Selection and sticklebacks

Over the last decade, there has been increasing circumstantial evidence for the action of natural selection in the genome, arising largely from molecular genetic surveys of large numbers of markers. In nonmodel organisms without densely mapped markers, a frequently used method is to identify loci that have unusually high or low levels of genetic differentiation, or low genetic diversity relative to other populations. The paper by Makinen et al. (2008a) in this issue of Molecular Ecology reports the results of a survey of microsatellite allele frequencies at more than 100 loci in seven populations of the three-spined stickleback (Gasterosteus aculeatus). They show that a microsatellite locus and two indel markers located within the intron of the Eda gene, known to control the number of lateral plates in the stickleback (Fig. 1), tend to be much more highly genetically differentiated than other loci, a finding that is consistent with the action of local selection. They identify a further two independent candidates for local selection, and, most intriguingly, they further suggest that up to 15% of their loci may provide evidence of balancing selection.

Identifying adaptive genetic divergence among populations from genome scans

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of F-ST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of F-ST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of F-ST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.