Genetic Association Studies in Host–Pathogen Interaction Analysis

  • Jose Luis RoyoEmail author
  • Luis Miguel Real
Part of the Methods in Molecular Biology book series (MIMB, volume 1734)


Studying host–pathogen interactions at a molecular level has been always technically challenging. Identifying the different biochemical and genetic pathways involved in the different stages of infection traditionally require complex molecular biology tools and often the use of costly animal models. In this chapter we illustrate a complementary approach to address host–pathogen interactions, taking advantage of the natural interindividual genetic diversity. The application of genetic association studies allows us to identify alleles involved in infection progression or resistance. Thus, this strategy may be useful to unravel new molecular pathways underlying host–pathogen interactions. Here we present the general steps that might be followed to plan, execute, and analyze a population-based study in order to identify genetic variants affecting human exposition to pathogens.

Key words

Host–pathogen genetics Association study Case–control study Study design 


  1. 1.
    Cooke GS, Hill AV (2001) Genetics of susceptibility to human infectious disease. Nat Rev Genet 2:967–977CrossRefPubMedGoogle Scholar
  2. 2.
    Kimman T (2001) Genetics of infectious disease susceptibility. Springer, BerlinGoogle Scholar
  3. 3.
    Dean M, Carrington M, Winkler C et al (1996) Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Haemophilia growth and development study, multicenter AIDS cohort study, multicenter Haemophilia cohort study, San Francisco City Cohort, ALIVE study. Science 273:1856–1862CrossRefPubMedGoogle Scholar
  4. 4.
    Samson M, Libert F, Doranz BJ et al (1996) Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 382:722–725CrossRefPubMedGoogle Scholar
  5. 5.
    Fisher A (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 53:399–433Google Scholar
  6. 6.
    Plomin R, Haworth CM, Davis OS (2009) Common disorders are quantitative traits. Nat Rev Genet 10:872–878CrossRefPubMedGoogle Scholar
  7. 7.
    Sugden PB, Cameron B, Luciani F, Lloyd AR (2014) Exploration of genetically determined resistance against hepatitis C infection in high-risk injecting drug users. J Viral Hepat 21:e65–e73CrossRefPubMedGoogle Scholar
  8. 8.
    Real LM, Herrero R, Rivero-Juarez A et al (2015) IFNL4 rs368234815 polymorphism is associated with innate resistance to HIV-1 infection. AIDS 29:1895–1897CrossRefPubMedGoogle Scholar
  9. 9.
    Sironi M, Biasin M, Gnudi F et al (2014) A regulatory polymorphism in HAVCR2 modulates susceptibility to HIV-1 infection. PLoS One 9:e106442CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    McLaren PJ, Coulonges C, Ripke S et al (2013) Association study of common genetic variants and HIV-1 acquisition in 6,300 infected cases and 7,200 controls. PLoS Pathog 9:e1003515CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Solé X, Guinó E, Valls J, Iniesta R, Moreno V (2006) SNPStats: a web tool for the analysis of association studies. Bioinformatics Aug 1;22(15):1928–1929Google Scholar
  13. 13.
    Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  14. 14.
    Gayan J, Gonzalez-Perez A, Bermudo F et al (2008) A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. BMC Genomics 9:360CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Ionita I, Man M (2006) Optimal two-stage strategy for detecting interacting genes in complex diseases. BMC Genet 7:39PubMedGoogle Scholar
  16. 16.
    Zhang Y, Liu JS (2007) Bayesian inference of epistatic interactions in case-control studies. Nat Genet 39:1167–1173CrossRefPubMedGoogle Scholar
  17. 17.
    Rauch A, Kutalik Z, Descombes P et al (2010) Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 138:1338–1345CrossRefPubMedGoogle Scholar
  18. 18.
    Ge D, Fellay J, Thompson AJ et al (2009) Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461:399–401CrossRefPubMedGoogle Scholar
  19. 19.
    Mandorfer M, Neukam K, Reiberger T et al (2013) The impact of interleukin 28B rs12979860 single nucleotide polymorphism and liver fibrosis stage on response-guided therapy in HIV/HCV-coinfected patients. AIDS 27:2707–2714CrossRefPubMedGoogle Scholar
  20. 20.
    Real LM, Neukam K, Herrero R et al (2014) IFNL4 ss469415590 variant shows similar performance to rs12979860 as predictor of response to treatment against hepatitis C virus genotype 1 or 4 in Caucasians. PLoS One 9:e95515CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Prokunina-Olsson L, Muchmore B, Tang W, Pfeiffer RM et al (2013) A variant upstream of IFNL3 (IL28B) creating a new interferon gene IFNL4 is associated with impaired clearance of hepatitis C virus. Nat Genet 45:164–171CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Band G, Le QS, Jostins L, Pirinen M et al (2013) Imputation-based meta-analysis of severe malaria in three African populations. PLoS Genet 9:e1003509CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Jallow M, Teo YY, Small KS et al (2009) Genome-wide and fine-resolution association analysis of malaria in West Africa. Nat Genet 41:657–665CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Timmann C, Thye T, Vens M et al (2012) Genome-wide association study indicates two novel resistance loci for severe malaria. Nature 489:443–446CrossRefPubMedGoogle Scholar
  25. 25.
    Manjurano A, Sepulveda N, Nadjm B et al (2015) USP38, FREM3, SDC1, DDC, and LOC727982 gene polymorphisms and differential susceptibility to severe malaria in Tanzania. J Infect Dis 212:1129–1139CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.Departamento de Bioquímica, Biología Molecular e Inmunología, Facultad de MedicinaUniversidad de MálagaMálagaSpain
  2. 2.Unidad Clínica de Enfermedades Infecciosas y MicrobiologíaHospital Universitario de ValmeSevilleSpain

Personalised recommendations