Skip to main content
SpringerLink
Log in

Analytical Approaches to Uncover Genetic Associations for Rare Outcomes: Lessons from West Nile Neuroinvasive Disease

  • Protocol
  • First Online:
West Nile Virus

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2585))

Abstract

West Nile viral infection causes severe neuroinvasive disease in less than 1% of infected humans. There are no targeted therapeutics for this serious and potentially fatal disease, and to date no vaccine has been approved for humans. With climate change expected to result in rising incidence of West Nile and other related vector-borne viral infections, there is an increasing need to identify those at risk for serious disease and potential leads for therapeutic and vaccine development. Genetic variation, particularly in genes whose products are either directly or indirectly connected to immune response to infections, is a critical avenue of investigation to identify those at higher risk of clinically apparent West Nile infection. Given the small percent of infections that progress to severe disease and the relatively low numbers of reported infections, it is challenging to conduct well-powered studies to identify genetic factors associated with more severe outcomes. In this chapter, we outline several approaches with the objective to take full advantage of all available data in order to identify genetic factors which lead to increased risk of severe West Nile neuroinvasive disease. These methods are generalizable to other conditions with limited cohort size and rare outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Debiasi RL (2011) West Nile virus neuroinvasive disease. Curr Infect Dis Rep 13(4):350–359. https://doi.org/10.1007/s11908-011-0193-9

    Article  PubMed  Google Scholar 

  2. Centers for Disease Prevention and Control: West Nile virus: symptoms, diagnosis, & treatment (2018) https://www.cdc.gov/westnile/symptoms/index.html. Accessed December 16, 2021

  3. Cahill ME, Conley S, DeWan AT et al (2018) Identification of genetic variants associated with dengue or West Nile virus disease: a systematic review and meta-analysis. BMC Infect Dis 18(1):282. https://doi.org/10.1186/s12879-018-3186-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. COVID-19 Host Genetics Initiative (2021) Mapping the human genetic architecture of COVID-19. Nature 600(7889):472–477. https://doi.org/10.1038/s41586-021-03767-x

    Article  CAS  Google Scholar 

  5. Pairo-Castineira E, Clohisey S, Klaric L et al (2021) Genetic mechanisms of critical illness in COVID-19. Nature 591(7848):92–98. https://doi.org/10.1038/s41586-020-03065-y

    Article  CAS  PubMed  Google Scholar 

  6. Loeb M, Eskandarian S, Rupp M et al (2011) Genetic variants and susceptibility to neurological complications following West Nile virus infection. J Infect Dis 204(7):1031–1037. https://doi.org/10.1093/infdis/jir493

    Article  CAS  PubMed  Google Scholar 

  7. Chancey C, Grinev A, Volkova E et al (2015) The global ecology and epidemiology of West Nile virus. Biomed Res Int 2015:376230. https://doi.org/10.1155/2015/376230

    Article  PubMed  PubMed Central  Google Scholar 

  8. Paz S (2015) Climate change impacts on West Nile virus transmission in a global context. Philos Trans R Soc Lond B Biol Sci 370(1665). https://doi.org/10.1098/rstb.2013.0561

  9. Marees AT, de Kluiver H, Stringer S et al (2018) A tutorial on conducting genome-wide association studies: quality control and statistical analysis. Int J Methods Psychiatr Res 27(2):e1608. https://doi.org/10.1002/mpr.1608

    Article  PubMed  PubMed Central  Google Scholar 

  10. Turner S, Armstrong LL, Bradford Y, et al (2011) Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet;Chapter 1:Unit1 19. https://doi.org/10.1002/0471142905.hg0119s68

  11. Cahill ME, Loeb M, Dewan AT et al (2020) In-depth analysis of genetic variation associated with severe West Nile viral disease. Vaccines (Basel) 8(4). https://doi.org/10.3390/vaccines8040744

  12. Wood AR, Perry JR, Tanaka T et al (2013) Imputation of variants from the 1000 genomes project modestly improves known associations and can identify low-frequency variant-phenotype associations undetected by HapMap based imputation. PLoS One 8(5):e64343. https://doi.org/10.1371/journal.pone.0064343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Das S, Forer L, Schonherr S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48(10):1284–1287. https://doi.org/10.1038/ng.3656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Christian Fuchsberger LF, Schoenherr S, Das S, Abecasis G (2021) Michigan imputation server: Free next-generation genotype imputation service. https://imputationserver.sph.umich.edu/. Accessed October 2, 2021

  15. Kent WJ, Sugnet CW, Furey TS et al (2002) The human genome browser at UCSC. Genome Res 12(6):996–1006. https://doi.org/10.1101/gr.229102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. University of California Santa Cruz Genomics Institute: lift genome annotations. https://genome.ucsc.edu/cgi-bin/hgLiftOver. Accessed October 5, 2021

  17. Delaneau O, Marchini J, Zagury JF (2011) A linear complexity phasing method for thousands of genomes. Nat Methods 9(2):179–181. https://doi.org/10.1038/nmeth.1785

    Article  CAS  PubMed  Google Scholar 

  18. Delaneau O, Zagury JF, Marchini J (2013) Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 10(1):5–6. https://doi.org/10.1038/nmeth.2307

    Article  CAS  PubMed  Google Scholar 

  19. Delaneau O. SHAPEIT. https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/. Accessed October 5, 2021

  20. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529. https://doi.org/10.1371/journal.pgen.1000529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 81(5):1084–1097. https://doi.org/10.1086/521987

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Howie B, Fuchsberger C, Stephens M et al (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44(8):955–959. https://doi.org/10.1038/ng.2354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Shi S, Yuan N, Yang M et al (2018) Comprehensive assessment of genotype imputation performance. Hum Hered 83(3):107–116. https://doi.org/10.1159/000489758

    Article  CAS  PubMed  Google Scholar 

  24. Roshyara NR, Horn K, Kirsten H et al (2016) Comparing performance of modern genotype imputation methods in different ethnicities. Sci Rep 6:34386. https://doi.org/10.1038/srep34386

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Clarke L, Fairley S, Zheng-Bradley X et al (2017) The International Genome Sample Resource (IGSR): a worldwide collection of genome variation incorporating the 1000 genomes project data. Nucleic Acids Res 45(D1):D854–D8D9. https://doi.org/10.1093/nar/gkw829

    Article  CAS  PubMed  Google Scholar 

  26. The 1000 Genomes Project Consortium (2015) A global reference for human genetic variation. Nature 526(7571):68–74. https://doi.org/10.1038/nature15393

    Article  CAS  Google Scholar 

  27. International Genome Sample Resource: Data Portal (2021) https://www.internationalgenome.org/data Accessed December 2, 2021

  28. Huang GH, Tseng YC. Genotype imputation accuracy with different reference panels in admixed populations. BMC Proc. 2014;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S64. https://doi.org/10.1186/1753-6561-8-S1-S64

  29. Howie B, Marchini J. Impute2: analyzing whole chromosomes. https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#whole_chroms. Accessed November 15, 2021

  30. Luan JT, Teumer A, Zhao J, Fuchsberger C, Willer C (2012) IMPUTE2: 1000 genomes imputation cookbook. https://genome.sph.umich.edu/wiki/IMPUTE2:_1000_Genomes_Imputation_Cookbook. Accessed October 5, 2021

  31. University of California Santa Cruz Genomics Institute: Cytoband. http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cytoBand.txt.gz. Accessed November 20, 2021

  32. Howie B, Marchini J. Impute2: details about ‘info’ metric. https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#info_metric_details. Accessed October 5, 2021

  33. Purcell S (2021) PLINK 1.9 input filtering. https://www.cog-genomics.org/plink/1.9/filter. Accessed November 15, 2021

  34. Cordell HJ (2002) Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11(20):2463–2468. https://doi.org/10.1093/hmg/11.20.2463

    Article  CAS  PubMed  Google Scholar 

  35. Panagiotou OA, Evangelou E, Ioannidis JP (2010) Genome-wide significant associations for variants with minor allele frequency of 5% or less – an overview: a HuGE review. Am J Epidemiol 172(8):869–889. https://doi.org/10.1093/aje/kwq234

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tryka KA, Hao L, Sturcke A et al (2014) NCBI’s database of genotypes and phenotypes: dbGaP. Nucleic Acids Res 42(Database issue):D975–D979. https://doi.org/10.1093/nar/gkt1211

    Article  CAS  PubMed  Google Scholar 

  37. National Center for Biotechnology Information, National Library of Medicine: Database of Genotypes and Phenotypes (dbGaP). https://www.ncbi.nlm.nih.gov/gap/. Accessed December 5, 2021

  38. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12):e190. https://doi.org/10.1371/journal.pgen.0020190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909. https://doi.org/10.1038/ng1847

    Article  CAS  PubMed  Google Scholar 

  40. Auer PL, Lettre G (2015) Rare variant association studies: considerations, challenges and opportunities. Genome Med 7(1):16. https://doi.org/10.1186/s13073-015-0138-2

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cirillo E, Parnell LD, Evelo CT (2017) A review of pathway-based analysis tools that visualize genetic variants. Front Genet 8:174. https://doi.org/10.3389/fgene.2017.00174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cooley PCC, Clark RF, Folsom RE (2014) Assessing gene-environment interactions in genome-wide association studies: statistical approaches, RTI Press research report series. Research Triangle Institute, Research Triangle Park

    Book  Google Scholar 

  43. Lin WY, Huang CC, Liu YL et al (2018) Genome-wide gene-environment interaction analysis using set-based association tests. Front Genet 9:715. https://doi.org/10.3389/fgene.2018.00715

    Article  CAS  PubMed  Google Scholar 

  44. European Centre for Disease Prevention and Control (2021) West Nile virus infection. Annual epidemiological report for 2019. ECDC, Stockholm

    Google Scholar 

  45. Wojcik GL, Graff M, Nishimura KK et al (2019) Genetic analyses of diverse populations improves discovery for complex traits. Nature 570(7762):514–518. https://doi.org/10.1038/s41586-019-1310-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Mills MC, Rahal C (2020) The GWAS diversity monitor tracks diversity by disease in real time. Nat Genet 52(3):242–243. https://doi.org/10.1038/s41588-020-0580-y

    Article  CAS  PubMed  Google Scholar 

  47. Peterson RE, Kuchenbaecker K, Walters RK et al (2019) Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179(3):589–603. https://doi.org/10.1016/j.cell.2019.08.051

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Atkinson EG, Maihofer AX, Kanai M et al (2021) Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet 53(2):195–204. https://doi.org/10.1038/s41588-020-00766-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cahill ME, Yao Y, Nock D et al (2017) West Nile virus seroprevalence, Connecticut, USA, 2000–2014. Emerg Infect Dis 23(4):708–710. https://doi.org/10.3201/eid2304.161669

    Article  PubMed  PubMed Central  Google Scholar 

  50. Garcia MN, Hause AM, Walker CM et al (2014) Evaluation of prolonged fatigue post-West Nile virus infection and association of fatigue with elevated antiviral and proinflammatory cytokines. Viral Immunol 27(7):327–333. https://doi.org/10.1089/vim.2014.0035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Beloor J, Maes N, Ullah I et al (2018) Small interfering RNA-mediated control of virus replication in the CNS is therapeutic and enables natural immunity to West Nile virus. Cell Host Microbe 23(4):549–56 e3. https://doi.org/10.1016/j.chom.2018.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Diamond MS (2009) Progress on the development of therapeutics against West Nile virus. Antivir Res 83(3):214–227. https://doi.org/10.1016/j.antiviral.2009.05.006

    Article  CAS  PubMed  Google Scholar 

  53. Ulbert S (2019) West Nile virus vaccines – current situation and future directions. Hum Vaccin Immunother 15(10):2337–2342. https://doi.org/10.1080/21645515.2019.1621149

    Article  PubMed  PubMed Central  Google Scholar 

  54. Bai F, Thompson EA, Vig PJS et al (2019) Current understanding of West Nile virus clinical manifestations, immune responses, neuroinvasion, and immunotherapeutic implications. Pathogens 8(4). https://doi.org/10.3390/pathogens8040193

  55. Bouaziz M, Ambroise C, Guedj M (2011) Accounting for population stratification in practice: a comparison of the main strategies dedicated to genome-wide association studies. PLoS One 6(12):e28845. https://doi.org/10.1371/journal.pone.0028845

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhao H, Mitra N, Kanetsky PA et al (2018) A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS). Stat Appl Genet Mol Biol 17(6). https://doi.org/10.1515/sagmb-2017-0054

  57. Cross-Disorder Group of the Psychiatric Genomics Consortium (2019) Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179(7):1469–82 e11. https://doi.org/10.1016/j.cell.2019.11.020

    Article  CAS  PubMed Central  Google Scholar 

  58. Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145):661–678. https://doi.org/10.1038/nature05911

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the US National Institutes of Health (NIH)/National Institute of Allergy and Infectious Diseases (NIAID) Human Immunology Project Consortium (HIPC) award U19 AI 089992. The authors are grateful to Dr. Andrew Dewan for expert guidance and Ms. Xiaomei Wang for valuable support.

Author information

Authors and Affiliations

  1. Department of Chronic Disease Epidemiology and the Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, USA

    Megan E. Cahill

  2. Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Ruth R. Montgomery

Authors
  1. Megan E. Cahill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruth R. Montgomery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth R. Montgomery .

Editor information

Editors and Affiliations

  1. Department of Cell and Molecular Biology, Center for Molecular and Cellular Biosciences The University of Southern Mississippi, Hattiesburg, MS, USA

    Fengwei Bai

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cahill, M.E., Montgomery, R.R. (2023). Analytical Approaches to Uncover Genetic Associations for Rare Outcomes: Lessons from West Nile Neuroinvasive Disease. In: Bai, F. (eds) West Nile Virus. Methods in Molecular Biology, vol 2585. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2760-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2760-0_17

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2759-4

  • Online ISBN: 978-1-0716-2760-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation