Publisher
BMC Springer Nature
Abstract
Background: The spread of infectious diseases in populations is controlled by the susceptibility (propensity to
acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals.
Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease
spread through genetic control strategies. Previous studies have identified important 'disease resistance single nucleotide
polymorphisms (SNPs)', but how these affect the underlying traits is an unresolved question. Recent advances in
computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g.
infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively
design disease transmission experiments or field studies to maximise the precision with which these effects can
be estimated.
Results: In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP
effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact
groups. Maximising these expressions leads to three distinct 'experimental' designs, each specifying a different set of
ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design
termed "pure", and (c) a multi-group design termed "mixed", where 'pure' and 'mixed' refer to groupings that consist of
individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and
recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the
analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision,
the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections.
The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects,
such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental
setup is implemented in an online software tool SIRE-PC.
Conclusions: Methodology was developed to aid the design of disease transmission experiments for estimating the
effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability.
Designs that maximize the precision of estimates were derived.
Year
2022
Category
Refereed journal