Disease epidemics can be devastating. How can the spread of infectious disease be controlled? It is believed that more genetically diverse host populations have lower prevalence of infectious diseases. This pattern is particularly strong in agricultural systems where diverse mixtures of crops are less susceptible to epidemics than single species (the “monoculture effect”). But how does host genetic diversity affect disease spread? IU professor Curtis M. Lively uses theoretical modelling as an approach to investigate this question in the March 2016 issue of The American Naturalist.
Infection-genetics models: This study examines two theoretical models of infection genetics, namely the matching alleles model (MAM) and the inverse matching alleles model (IMAM), to ask whether the effect of increasing genetic variation on disease spread can be affected by which model underlies the genetic process of infection. Infection genetics models are broad, theoretical frameworks used to describe the interactions between specific host genotypes and parasite genotypes. Genotype is the genetic make-up of an individual. Alleles are the alternative versions of the same gene. For example, an individual with genotype AB is defined by the presence of allele A at one gene and B at another gene while genotype ab is defined by the presence of different alleles at the same genes, a and b.
As depicted in Figure 1a, under MAM, a successful infection occurs when the parasite genotype matches the host genotype. As a consequence, every parasite genotype can only infect a limited subset of the host genotypes and every host genotype is resistant to most parasite genotypes. This model is conceptually similar to self/non-self recognition systems in the vertebrate immune system which can selectively destroy entities that exhibit non-self antigens.
The IMAM model (Fig 1b) assumes exactly the opposite. A parasite genotype can infect non-matching host genotypes. In other words, every parasite genotype can infect most host genotypes and are resisted by a limited subset of hosts. In both models, no parasite genotype can infect all host genotypes and no host genotype is resistant to all parasite genotypes. Thus, no host/parasite genotype is better than any other. While such strict conditions may not be accurately representative of infection genetics in the real world, whether individual parasite genotypes infect a small subset or a majority of host genotypes constitute relevant patterns.
Why study these theoretical models instead of identifying specific genes? Understanding the genetic basis of infection is a challenging yet fundamentally important task. Looking for every single gene in every host and every parasite across diverse species is a staggering, expensive and extremely time-consuming exercise. Infection-genetics models provide explicit predictions that can be tested more easily by empirical work to understand unifying infection genetics patterns irrespective of specific genes.
Measuring disease spread: The parameter used here to measure disease spread is R0. R0 represents the number of secondary infections, i.e., the number of new hosts to which one infected host can pass on the disease. When R0 > 1, disease will spread because each infected hosts passes it on to more than one new host. Conversely, if R0 < 1, the infection will eventually die out. In either case, higher values of R0 imply faster disease spread and larger epidemics than lower R0. This study examines how the different infection genetics models can affect R0.
Results: Under the Matching alleles model (MAM) where the parasite has to match the host genotype in order to successfully infect, R0 is inversely proportional to the number of host genotypes suggesting that increasing host genetic diversity can reduce R0 and thereby reduce disease spread. In contrast, under the Inverse Matching Alleles Model (IMAM), increasing the number of host genotypes results in increased R0.
Significance: These results demonstrate how important it may be to understand the underlying infection genetics for developing strategies to control disease spread. Increasing genetic variation might only work in systems that follow a matching-alleles type infection process. In addition, this work is an early attempt to incorporate epidemiology with evolutionary theory. Pure epidemiology has focused on host density, host-parasite contact rate and similar factors to assess risk of disease whereas disease evolution and ecology focus on changes in host and parasite genotypes across generations.
Overall, this study provides a very important insight into the significance of the underlying infection genetics on the rate of disease spread. While the limited, existing empirical evidence is consistent with a matching-alleles framework, further empirical work is needed to understand the underlying genetics of infection and devise strategies to minimise disease spread among populations.