosome, Gene, Number of SNPs, Number of simulations, Start position, Stop position, Gene-based test statistic, P-value. If the Top-x% test or Top-SNP test is selected, there will be an additional column indicating the Top-x%/Top-SNP p-value

Offline version

Publications
  • Our paper describing VEGAS is:
  • Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, AMFS Investigators, Hayward NK, Montgomery GW, Visscher PM, Martin NG, MacGregor S. (2010). A Versatile Gene-Based Test for Genome-wide Association Studies. American Journal of Human Genetics, 87. [doi] [pdf]
    Please cite this paper if you have used VEGAS in your research. We would love to hear from you!
  • Publications that have succesfully used VEGAS:
    1. Verweij KJ, Zietsch BP, Medland SE, Gordon SD, Benyamin B, Nyholt DR, McEvoy BP, Sullivan PF, Heath AC, Madden PA, Henders AK, Montgomery GW, Martin NG, Wray NR. (2010). A genome-wide association study of Cloninger's tempermant scales: Implications for the evolutionary genetics of personality. Biological Psychology, e-pub 04 Aug. [doi]
    2. Mosing MA, Verweij KJ, Medland SE, Painter J, Gordon SD, Heath AC, Madden PA, Montgomery GW, Martin NG. (2010). A genome-wide association study of self-rated health. Twin Research and Human Genetics 13:398-403. [doi]
    3. Meta-analysis of genome-wide association studies for personality. de Moor MH, Costa PT, Terracciano A, Krueger RF, de Geus EJ, Toshiko T, Penninx BW, Esko T, Madden PA, Derringer J, Amin N, Willemsen G, Hottenga JJ, Distel MA, Uda M, Sanna S, Spinhoven P, Hartman CA, Sullivan P, Realo A, Allik J, Heath AC, Pergadia ML, Agrawal A, Lin P, Grucza R, Nutile T, Ciullo M, Rujescu D, Giegling I, Konte B, Widen E, Cousminer DL, Eriksson JG, Palotie A, Peltonen L, Luciano M, Tenesa A, Davies G, Lopez LM, Hansell NK, Medland SE, Ferrucci L, Schlessinger D, Montgomery GW, Wright MJ, Aulchenko YS, Janssens AC, Oostra BA, Metspalu A, Abecasis GR, Deary IJ, Räikkönen K, Bierut LJ, Martin NG, van Duijn CM, Boomsma DI. Mol Psychiatry. 2010 Dec 21. [Epub ahead of print]
    4. Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Wray NR, Pergadia ML, Blackwood DH, Penninx BW, Gordon SD, Nyholt DR, Ripke S, Macintyre DJ, McGhee KA, Maclean AW, Smit JH, Hottenga JJ, Willemsen G, Middeldorp CM, de Geus EJ, Lewis CM, McGuffin P, Hickie IB, van den Oord EJ, Liu JZ, Macgregor S, McEvoy BP, Byrne EM, Medland SE, Statham DJ, Henders AK, Heath AC, Montgomery GW, Martin NG, Boomsma DI, Madden PA, Sullivan PF. Mol Psychiatry. 2010 Nov 2. [Epub ahead of print]
    5. Genome-Wide Meta-Analysis Identifies Regions on 7p21 (AHR) and 15q24 (CYP1A2) As Determinants of Habitual Caffeine Consumption. Cornelis MC, Monda KL, Yu K, Paynter N, Azzato EM, Bennett SN, Berndt SI, Boerwinkle E, Chanock S, Chatterjee N, Couper D, Curhan G, Heiss G, Hu FB, Hunter DJ, Jacobs K, Jensen MK, Kraft P, Landi MT, Nettleton JA, Purdue MP, Rajaraman P, Rimm EB, Rose LM, Rothman N, Silverman D, Stolzenberg-Solomon R, Subar A, Yeager M, Chasman DI, van Dam RM, Caporaso NE. PLoS Genet. 2011 Apr;7(4):e1002033. Epub 2011 Apr 7.
    6. Identification of QTL genes for BMD variation using both linkage and gene-based association approaches. Li GH, Cheung CL, Xiao SM, Lau KS, Gao Y, Bow CH, Huang QY, Sham PC, Kung AW. Hum Genet. 2011 Mar 19. [Epub ahead of print]
    7. Meta-analysis of genome-wide association for migraine in six population-based European cohorts. Ligthart L, de Vries B, Smith AV, Ikram MA, Amin N, Hottenga JJ, Koelewijn SC, Kattenberg VM, de Moor MH, Janssens AC, Aulchenko YS, Oostra BA, de Geus EJ, Smit JH, Zitman FG, Uitterlinden AG, Hofman A, Willemsen G, Nyholt DR, Montgomery GW, Terwindt GM, Gudnason V, Penninx BW, Breteler M, Ferrari MD, Launer LJ, van Duijn CM, van den Maagdenberg AM, Boomsma DI. Eur J Hum Genet. 2011 Mar 30. [Epub ahead of print]

Jimmy Liu (jimmy dot liu at qimr dot edu dot au) and Stuart MacGregor (stuart dot macgregor at qimr dot edu dot au), Queensland Statistical Genetics, Queensland Institute of Medical Research. Last updated August 27 2010.