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. 2005 Nov;171(3):1331-9.
doi: 10.1534/genetics.105.044545. Epub 2005 Aug 5.

Bias and precision in QST estimates: problems and some solutions

R B O'Hara  1 , J Merilä
Affiliations

Bias and precision in QST estimates: problems and some solutions

R B O'Hara et al. Genetics. 2005 Nov.

Abstract

Comparison of population differentiation in neutral marker genes and in genes coding quantitative traits by means of F(ST) and Q(ST) indexes has become commonplace practice. While the properties and estimation of F(ST) have been the subject of much interest, little is known about the precision and possible bias in Q(ST) estimates. Using both simulated and real data, we investigated the precision and bias in Q(ST) estimates and various methods of estimating the precision. We found that precision of Q(ST) estimates for typical data sets (i.e., with <20 populations) was poor. Of the methods for estimating the precision, a simulation method, a parametric bootstrap, and the Bayesian approach returned the most precise estimates of the confidence intervals.

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Figures

F<sc>igure</sc> 1.—
Figure 1.—
Normal probability plots for estimated effects of (a) population, (b) sires, (c) dams, and (d) individuals. If normality is a reasonable assumption, then the points should lie along the straight lines.
F<sc>igure</sc> 2.—
Figure 2.—
Plots of estimated effect sizes: (a) sires plotted against population, (b) dams plotted against population, (c) individuals plotted against population, (d) dams plotted against sire, (e) individuals plotted against sire, and (f) individuals, plotted against dam.
F<sc>igure</sc> 3.—
Figure 3.—
Estimated bias and precision of QST, estimated by REML, for simulated data sets with different values of QST. The boxes show the interquartile range (i.e., from the 25% to the 75% quantile), the horizontal lines in the boxes show the mean, and the whiskers show the 95% confidence intervals. The diagonal line is a 1:1 correspondence between actual and estimated QST.
F<sc>igure</sc> 4.—
Figure 4.—
Estimated bias and precision of QST, estimated by REML, for simulated data sets with different numbers of populations. The boxes show the interquartile range (i.e., from the 25% to the 75% quantile), the lines in the boxes show the mean, and the whiskers show the 95% confidence intervals. The solid lines are a 1:1 correspondence between actual and estimated QST. (a) QST = 0.9; (b) QST = 0.5.
F<sc>igure</sc> 5.—
Figure 5.—
The percentage of times that the nominal 95% interval misses the true QST for different methods of confidence interval estimation. The shaded area shows the 95% confidence region if the actual coverage is correct.
F<sc>igure</sc> 6.—
Figure 6.—
Point estimates, 95% confidence limits, and standard errors (Std Error) for QST from different estimation methods for a real data set.
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References

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