Abstract
Studying host-pathogen interactions at a molecular level has always been 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.
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References
Cooke GS, Hill AV (2001) Genetics of susceptibility to human infectious disease. Nat Rev Genet 2:967–977
Kimman T (2001) Genetics of infectious disease susceptibility. Springer, Berlin
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 273:1856–1862
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 382:722–725
The Severe Covid-19 GWAS Group (2020) Genome-wide association study of severe Covid-19 with respiratory failure. N Engl J Med 383:1522–1534
Fisher A (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb 53:399–433
Plomin R, Haworth CM, Davis OS (2009) Common disorders are quantitative traits. Nat Rev Genet 10:872–878
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–e73
Real LM, Herrero R, Rivero-Juárez A et al (2015) IFNL4 rs368234815 polymorphism is associated with innate resistance to HIV-1 infection. AIDS 29:1895–1897
Sironi M, Biasin M, Gnudi F et al (2014) A regulatory polymorphism in HAVCR2 modulates susceptibility to HIV-1 infection. PLoS One 9:e106442
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:e1003515
R Core Team (2022) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
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 81:559–575
Sullivan KM, Dean A, Soe MM (2009) OpenEpi: open source epidemiologic statistics for public health. Public Health Rep 124:471–474
Solé X, Guinó E, Valls J et al (2006) SNPStats: a web tool for the analysis of association studies. Bioinformatics 22:1928–1929
Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824
Moser G, Lee SH, Hayes BJ et al (2015) Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet 11:e1004969
Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678
Saha S, Perrin L, Röder L et al (2022) Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests. Nucleic Acids Res 50:e114
Ponte-Fernández C, González-Domínguez J, Carvajal-Rodríguez A, Martin MJ (2022) Evaluation of existing methods for high-order epistasis detection. IEEE/ACM Trans Comput Biol Bioinform 19:912–926
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 138:1338–1345
Ge D, Fellay J, Thompson AJ et al (2009) Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461:399–401
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 27:2707–2714
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:e95515
Prokunina-Olsson L, Muchmore B, Tang W 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 45:164–171
Angulo-Aguado M, Corredor-Orlandelli D, Carrillo-Martínez JC et al (2022) Association between the LZTFL1 rs11385942 polymorphism and COVID-19 severity in Colombian population. Front Med 9:910098
Downes DJ, Cross AR, Hua P et al (2021) Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus. Nat Genet 53:1606–1615
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Laplana, M., Royo, J.L., Real, L.M. (2024). Genetic Association Studies in Host-Pathogen Interaction Analysis. In: Medina, C., López-Baena, F.J. (eds) Host-Pathogen Interactions. Methods in Molecular Biology, vol 2751. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3617-6_2
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DOI: https://doi.org/10.1007/978-1-0716-3617-6_2
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