The relative importance of natural selection vs. genetic drift in explaining evolutionary changes both at molecular (e.g. Kimura, 1983; Mitton, 1994; Ohta, 2002) and phenotypic (e.g. Lande, 1976; Lynch, 1994; Whitlock & Phillips, 2000; O’Hara, 2005) levels constitute a long-standing source of debate in evolutionary biology. Although it is clear that both processes occur and most of the phenotypic evolution is likely to be driven by natural selection, there are also ample opportunities for genetic drift to be an important component of population differentiation. This is true especially in populations with small effective sizes (Ne). In other words, small Ne increases the rate of genetic drift providing more scope for nonadaptive differentiation, and the efficiency of natural selection is inversely related to Ne (e.g. Jones et al., 1968; Frankham et al., 2002; England et al., 2003). Therefore, small populations can respond to selection more slowly than large populations (Robertson, 1960; Frankham & Weber, 2000). Similarly, although there is ample evidence for rapid evolutionary differentiation apparently driven by selection (Hendry & Kinnison, 1999; Kinnison & Hendry, 2001), evolutionary rates are also often so low – especially over longer time scales (Gingerich, 2001; Kinnison & Hendry, 2001) – that in many cases they could, at least in principle, have been achieved with very little selection or even by genetic drift alone (e.g. Lande, 1976; Lynch, 1988; Kinnison & Hendry, 2001; but see Estes & Arnold, 2007).
Today, as our understanding of the genetic basis of quantitative trait variability and differentiation is increasing at an accelerating pace (e.g. Mackay, 2001, 2004;Anholt & MacKay, 2004; Colosimo et al., 2005; Chenoweth & Blows, 2006; Hoekstra et al., 2006), new insights into many of the long-standing controversies about the relative roles of selection and drift in evolution can be expected. Although there are already a few examples where enough is known both about the genetic details and selective factors to firmly infer selective cause for population differentiation in particular genes (e.g. Colosimo et al., 2005; Hoekstra et al., 2006), it will probably take a good while before these technologies and possibilities become widely applicable for nonmodel organisms and more complex traits (e.g. Chenoweth & Blows, 2006). Therefore, comparisons of quantitative genetic (as measured by the QST) and neutral marker gene (as measured by FST) differentiations are still providing one of the most accessible and universal tools for inferring the role of natural selection in population differentiation for quantitative traits (see Merilä & Crnokrak, 2001; McKay & Latta, 2002 for reviews).
The logic of FST and QST comparisons is based on the realization that for loci subject to the effects of genetic drift and migration only (assuming a negligible contribution from mutation), FST for neutral markers provides a null expectation for the degree of population differentiation attainable without selection (e.g. Merilä & Crnokrak, 2001). When comparing FST with the analogous index for quantitative traits (QST), three outcomes are possible (Merilä & Crnokrak, 2001). First, if QST > FST, then this implies that the degree of differentiation in quantitative traits exceeds that attainable by genetic drift alone, and directional natural selection favouring different phenotypes in different populations is the likely cause of this differentiation. Secondly, if the QST and FST estimates are almost equal, the observed degree of differentiation in quantitative traits could have been reached by genetic drift alone. However, this does not prove that the observed degree of differentiation was caused by genetic drift – only that the relative contributions of drift and selection are unknown. Third, if QST < FST, this implies that the observed degree of differentiation is actually less than expected on the basis of genetic drift alone, the most likely cause for this being stabilizing selection.
Comparative studies of differentiation in quantitative traits and in presumed neutral marker genes have become increasingly popular since the last comprehensive review and meta-analysis was published about 5 years ago (Merilä & Crnokrak, 2001; see also McKay & Latta, 2002). The number of studies providing comparative data on both QST and FST from the same populations has more than doubled since then and interest seems to continue, as reflected in the steady increase in publication number in this topic (Fig. 1).
Although the foundation for this approach to compare marker genes and quantitative traits was laid down by Sewall Wright as early as in 1951 (Wright, 1951), it took about 30 years before it was put in practical use by Rogers & Harpending (1983; Fig. 1). Soon after, Lewontin (1984) published a commentary that provided the earliest criticism directed against comparisons of marker and quantitative genetic measures of population differentiation. In his criticism, Lewontin (1984) was concerned about the actual information content of marker vs. quantitative trait comparisons and concluded that such comparisons would serve little useful purpose. Lewontin’s (1984) article was followed by Felsenstein’s (1986) commentary and Rogers’ (1986) response concluding that Lewontin’s (1984) criticism was not entirely valid – a point that Lewontin (1986) later commented on. After this rather dramatic dawn of comparative studies of marker genes and quantitative traits, it took about another 10 years before the next empirical contribution to field was made by Prout & Barker (1993).
The work by Prout & Barker (1993) was followed by Spitze’s (1993) study, which also introduced and established the ‘QST’ notation to the terminology of comparative studies of quantitative trait and neutral marker differentiation. Several other studies followed, but the number of publications in this topic remained low until about the year 2000, after which the interest in QST–FST comparisons experienced a noticeable increase (Fig. 1). The markedly delayed surge of these studies – relative to Wright’s (1951) original paper – is understandable as access to polymorphic markers, as well as interest in evolutionary quantitative genetic studies were both quite limited until the 1980s. Hence, most empirical studies on QST–FST comparisons have been published in the 21st century (Fig. 1).
Theoretical studies focussing on the assumptions and properties of QST have been rather slow to appear (Fig. 1), starting with Whitlock’s (1999) simulations of effects of nonadditive genetic effects on the difference between FST and QST. This work has been recently extended by several authors (Le Corre & Kremer, 2003;Lopez-Fanjul et al., 2003;Goudet & Buchi, 2006), the main practical conclusion being that nonadditive genetic effects are unlikely to bias QST estimates considerably. An important concern in comparative studies of quantitative trait and marker gene differentiation is also the distinction between QST for a quantitative trait and individual quantitative trait loci (Latta, 1998; McKay & Latta, 2002). As it turns out, one should not expect to see the signature of selection (QST > FST) in individual loci in the same way as for traditional estimates of QST based on quantitative traits (Latta, 1998).
During the past 5 years or so, a number of practical and conceptual problems with comparative studies of quantitative trait and marker differentiation have emerged (e.g. Hendry, 2002; O’Hara & Merilä, 2005). One of these concerns the difficulty of obtaining accurate estimates of QST with a small number of populations, as well as the related and more practical problem of correctly estimating its standard errors (O’Hara & Merilä, 2005). Another important concern with the comparative studies is the problem with the effect of mutation rates on the difference between FST and QST (Hendry, 2002). For instance, if the mutation rates for neutral marker traits are larger than those for quantitative traits, then the comparisons might be predisposed to find QST > FST. However, it is yet unclear whether mutations in quantitative traits will affect QST in an analogous fashion or how this influences QST–FST comparisons.
Some controversy has also arisen around the issue of whether molecular and quantitative genetic estimates of population differentiation are correlated or not (Merilä & Crnokrak, 2001; Crnokrak & Merilä, 2002; Latta & McKay, 2002; McKay & Latta, 2002). This controversy was mainly due to the fact that two reviews (Merilä & Crnokrak, 2001; McKay & Latta, 2002) used different criteria for selecting data, and there were some minor errors in numerical values of the estimates in one of the data sets (Crnokrak & Merilä, 2002). Irrespective of these issues, the amount of data in both of the reviews was so low that no strong conclusions about the existence relationship – or lack of it thereof – were possible.
The main aim of this review was to provide an update of the earlier meta-analysis of comparative studies of QST and FST estimates, with about 100% more data. In particular, we are interested to see whether the earlier main findings, that is overwhelming evidence for selective nature of quantitative trait differentiation (QST > FST) and a weak correlation between QST and FST across different studies – still hold with a larger data set. At the same time, we extended the meta-analyses to the individual trait level (as opposed to averages over traits) while also accounting for nonindependence of individual QST and FST estimates. We accomplished this by extending the meta-analysis to a hierarchical modelling framework. Our analytical approach also allowed us to evaluate the relative influence of marker type, trait type, population number and other factors related to study design on the outcome of QST–FST comparisons. In addition to the results of meta-analyses, we evaluate and discuss the results in the light of some potentially critical assumptions and problems in QST and FST estimation. While doing so, we will focus on ideas and findings that have emerged since the previous meta-analysis (Merilä & Crnokrak, 2001) to avoid extensive repetition of basic assumptions, background and potential pitfalls.