Candidate genes versus genome-wide associations: which are better for detecting genetic susceptibility to infectious disease?

Abstract

Technological developments allow increasing numbers of markers to be deployed in case-control studies searching for genetic factors that influence disease susceptibility. However, with vast numbers of markers, true 'hits' may become lost in a sea of false positives. This problem may be particularly acute for infectious diseases, where the control group may contain unexposed individuals with susceptible genotypes. To explore this effect, we used a series of stochastic simulations to model a scenario based loosely on bovine tuberculosis. We find that a candidate gene approach tends to have greater statistical power than studies that use large numbers of single nucleotide polymorphisms (SNPs) in genome-wide association tests, almost regardless of the number of SNPs deployed. Both approaches struggle to detect genetic effects when these are either weak or if an appreciable proportion of individuals are unexposed to the disease when modest sample sizes (250 each of cases and controls) are used, but these issues are largely mitigated if sample sizes can be increased to 2000 or more of each class. We conclude that the power of any genotype-phenotype association test will be improved if the sampling strategy takes account of exposure heterogeneity, though this is not necessarily easy to do.

Figure 1.

How the probability of finding a significant genotype–phenotype association for an infectious disease varies with marker type, exposure rate, recombination rate and size of genetic effect. Each cell contains the proportion of approximately 100 replicate simulations that yielded an experiment-wide significant association at α = 5%. Greyscale represents: white = 0–5% significant; light grey = 5–50% significant; dark grey = 50–90% significant; black = 90–100% significant. Exposure is the proportion of individuals exposed to the disease, genetic effect is the probability of an exposed, genetically resistant individual catching the disease relative to an exposed, susceptible individual (p = 1). (a) ‘SNPs’ (=marker carries three genotypes), recombination rate = 10⁻³; (b) ‘SNPs’, recombination rate = 10⁻⁵; (c) ‘microsatellites’ (=marker carries 10+ genotypes), recombination rate = 10⁻³; (d) ‘microsatellites’, recombination rate = 10⁻⁵.

Figure 2.

How the probability of finding a significant genotype–phenotype association for an infectious disease varies with exposure rate and size of genetic effect. (a) Results for a panel of 50 000 SNPs that includes the functional mutation. (b) Results for a CG approach using a sample size of 4000 (equal numbers of cases and controls). For details of simulations and greyscale, see legend of figure 1.

Figure 3.

Effectiveness of a genome-wide association (GWA) study to reveal a genotype–phenotype association with sample sizes of 500 and 4000. In each set of simulations, we assume a large, SNP-based study deploying either 50 000 or two million markers. The probability of finding an association is calculated by combining the probability of one marker lying close enough to the functional gene for the recombination rate to be 10⁻⁵ with the probability that the functional mutation itself is included in the panel. For other details, see legend of figure 1. (a) 50 000 SNPs, sample size = 500 (250 case plus 250 controls); (b) 50 000 SNPs, sample size = 4000; (c) two million SNPs, sample size = 500; (d) two million SNPs, sample size = 4000.