Discrepancies in the estimation of gene flow in partula

Methods for estimating gene flow (Nm) from genetic data should provide important insights into the dynamics of natural populations. If they are to be used with confidence, however, the methods must be shown to produce valid results. Estimates of Nm have been obtained for the snails Partula taeniata and Partula suturalis, based on F(ST) and on the frequencies of private alleles, p(1). Jackknifing was used to reduce the bias of estimates and to obtain confidence limits. The estimates derived from F(ST) are consistent with the low vagility of snails, and with direct field studies of gene flow in P. taeniata. In contrast, the estimates derived from p(1) were up to seven times as large, less precise and less consistent. Although the underlying causes of these discrepancies are not clear, the results suggest that F(ST) is the more reliable indirect estimator of gene flow, at least for Partula.     

Comparisons between QST and FST—how wrong have we been?

The comparison between quantitative genetic divergence (Q(ST) ) and neutral genetic divergence (F(ST) ) among populations has become the standard test for historical signatures of selection on quantitative traits. However, when the mutation rate of neutral markers is relatively high in comparison with gene flow, estimates of F(ST) will decrease, resulting in upwardly biased comparisons of Q(ST) vs. F(ST) . Reviewing empirical studies, the difference between Q(ST) and F(ST) is positively related to marker heterozygosity. After refuting alternative explanations for this pattern, we conclude that marker mutation rate indeed has had a biasing effect on published Q(ST) -F(ST) comparisons. Hence, it is no longer clear that populations have commonly diverged in response to divergent selection. We present and discuss potential solutions to this bias. Comparing Q(ST) with recent indices of neutral divergence that statistically correct for marker heterozygosity (Hedrick's G'st and Jost's D) is not advised, because these indices are not theoretically equivalent to Q(ST) . One valid solution is to estimate F(ST) from neutral markers with mutation rates comparable to those of the loci underlying quantitative traits (e.g. SNPs). Q(ST) can also be compared to Φ(ST) (Phi(ST) ) of amova, as long as the genetic distance among allelic variants used to estimate Φ(ST) reflects evolutionary history: in that case, neutral divergence is independent of mutation rate. In contrast to their common usage in comparisons of Q(ST) and F(ST) , microsatellites typically have high mutation rates and do not evolve according to a simple evolutionary model, so are best avoided in Q(ST) -F(ST) comparisons.     

Comparing Bayesian estimates of genetic differentiation of molecular markers and quantitative traits: an application to Pinus sylvestris

Comparison of the level of differentiation at neutral molecular markers (estimated as F(ST) or G(ST)) with the level of differentiation at quantitative traits (estimated as Q(ST)) has become a standard tool for inferring that there is differential selection between populations. We estimated Q(ST) of timing of bud set from a latitudinal cline of Pinus sylvestris with a Bayesian hierarchical variance component method utilizing the information on the pre-estimated population structure from neutral molecular markers. Unfortunately, the between-family variances differed substantially between populations that resulted in a bimodal posterior of Q(ST) that could not be compared in any sensible way with the unimodal posterior of the microsatellite F(ST). In order to avoid publishing studies with flawed Q(ST) estimates, we recommend that future studies should present heritability estimates for each trait and population. Moreover, to detect variance heterogeneity in frequentist methods (ANOVA and REML), it is of essential importance to check also that the residuals are normally distributed and do not follow any systematically deviating trends.     

Global analysis of regional differences in craniometric diversity and population substructure.

Estimates of genetic diversity in major geographic regions are frequently made by pooling all individuals into regional aggregates. This method can potentially bias results if there are differences in population substructure within regions, since increased variation among local populations could inflate regional diversity. A preferred method of estimating regional diversity is to compute the mean diversity within local populations. Both methods are applied to a global sample of craniometric data consisting of 57 measurements taken on 1734 crania from 18 local populations in six geographic regions: sub-Saharan Africa, Europe, East Asia, Australasia, Polynesia, and the Americas. Each region is represented by three local populations. Both methods for estimating regional diversity show sub-Saharan Africa to have the highest levels of phenotypic variation, consistent with many genetic studies. Polynesia and the Americas both show high levels of regional diversity when regional aggregates are used, but the lowest mean local population diversity. Regional estimates of F(ST) made using quantitative genetic methods show that both Polynesia and the Americas also have the highest levels of differentiation among local populations, which inflates regional diversity. Regional differences in F(ST) are directly related to the geographic dispersion of samples within each region; higher F(ST) values occur when the local populations are geographically dispersed. These results show that geographic sampling can affect results, and suggest caution in making inferences regarding regional diversity when population substructure is ignored.     

The effects of dominance, regular inbreeding and sampling design on Q(ST), an estimator of population differentiation for quantitative traits

To test whether quantitative traits are under directional or homogenizing selection, it is common practice to compare population differentiation estimates at molecular markers (F(ST)) and quantitative traits (Q(ST)). If the trait is neutral and its determinism is additive, then theory predicts that Q(ST) = F(ST), while Q(ST) > F(ST) is predicted under directional selection for different local optima, and Q(ST) < F(ST) is predicted under homogenizing selection. However, nonadditive effects can alter these predictions. Here, we investigate the influence of dominance on the relation between Q(ST) and F(ST) for neutral traits. Using analytical results and computer simulations, we show that dominance generally deflates Q(ST) relative to F(ST). Under inbreeding, the effect of dominance vanishes, and we show that for selfing species, a better estimate of Q(ST) is obtained from selfed families than from half-sib families. We also compare several sampling designs and find that it is always best to sample many populations (>20) with few families (five) rather than few populations with many families. Provided that estimates of Q(ST) are derived from individuals originating from many populations, we conclude that the pattern Q(ST) > F(ST), and hence the inference of directional selection for different local optima, is robust to the effect of nonadditive gene actions.     

The effect of neutral nonadditive gene action on the quantitative index of population divergence

For neutral additive genes, the quantitative index of population divergence (Q(ST)) is equivalent to Wright's fixation index (F(ST)). Thus, divergent or convergent selection is usually invoked, respectively, as a cause of the observed increase (Q(ST) > F(ST)) or decrease (Q(ST) < F(ST)) of Q(ST) from its neutral expectation (Q(ST) = F(ST)). However, neutral nonadditive gene action can mimic the additive expectations under selection. We have studied theoretically the effect of consecutive population bottlenecks on the difference F(ST) - Q(ST) for two neutral biallelic epistatic loci, covering all types of marginal gene action. With simple dominance, Q(ST) < F(ST) for only low to moderate frequencies of the recessive alleles; otherwise, Q(ST) > F(ST). Additional epistasis extends the condition Q(ST) < F(ST) to a broader range of frequencies. Irrespective of the type of nonadditive action, Q(ST) < F(ST) generally implies an increase of both the within-line additive variance after bottlenecks over its ancestral value (V(A)) and the between-line variance over its additive expectation (2F(ST)V(A)). Thus, both the redistribution of the genetic variance after bottlenecks and the F(ST) - Q(ST) value are governed largely by the marginal properties of single loci. The results indicate that the use of the F(ST) - Q(ST) criterion to investigate the relative importance of drift and selection in population differentiation should be restricted to pure additive traits.     

Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce

Outlier detection methods were used to scan the genome of the boreal conifer black spruce (Picea mariana [Mill.] B.S.P.) for gene single-nucleotide polymorphisms (SNPs) potentially involved in adaptations to temperature and precipitation variations. The scan involved 583 SNPs from 313 genes potentially playing adaptive roles. Differentiation estimates among population groups defined following variation in temperature and precipitation were moderately high for adaptive quantitative characters such as the timing of budset or tree height (Q(ST) = 0.189-0.314). Average differentiation estimates for gene SNPs were null, with F(ST) values of 0.005 and 0.006, respectively, among temperature and precipitation population groups. Using two detection approaches, a total of 26 SNPs from 25 genes distributed among 11 of the 12 linkage groups of black spruce were detected as outliers with F(ST) as high as 0.078. Nearly half of the outlier SNPs were located in exons and half of those were nonsynonymous. The functional annotations of genes carrying outlier SNPs and regression analyses between the frequencies of these SNPs and climatic variables supported their implication in adaptive processes. Several genes carrying outlier SNPs belonged to gene families previously found to harbour outlier SNPs in a reproductively isolated but largely sympatric congeneric species, suggesting differential subfunctionalization of gene duplicates. Selection coefficient estimates (S) were moderate but well above the magnitude of drift (>1/N(e)), indicating that the signature of natural selection could be detected at the nucleotide level despite the recent establishment of these populations during the Holocene.     

Experimental demonstration of a causal relationship between heterogeneity of selection and genetic differentiation in quantitative traits

Comparisons of estimates of genetic differentiation at molecular markers (F(ST)) and at quantitative traits (Q(ST)) are a means of inferring the level and heterogeneity of selection in natural populations. However, such comparisons are questionable because they require that the influence of drift and selection on Q(ST) be detectable over possible background influences of environmental or nonadditive genetic effects on Q(ST)-values. Here we test this using an experimental evolution approach in metapopulations of Arabidopsis thaliana experiencing different levels of drift and selection heterogeneity. We estimated the intensity and heterogeneity of selection on morphological and phenological traits via selection differentials. We demonstrate that Q(ST)-values increased with increasing selection heterogeneity when genetic drift was limited. The effect of selection on Q(ST) was thus detectable despite significant genotype-by-environment interactions that most probably biased the estimates of genetic differentiation. Although they cannot be used as a direct validation of the conclusions of prior studies, our results strongly support both the relevance of Q(ST) as an estimator of genetic differentiation and the role of local selection in shaping the genetic differentiation of natural populations.     

Evolutionary inference from QST

Q(ST) is a commonly used metric of the degree of genetic differentiation among populations displayed by quantitative traits. Typically, Q(ST) is compared to F(ST) measured on putatively neutral loci; if Q(ST)=F(ST), this is taken as evidence of spatially heterogeneous and diversifying selection. This paper reviews the uses, assumptions and statistics of Q(ST) and F(ST) comparisons. Unfortunately, Q(ST)/F(ST) comparisons are statistically challenging. For a single trait, Q(ST) must be compared not to the mean F(ST) but to the distribution of F(ST) values. The sources of biases and sampling error for Q(ST) are reviewed, and a new method for comparing Q(ST) and F(ST) is suggested. Simulation results suggest that the distribution of neutral F(ST) and Q(ST) values are little affected by various deviations from the island model. Consequently, the distributions of Q(ST) and F(ST) are well approximated by the Lewontin-Krakauer prediction, even with realistic deviations from the island-model assumptions.     

Methodology matters when estimating deer abundance: a global systematic review and recommendations for improvements

Deer (Cervidae) are key components of many ecosystems and estimating deer abundance or density is important to understanding these roles. Many field methods have been used to estimate deer abundance and density, but the factors determining where, when, and why a method was used, and its usefulness, have not been investigated. We systematically reviewed journal articles published during 2004–2018 to evaluate spatio-temporal trends in study objectives, methodologies, and deer abundance and density estimates, and determine how they varied with biophysical and anthropogenic attributes. We also reviewed the precision and bias of deer abundance estimation methods. We found 3,870 deer abundance and density estimates. Most estimates (58%) were for white-tailed deer (Odocoileus virginianus), red deer (Cervus elaphus), and roe deer (Capreolus capreolus). The 6 key methods used to estimate abundance and density were pedestrian sign (track or fecal) counts, pedestrian direct counts, vehicular direct counts, aerial direct counts, motion-sensitive cameras, and harvest data. There were regional differences in the use of these methods, but a general pattern was a temporal shift from using harvest data, pedestrian direct counts, and aerial direct counts to using pedestrian sign counts and motion-sensitive cameras. Only 32% of estimates were accompanied by a measure of precision. The most precise estimates were from vehicular spotlight counts and from capture–recapture analysis of images from motion-sensitive cameras. For aerial direct counts, capture–recapture methods provided the most precise estimates. Bias was robustly assessed in only 16 studies. Most abundance estimates were negatively biased, but capture–recapture methods were the least biased. The usefulness of deer abundance and density estimates would be substantially improved by 1) reporting key methodological details, 2) robustly assessing bias, 3) reporting the precision of estimates, 4) using methods that increase and estimate detection probability, and 5) staying up to date on new methods. The automation of image analysis using machine learning should increase the accuracy and precision of abundance estimates from direct aerial counts (visible and thermal infrared, including from unmanned aerial vehicles [drones]) and motion-sensitive cameras, and substantially reduce the time and cost burdens of manual image analysis.     

A simulation study of three methods for estimating selective values and survival rates from recapture data

The methods ofManly (1973),Manly (1975) andManly (1977) for estimating survival rates and relative survival rates from recapture data have been compared by computer simulation. In the simulations batches of two types of animal were “released” at one point in “time” and recapture samples were taken at “daily” intervals from then on. The various methods of estimation were then used to estimate, the daily survival rates of type 1 and type 2 animals, and also the survival rate of the type 2 animals relative to the type 1 animals. Simulation experiments were designed to examine (a) the bias in estimates, (b) the relative precision of different methods of estimation, (c) the validity of confidence intervals for true parameter values, and (d) the effect on estimates of the failure of certain assumptions.     

Comparative studies of quantitative trait and neutral marker divergence: a meta-analysis

Comparative studies of quantitative genetic and neutral marker differentiation have provided means for assessing the relative roles of natural selection and random genetic drift in explaining among-population divergence. This information can be useful for our fundamental understanding of population differentiation, as well as for identifying management units in conservation biology. Here, we provide comprehensive review and meta-analysis of the empirical studies that have compared quantitative genetic (Q(ST)) and neutral marker (F(ST)) differentiation among natural populations. Our analyses confirm the conclusion from previous reviews - based on ca. 100% more data - that the Q(ST) values are on average higher than F(ST) values [mean difference 0.12 (SD 0.27)] suggesting a predominant role for natural selection as a cause of differentiation in quantitative traits. However, although the influence of trait (life history, morphological and behavioural) and marker type (e.g. microsatellites and allozymes) on the variance of the difference between Q(ST) and F(ST) is small, there is much heterogeneity in the data attributable to variation between specific studies and traits. The latter is understandable as there is no reason to expect that natural selection would be acting in similar fashion on all populations and traits (except for fitness itself). We also found evidence to suggest that Q(ST) and F(ST) values across studies are positively correlated, but the significance of this finding remains unclear. We discuss these results in the context of utility of the Q(ST)-F(ST) comparisons as a tool for inferring natural selection, as well as associated methodological and interpretational problems involved with individual and meta-analytic studies.     

A Comparison of Isozyme and Quantitative Genetic Variation in Pinus Contorta Ssp. Latifolia by F(st)

We employed F-statistics to analyze quantitative and isozyme variation among five populations of Pinus contorta ssp. latifolia, a wind-pollinated outcrossing conifer with wide and continuous distribution in west North America. Estimates of population differentiation (F(ST)) for six quantitative traits were compared with the overall estimate of the differentiation (F*(ST)) from 19 isozymes that tested neutral to examine whether similar evolutionary processes were involved in morphological and isozyme differentiation. While the F(ST) estimates for specific gravity, stem diameter, stem height and branch length were significantly greater than the F*(ST) estimate, as judged from the 95% confidence intervals by bootstrapping, the F(ST) estimates for branch angle and branch diameter were indistinguishable from the F*(ST) estimate. Differentiation in stem height and stem diameter might reflect the inherent adaptation of the populations for rapid growth to escape suppression by neighboring plants during establishment and to regional differences in photoperiod, precipitation and temperature. In contrast, divergences in wood specific gravity and branch length might be correlated responses to population differentiation in stem growth. Possible bias in the estimation of F(ST) due to Hardy-Weinberg disequilibrium (F(IS) & 0), linkage disequilibrium, maternal effects and nonadditive genetic effects was discussed with special reference to P. contorta ssp. latifolia.     

Molecular and quantitative genetic divergence among populations of house mice with known evolutionary histories

Evolutionary biologists have long been interested in the processes influencing population differentiation, but separating the effects of neutral and adaptive evolution has been an obstacle for studies of population subdivision. A recently developed method allows tests of whether disruptive (ie, spatially variable) or stabilizing (ie, spatially uniform) selection is influencing phenotypic differentiation among subpopulations. This method, referred to as the F(ST) vs Q(ST) comparison, separates the total additive genetic variance into within- and among-population components and evaluates this level of differentiation against a neutral hypothesis. Thus, levels of neutral molecular (F(ST)) and quantitative genetic (Q(ST)) divergence are compared to evaluate the effects of selection and genetic drift on phenotypic differentiation. Although the utility of such comparisons appears great, its accuracy has not yet been evaluated in populations with known evolutionary histories. In this study, F(ST) vs Q(ST) comparisons were evaluated using laboratory populations of house mice with known evolutionary histories. In this model system, the F(ST) vs Q(ST) comparisons between the selection groups should reveal quantitative trait differentiation consistent with disruptive selection, while the F(ST) vs Q(ST) comparisons among lines within the selection groups should suggest quantitative trait differentiation in agreement with drift. We find that F(ST) vs Q(ST) comparisons generally produce the correct evolutionary inference at each level in the population hierarchy. Additionally, we demonstrate that when strong selection is applied between populations Q(ST) increases relative to Q(ST) among populations diverging by drift. Finally, we show that the statistical properties of Q(ST), a variance component ratio, need further investigation.     

Dynamically heterogenous partitions and phylogenetic inference: an evaluation of analytical strategies with cytochrome b and ND6 gene sequences in cranes

ki ctes over whether molecular sequence data should be partitioned for phylogenetic analysis often confound two types of heterogeneity among partitions. We distinguish historical heterogeneity (i.e., different partitions have different evolutionary relationships) from dynamic heterogeneity (i.e., different partitions show different patterns of sequence evolution) and explore the impact of the latter on phylogenetic accuracy and precision with a two-gene, mitochondrial data set for cranes. The well-established phylogeny of cranes allows us to contrast tree-based estimates of relevant parameter values with estimates based on pairwise comparisons and to ascertain the effects of incorporating different amounts of process information into phylogenetic estimates. We show that codon positions in the cytochrome b and NADH dehydrogenase subunit 6 genes are dynamically heterogenous under both Poisson and invariable-sites + gamma-rates versions of the F84 model and that heterogeneity includes variation in base composition and transition bias as well as substitution rate. Estimates of transition-bias and relative-rate parameters from pairwise sequence comparisons were comparable to those obtained as tree-based maximum likelihood estimates. Neither rate-category nor mixed-model partitioning strategies resulted in a loss of phylogenetic precision relative to unpartitioned analyses. We suggest that weighted-average distances provide a computationally feasible alternative to direct maximum likelihood estimates of phylogeny for mixed-model analyses of large, dynamically heterogenous data sets.     

If FST does not measure neutral genetic differentiation,then comparing it with QST is misleading. Or is it?

The comparison between neutral genetic differentiation (F(ST) ) and quantitative genetic differentiation (Q(ST) ) is commonly used to test for signatures of selection in population divergence. However, there is an ongoing discussion about what F(ST) actually measures, even resulting in some alternative metrics to express neutral genetic differentiation. If there is a problem with F(ST) , this could have repercussions for its comparison with Q(ST) as well. We show that as the mutation rate of the neutral marker increases, F(ST) decreases: a higher within-population heterozygosity (He) yields a lower F(ST) value. However, the same is true for Q(ST) : a higher mutation rate for the underlying QTL also results in a lower Q(ST) estimate. The effect of mutation rate is equivalent in Q(ST) and F(ST) . Hence, the comparison between Q(ST) and F(ST) remains valid, if one uses neutral markers whose mutation rates are not too high compared to those of quantitative traits. Usage of highly variable neutral markers such as hypervariable microsatellites can lead to serious biases and the incorrect inference that divergent selection has acted on populations. Much of the discussion on F(ST) seems to stem from the misunderstanding that it measures the differentiation of populations, whereas it actually measures the fixation of alleles. In their capacity as measures of population differentiation, Hedrick's G'(ST) and Jost's D reach their maximum value of 1 when populations do not share alleles even when there remains variation within populations, which invalidates them for comparisons with Q(ST) .     

Microsatellite null alleles and estimation of population differentiation

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on F(ST) and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating F(ST) and genetic distances, and we compared this method with a new method proposed here (for F(ST) only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, F(ST) and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of F(ST) and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. F(ST) estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results.     

Addressing Temporal Variability in Bird Calling with Design and Estimation: A Northern Bobwhite Example

Imprecise or biased density estimates can lead to inadequate conservation action, overexploitation of game species, or lost recreational opportunities. Common approaches to estimating density of avian populations often either ignore the probability that an individual is present within the sampling area but is not available to be sampled (e.g., not vocalizing), or do not consider covariates that could influence availability. Additionally, management decisions made at the management unit scale are often informed by inadequate monitoring practices, such as limited sampling intensity. In such cases, management agencies calculate density by applying correction factors (e.g., detection probabilities estimated using empirical data from a different study system) to count data, rather than estimating a detection function directly using statistical models. We conducted a simulation study using northern bobwhite (Colinus virginianus; bobwhite) as a model species to quantify the consequences of mis-specifying avian point count models on bias and precision of density estimates. We compared bias and precision of estimates from a fully specified distance-sampling model that estimates availability and detection to 4 different mis-specified approaches, including 2 approaches to calculating density using correction factors. Using correction factors to calculate density produced estimates with low bias but relatively lower precision compared to the fully specified model (CV of density estimates at 35 sites over 5 years: fully specified = 10%, correction factors = 25% and 30%). Although the mean precision and bias of the fully specified model improved with more data (70 sites over 5 years, CV = 9%; 35 sites over 10 years, CV = 9%), precision of correction factors did not (70 sites over 5 years, CV = 22% and 27%; 35 sites over 10 years, CV = 24% and 29%). The fully specified model captured the underlying temporal variation in detection and availability. Increasing sampling duration from 5 to 10 years improved modeled estimates of growth rate, even for mis-specified models, but not derived growth rates using pre-determined detection functions. We demonstrated that conducting point counts 3 times/year at a feasible number of sites can produce relatively unbiased estimates of bobwhite density. Pre-determined detection functions can be fortuitously unbiased for certain years, but they are not a reliable method for determining density or identifying trends in density over time. © 2020 The Wildlife Society.     

Nei's to Bayes': comparing computational methods and genetic markers to estimate patterns of genetic variation in Tolpis (Asteraceae)

Accurate determination of patterns of genetic variation provides a powerful inferential tool for studies of evolution and conservation. For more than 30 years, enzyme electrophoresis was the preferred method for elucidating these patterns. As a result, evolutionary geneticists have acquired considerable understanding of the relationship between patterns of allozyme variation and aspects of evolutionary process. Myriad molecular markers and statistical analyses have since emerged, enabling improved estimates of patterns of genetic diversity. With these advances, there is a need to evaluate results obtained with different markers and analytical methods. We present a comparative study of gene statistic estimates (F(ST), G(ST), F(IS), H(S), and H(T)) calculated from an intersimple sequence repeat (ISSR) and an allozyme data set derived from the same populations using both standard and Bayesian statistical approaches. Significant differences were found between estimates, owing to the effects of marker and analysis type. Most notably, F(ST) estimates for codominant data differ between Bayesian and standard approaches. Levels of statistical significance are greatly affected by methodology and, in some cases, are not associated with similar levels of biological significance. Our results suggest that caution should be used in equating or comparing results obtained using different markers and/or methods of analysis.     

Empirical Bayes inference of pairwise F(ST) and its distribution in the genome

Populations often have very complex hierarchical structure. Therefore, it is crucial in genetic monitoring and conservation biology to have a reliable estimate of the pattern of population subdivision. F(ST)'s for pairs of sampled localities or subpopulations are crucial statistics for the exploratory analysis of population structures, such as cluster analysis and multidimensional scaling. However, the estimation of F(ST) is not precise enough to reliably estimate the population structure and the extent of heterogeneity. This article proposes an empirical Bayes procedure to estimate locus-specific pairwise F(ST)'s. The posterior mean of the pairwise F(ST) can be interpreted as a shrinkage estimator, which reduces the variance of conventional estimators largely at the expense of a small bias. The global F(ST) of a population generally varies among loci in the genome. Our maximum-likelihood estimates of global F(ST)'s can be used as sufficient statistics to estimate the distribution of F(ST) in the genome. We demonstrate the efficacy and robustness of our model by simulation and by an analysis of the microsatellite allele frequencies of the Pacific herring. The heterogeneity of the global F(ST) in the genome is discussed on the basis of the estimated distribution of the global F(ST) for the herring and examples of human single nucleotide polymorphisms (SNPs).