Comparison of genetic differentiation at marker loci and quantitative traits


Juha Merilä, Section of Population Biology, Department of Ecology and Systematics, PL 17, FIN-00014, University of Helsinki, Finland. Tel.: +358 9 40 8374165; fax: +358 9 191 28701; e-mail:


The comparison of the degree of differentiation in neutral marker loci and genes coding quantitative traits with standardized and equivalent measures of genetic differentiation (FST and QST, respectively) can provide insights into two important but seldom explored questions in evolutionary genetics: (i) what is the relative importance of random genetic drift and directional natural selection as causes of population differentiation in quantitative traits, and (ii) does the degree of divergence in neutral marker loci predict the degree of divergence in genes coding quantitative traits? Examination of data from 18 independent studies of plants and animals using both standard statistical and meta-analytical methods revealed a number of interesting points. First, the degree of differentiation in quantitative traits (QST) typically exceeds that observed in neutral marker genes (FST), suggesting a prominent role for natural selection in accounting for patterns of quantitative trait differentiation among contemporary populations. Second, the FST – QST difference is more pronounced for allozyme markers and morphological traits, than for other kinds of molecular markers and life-history traits. Third, very few studies reveal situations were QST < FST, suggesting that selection pressures, and hence optimal phenotypes, in different populations of the same species are unlikely to be often similar. Fourth, there is a strong correlation between QST and FST indices across the different studies for allozyme (r=0.81), microsatellite (r=0.87) and combined (r=0.75) marker data, suggesting that the degree of genetic differentiation in neutral marker loci is closely predictive of the degree of differentiation in loci coding quantitative traits. However, these interpretations are subject to a number of assumptions about the data and methods used to derive the estimates of population differentiation in the two sets of traits.