QST (genetics)

In quantitative genetics, Q_ST is a statistic intended to measure the degree of genetic differentiation among populations with regard to a quantitative trait. It was developed by Ken Spitze in 1993. Its name reflects that Q_ST was intended to be analogous to the fixation index for a single genetic locus (F_ST). Q_ST is often compared with F_ST of neutral loci to test if variation in a quantitative trait is a result of divergent selection or genetic drift, an analysis known as Q_ST–F_ST comparisons.

Calculation of Q_ST

Equations

Q_ST represents the proportion of variance among subpopulations, and its calculation is synonymous to F_ST developed by Sewall Wright. However, instead of using genetic differentiation, Q_ST is calculated by finding the variance of a quantitative trait within and among subpopulations, and for the total population. Variance of a quantitative trait among populations (σ²_GB) is described as:

$\sigma_^2 = (1-Q_)\sigma_T^2$

And the variance of a quantitative trait within populations (σ²_GW) is described as:

$\sigma _^2 = 2Q_\sigma_T^2$

Where σ²_T is the total genetic variance in all populations. Therefore, Q_ST can be calculated with the following equation:

$Q_ = \frac$

Assumptions

Calculation of Q_ST is subject to several assumptions: populations must be in Hardy–Weinberg equilibrium, observed variation is assumed to be due to additive genetic effects only, selection and linkage disequilibrium are not present, and the subpopulations exist within an island model.

Q_ST–F_ST comparisons

Q_ST–F_ST analyses often involve culturing organisms in consistent environmental conditions, known as common garden experiments, and comparing the phenotypic variance to genetic variance. If Q_ST is found to exceed F_ST, this is interpreted as evidence of divergent selection, because it indicates more differentiation in the trait than could be produced solely by genetic drift. If Q_ST is less than F_ST, balancing selection is expected to be present. If the values of Q_ST and F_STare equivalent, the observed trait differentiation could be due to genetic drift.

Suitable comparison of Q_ST and F_ST is subject to multiple ecological and evolutionary assumptions, and since the development of Q_ST, multiple studies have examined the limitations and constrictions of Q_ST–F_ST analyses. Leinonen et al. notes F_ST must be calculated with neutral loci, however over filtering of non-neutral loci can artificially reduce F_STvalues. Cubry et al. found Q_ST is reduced in the presence of dominance, resulting in conservative estimates of divergent selection when Q_ST is high, and inconclusive results of balancing selection when Q_ST is low. Additionally, population structure can significantly impact Q_ST–F_ST ratios. Stepping stone models, which can generate more evolutionary noise than island models, are more likely to experience type 1 errors. If a subset of populations act as sources, such as during invasion, weighting the genetic contributions of each population can increase detection of adaptation. In order to improve precision of Q_ST analyses, more populations (>20) should be included in analyses.

Q_ST applications in literature

Multiple studies have incorporated Q_ST to separate effects of natural selection and genetic drift, and Q_ST is often observed to exceed F_ST, indicating local adaptation. In an ecological restoration study, Bower and Aitken used Q_ST to evaluate suitable populations for seed transfer of whitebark pine. They found high Q_ST values in many populations, suggesting local adaptation for cold-adapted characteristics. During an assessment of the invasive species, '' Brachypodium sylvaticum'', Marchini et al. found divergence between native and invasive populations during initial establishment in the invaded range, but minimal divergence during range expansion. In an examination of the common snapdragon (''Antirrhinum majus'') along an elevation gradient, Q_ST–F_ST analyses revealed different adaptation trends between two subspecies (''A. m. pseudomajus'' and ''A. m. striatum''). While both subspecies occur at all elevations, ''A. m. striatum'' had high Q_ST values for traits associated with altitude adaptation: plant height, number of branches, and internode length. ''A. m. pseudomajus'' had lower Q_ST than F_ST values for germination time.

See also

* F-statistics
* Quantitative genetics
* Conservation genetics
* Divergent selection
* Genetic diversity

References

{{Reflist, 30em

Genetics terminology
Population genetics
Statistical tests