EVE: Cloud-Based Annotation of Human Genetic Variants

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Annotation of human genetic variants enables genotype-phenotype association studies at the gene, pathway, and tissue level. Annotation results are difficult to reproduce across study sites due to shifting software versions and a lack of a unified hardware interface between study sites. Cloud computing offers a promising solution by integrating hardware and software into reproducible virtual appliances which may be utilized on-demand and shared across institutions. We developed ENSEMBL VEP on EC2 (EVE), a cloud-based virtual appliance for annotation of human genetic variants built around the ENSEMBL Variant Effect Predictor. We integrated virtual hardware infrastructure, open-source software, and publicly available genomic datasets to provide annotation capability for genetic variants in the context of genes/transcripts, Gene Ontology pathways, tissue-specific expression from the Gene Expression Atlas, miRNA annotations, minor allele frequencies from the 1000 Genomes Project and the Exome Aggregation Consortium, and deleteriousness scores from Combined Annotation Dependent Depletion. We demonstrate the utility of EVE by annotating the genetic variants in a case-control study of glaucoma. Cloud computing can reduce the difficulty of replicating complex software pipelines such as annotation pipelines across study sites. We provide a publicly available CloudFormation template of the EVE virtual appliance which can automatically provision and deploy a parameterized, preconfigured hardware/software stack ready for annotation of human genetic variants (github.com/epistasislab/EVE). This approach offers increased reproducibility in human genetic studies by providing a unified appliance to researchers across the world.

Keywords

Annotation GWAS Cloud computing Reproducibility Infrastructure-as-Code 

Notes

Acknowledgements

This work is supported by an Amazon Web Services Cloud Credits for Research award to BSC and NIH AI116794 to JHM.

References

  1. 1.
    Klein, R., Zeiss, C., Chew, E., Tsai, J.: Complement factor H polymorphism in age-related macular degeneration. Science 308(5720), 385–389 (2005). doi: 10.1126/science.1109557.ComplementCrossRefGoogle Scholar
  2. 2.
    Welter, D., MacArthur, J., Morales, J., et al.: The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42(D1), 1001–1006 (2014). doi: 10.1093/nar/gkt1229CrossRefGoogle Scholar
  3. 3.
    Witte, J.S.: Genome-wide association studies and beyond. Annu. Rev. Public Health 77, 9–20 (2014). doi: 10.1146/annurev.publhealth.012809.103723.Genome-WideGoogle Scholar
  4. 4.
    Manolio, T.A.: Genomewide association studies and assessment of risk of disease. N. Engl. J. Med. 363, 2076–2077 (2010). doi: 10.1056/NEJMc1010310CrossRefGoogle Scholar
  5. 5.
    Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics challenges for genome-wide association studies. Bioinformatics 26(4), 445–455 (2010). doi: 10.1093/bioinformatics/btp713CrossRefGoogle Scholar
  6. 6.
    Greene, C.S., Voight, B.F.: Pathway and network-based strategies to translate genetic discoveries into effective therapies. Hum. Mol. Genet., 1–5 (2016). doi: 10.1093/hmg/ddw160
  7. 7.
    Greene, C.S., Krishnan, A., Wong, A.K., et al.: Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47(6) (2015). doi: 10.1038/ng.3259
  8. 8.
    McLaren, W., Gil, L., Hunt, S.E., et al.: The ensembl variant effect predictor. Genome Biol. 17(122) (2016). doi: 10.1186/s13059-016-0974-4
  9. 9.
    Wang, K., Li, M., Hakonarson, H.: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38(16), e164 (2010). doi: 10.1093/nar/gkq603CrossRefGoogle Scholar
  10. 10.
    Evangelou, E., Ioannidis, J.P.A.: Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14(6), 379–389 (2013). doi: 10.1038/nrg3472CrossRefGoogle Scholar
  11. 11.
    Purcell, S., Neale, B., Todd-Brown, K., et al.: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575 (2007). doi: 10.1086/519795CrossRefGoogle Scholar
  12. 12.
    Lek, M., Karczewski, K.J., Minikel, E.V., et al.: Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616), 285–291 (2016). doi: 10.1038/nature19057CrossRefGoogle Scholar
  13. 13.
    Kircher, M.: A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46(3), 310–315 (2014). doi: 10.1038/ng.2892.ACrossRefGoogle Scholar
  14. 14.
    Consortium TGO: Gene ontologie: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000). doi: 10.1038/75556.GeneCrossRefGoogle Scholar
  15. 15.
    Kapushesky, M., Adamusiak, T., Burdett, T., et al.: Gene Expression Atlas update–a value-added database of microarray and sequencing-based functional genomics experiments. Nucleic Acids Res. 40(Database issue), D1077-81 (2012). doi: 10.1093/nar/gkr913
  16. 16.
    Wiggs, J.L., Hauser, M.A., Abdrabou, W., et al.: The NEIGHBOR consortium primary open angle glaucoma genome-wide association study: rationale, study design and clinical variables. J. Glaucoma 22(7), 517–525 (2013). doi: 10.1097/IJG.0b013e31824d4fd8CrossRefGoogle Scholar
  17. 17.
    Wiggs, J.L., Yaspan, B.L., Hauser, M.A., et al.: Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. PLoS Genet. 8(4) (2012). doi: 10.1371/journal.pgen.1002654
  18. 18.
    Anderson, C.A., Pettersson, F.H., Clarke, G.M., Cardon, L.R., Morris, A.P., Zondervan, K.T.: Data quality control in genetic case-control association studies. Nat. Protoc. 5(9), 1564–1573 (2010). doi: 10.1038/nprot.2010.116CrossRefGoogle Scholar
  19. 19.
    Begley, C.G., Ioannidis, J.P.A.: Reproducibility in science: improving the standard for basic and preclinical research. Circ. Res. 116(1), 116–126 (2015). doi: 10.1161/CIRCRESAHA.114.303819CrossRefGoogle Scholar
  20. 20.
    Peng, R.D.: Reproducible research in computational science. Science 334(6060), 1226–1227 (2011). doi: 10.1126/science.1213847CrossRefGoogle Scholar
  21. 21.
    Stein, L.D., Knopers, B.M., Campell, P., Getz, G., Korbel, J.O.: Create a cloud commons. Nature 523, 149–151 (2015). doi: 10.1038/523149aCrossRefGoogle Scholar
  22. 22.
    Project Consortium G, Consortium Participants are arranged by project role G, by institution alphabetically then, et al.: An integrated map of genetic variation from 1,092 human genomes. Nature 490(7422), 56–65 (2012). doi: 10.1038/nature11632
  23. 23.
    McLendon, R., Friedman, A., Bigner, D., et al.: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216), 1061–1068 (2008). doi: 10.1038/nature07385CrossRefGoogle Scholar
  24. 24.
    Li, J., Doyle, M.A., Saeed, I., et al.: Bioinformatics pipelines for targeted resequencing and whole-exome sequencing of human and mouse genomes: a virtual appliance approach for instant deployment. PLoS One 9(4) (2014). doi: 10.1371/journal.pone.0095217

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Biostatistics and Epidemiology, Perelman School of Medicine, Institute for Biomedical InformaticsUniversity of PennsylvaniaPhiladelphiaUSA

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