Isozyme analysis of genetic variability and population structure of Lactuca L. germplasm

Isozymes were used to investigate the genetic variability, population structure, and relationships of Lactuca germplasm. The isozyme systems revealed 16 putative loci of a total of 31 alleles. Out of these 16 loci, 11 were polymorphic. The average values of expected heterozygosity (H_e), observed heterozygosity (H_o), mean number of alleles per locus (A) and effective number of alleles per locus (A_e) were 0.2227, 0.266, 1.3005 and 1.369, respectively. The average fixation indices were lower than zero for most of the accessions studied, indicating an excess of heterozygotes. Genetic differentiation among accessions (F_ST) exhibited that 51.3% of the isozyme variation was recorded among accessions, and 48.7% of the genetic variation resided within accessions. The average values of total heterozygosity (H_T) and intra-accessional genetic diversity (H_S) were 0.352 and 0.171, respectively. Moreover, the inter-accessional genetic diversity (D_ST) ranged from 0 to 0.424 with an average of 0.18. Cluster analysis revealed that L. sativa cultivars were distributed throughout different Lactuca species. Thereby, isozymes results confirms the hypothesis of the polyphyletic origin of L. sativa. This high level of genetic variation proved that isozymes are efficient for polymorphism analysis of Lactuca germplasm.     

F(ST) and Q(ST) under neutrality

A commonly used test for natural selection has been to compare population differentiation for neutral molecular loci estimated by F_ST and for the additive genetic component of quantitative traits estimated by Q_ST. Past analytical and empirical studies have led to the conclusion that when averaged over replicate evolutionary histories, Q_ST = F_ST under neutrality. We used analytical and simulation techniques to study the impact of stochastic fluctuation among replicate outcomes of an evolutionary process, or the evolutionary variance, of Q_ST and F_ST for a neutral quantitative trait determined by n unlinked diallelic loci with additive gene action. We studied analytical models of two scenarios. In one, a pair of demes has recently been formed through subdivision of a panmictic population; in the other, a pair of demes has been evolving in allopatry for a long time. A rigorous analysis of these two models showed that in general, it is not necessarily true that mean Q_ST = F_ST (across evolutionary replicates) for a neutral, additive quantitative trait. In addition, we used finite-island model simulations to show there is a strong positive correlation between Q_ST and the difference Q_ST − F_ST because the evolutionary variance of Q_ST is much larger than that of F_ST. If traits with relatively large Q_ST values are preferentially sampled for study, the difference between Q_ST and F_ST will also be large and positive because of this correlation. Many recent studies have used tests of the null hypothesis Q_ST = F_ST to identify diversifying or uniform selection among subpopulations for quantitative traits. Our findings suggest that the distributions of Q_ST and F_ST under the null hypothesis of neutrality will depend on species-specific biology such as the number of subpopulations and the history of subpopulation divergence. In addition, the manner in which researchers select quantitative traits for study may introduce bias into the tests. As a result, researchers must be cautious before concluding that selection is occurring when Q_ST ≠ F_ST.     

Molecular genetic diversity and population structure in Lycium accessions using SSR markers

This study was conducted to assess the genetic diversity and population structure of 139 Lycium chinense accessions using 18 simple sequence repeat (SSR) markers. In total, 108 alleles were detected. The number of alleles per marker locus ranged from two to 17, with an average of six. The gene diversity and polymorphism information content value averaged 0.3792 and 0.3296, with ranges of 0.0793 to 0.8023 and 0.0775 to 0.7734, respectively. The average heterozygosity was 0.4394. The model-based structure analysis revealed the presence of three subpopulations, which was consistent with clustering based on genetic distance. An AMOVA analysis showed that the between-population component of genetic variance was less than 15.3%, in contrast to 84.7% for the within-population component. The overall F_ST value was 0.1178, indicating a moderate differentiation among groups. The results could be used for future L. chinense allele mining, association mapping, gene cloning, germplasm conservation, and designing effective breeding programs.     

The joint effects of selection and dominance on the QST - FST contrast

Q_ST measures the differentiation of quantitative traits between populations. It is often compared to F_ST, which measures population differentiation at neutral marker loci due to drift, migration, and mutation. When Q_ST is different from F_ST, it is usually taken as evidence that selection has either restrained or accelerated the differentiation of the quantitative trait relative to neutral markers. However, a number of other factors such as inbreeding, dominance, and epistasis may also affect the Q_ST − F_ST contrast. In this study, we examine the effects of dominance, selection, and inbreeding on Q_ST − F_ST. We compare Q_ST with F_ST at selected and neutral loci for populations at equilibrium between selection, drift, mutation, and migration using both analytic and simulation approaches. Interestingly, when divergent selection is acting on a locus, inbreeding and dominance generally inflate Q_ST relative to F_ST when they are both measured at the quantitative locus at equilibrium. As a consequence, dominance is unlikely to hide the signature of divergent selection on the Q_ST − F_ST contrast. However, although in theory dominance and inbreeding affect the expectation for Q_ST − F_ST, of most concern is the very large variance in both Q_ST and F_ST, suggesting that we should be cautious in attributing small differences between Q_ST and F_ST to selection.     

Molecular and quantitative trait variation within and among small fragmented populations of the endangered plant species Psilopeganum sinense

Background and Aims

Natural selection and genetic drift are important evolutionary forces in determining genetic and phenotypic differentiation in plant populations. The extent to which these two distinct evolutionary forces affect locally adaptive quantitative traits has been well studied in common plant and animal species. However, we know less about how quantitative traits respond to selection pressures and drift in endangered species that have small population sizes and fragmented distributions. To address this question, this study assessed the relative strengths of selection and genetic drift in shaping population differentiation of phenotypic traits in Psilopeganum sinense, a naturally rare and recently endangered plant species.

Methods

Population differentiation at five quantitative traits (Q_ST) obtained from a common garden experiment was compared with differentiation at putatively neutral microsatellite markers (F_ST) in seven populations of P. sinense. Q_ST estimates were derived using a Bayesian hierarchical variance component method.

Key Results

Trait-specific Q_ST values were equal to or lower than F_ST. Neutral genetic diversity was not correlated with quantitative genetic variation within the populations of P. sinense.

Conclusions

Despite the prevalent empirical evidence for Q_ST > F_ST, the results instead suggest a definitive role of stabilizing selection and drift leading to phenotypic differentiation among small populations. Three traits exhibited a significantly lower Q_ST relative to F_ST, suggesting that populations of P. sinense might have experienced stabilizing selection for the same optimal phenotypes despite large geographical distances between populations and habitat fragmentation. For the other two traits, Q_ST estimates were of the same magnitude as F_ST, indicating that divergence in these traits could have been achieved by genetic drift alone. The lack of correlation between molecular marker and quantitative genetic variation suggests that sophisticated considerations are required for the inference of conservation measures of P. sinense from neutral genetic markers.     

Genetic drift and uniform selection shape evolution of most traits in <Emphasis Type="Italic">Eugenia dysenterica</Emphasis> DC. (Myrtaceae)

Knowing how microevolutionary processes, such as genetic drift and natural selection, shape variation in adaptive traits is strategic for conservation measures. One way to estimate local adaptation is to compare divergences in quantitative traits (Q_ST) and neutral loci (F_ST). Therefore, we have assessed the pattern of phenotypic and molecular genetic divergence among natural subpopulations of the fruit tree Eugenia dysenterica DC. A provenance and progeny test was performed to assess the quantitative traits of the subpopulations collected in a wide distribution area of the species in the Brazilian Cerrado. The sampled environments are in a biodiversity hotspot with heterogeneous soil and climate conditions. By associating quantitative trait variation in initial seedling development with neutral microsatellite marker variation, we tested the local adaptation of the traits by the Q_ST–F_ST contrast. Genetic drift was prevalent in the phenotypic differentiation among the subpopulations, although the traits seedling emergence time and root green mass, which are relevant for adaptation to the Cerrado climate, showed signs of uniform selection. Our results suggest that E. dysenterica has a spatial genetic structure divided into two large groups, separated by a line that divides the Cerrado biome in a southwestern to northeastern direction. This structure must be taken into account for managing E. dysenterica genetic resources both for conservation and breeding purposes.     

Rapid adaptive divergence between ecotypes of an aquatic isopod inferred from FST–QST analysis

Divergent natural selection is often thought to be the principal factor driving phenotypic differentiation between populations. We studied two ecotypes of the aquatic isopod Asellus aquaticus which have diverged in parallel in several Swedish lakes. In these lakes, isopods from reed belts along the shores colonized new stonewort stands in the centre of the lakes and rapid phenotypic changes in size and pigmentation followed after colonization. We investigated if selection was likely to be responsible for these observed phenotypic changes using indirect inferences of selection (F_ST–Q_ST analysis). Average Q_ST for seven quantitative traits were higher than the average F_ST between ecotypes for putatively neutral markers (AFLPs). This suggests that divergent natural selection has played an important role during this rapid diversification. In contrast, the average Q_ST between the different reed ecotype populations was not significantly different from the mean F_ST. Genetic drift could therefore not be excluded as an explanation for the minor differences between allopatric populations inhabiting the same source habitat. We complemented this traditional F_ST–Q_ST approach by comparing the F_ST distributions across all loci (n = 67–71) with the Q_ST for each of the seven traits. This analysis revealed that pigmentation traits had diverged to a greater extent and at higher evolutionary rates than size‐related morphological traits. In conclusion, this extended and detailed type of F_ST–Q_ST analysis provides a powerful method to infer adaptive phenotypic divergence between populations. However, indirect inferences about the operation of divergent selection should be analyzed on a per‐trait basis and complemented with detailed ecological information.     

Unifying selection acts on competitive ability and relative growth rate in Scabiosa columbaria

Q_ST vs. F_ST comparisons can reveal diversifying or unifying selection pressures among populations for specific traits. In this study we performed Q_ST–F_ST analyses on eleven populations of Scabiosa columbaria from the Swiss Jura to reveal genetic differentiation in two quantitative traits (above-ground biomass and relative growth rate of leaf lengths) and in neutral molecular markers. Above-ground biomass of plants under competition has been shown to correlate with their competitive ability, which is an important fitness-related trait. We hypothesized that strong unifying selection acts on above-ground biomass, since underperformance would result in decreased fitness and overperformance is unlikely due to trade-offs with other plant functions.Overall G_ST (an F_ST analogue) was 0.12. Analysis of variance revealed that above-ground biomass and relative growth rate did not differ among populations, but both traits differed among seed families and were heritable (h² = 0.31 and h² = 0.35, respectively). Q_ST was close to zero for above-ground biomass and zero for relative growth rate of leaf lengths, and thus Q_ST was much lower than G_ST, indicating unifying selection on these traits.This conclusion is restricted by the limits of the used methodology. Q_ST < F_ST cannot always be considered as a proof for unifying selection, because in complex traits the assumption of purely additive effects of underlying genes may be violated. However, given the large differences between Q_ST and G_ST, together with substantial heritabilities of the traits under study, we conclude that our findings are not in contradiction with the hypothesis of unifying selection.     

Uniform selection as a primary force reducing population genetic differentiation of cavitation resistance across a species range

Background

Cavitation resistance to water stress-induced embolism determines plant survival during drought. This adaptive trait has been described as highly variable in a wide range of tree species, but little is known about the extent of genetic and phenotypic variability within species. This information is essential to our understanding of the evolutionary forces that have shaped this trait, and for evaluation of its inclusion in breeding programs.

Methodology

We assessed cavitation resistance (P ₅₀), growth and carbon isotope composition in six Pinus pinaster populations in a provenance and progeny trial. We estimated the heritability of cavitation resistance and compared the distribution of neutral markers (F _ST) and quantitative genetic differentiation (Q _ST), for retrospective identification of the evolutionary forces acting on these traits.

Results/Discussion

In contrast to growth and carbon isotope composition, no population differentiation was found for cavitation resistance. Heritability was higher than for the other traits, with a low additive genetic variance (h² _ns = 0.43±0.18, CV_A = 4.4%). Q _ST was significantly lower than F _ST, indicating uniform selection for P ₅₀, rather than genetic drift. Putative mechanisms underlying Q_STST are discussed.     

Comparison of genetic differentiation at marker loci and quantitative traits

The comparison of the degree of differentiation in neutral marker loci and genes coding quantitative traits with standardized and equivalent measures of genetic differentiation (F_ST and Q_ST, 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 (Q_ST) typically exceeds that observed in neutral marker genes (F_ST), suggesting a prominent role for natural selection in accounting for patterns of quantitative trait differentiation among contemporary populations. Second, the F_ST – Q_ST 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 Q_ST < F_ST, 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 Q_ST and F_ST 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.     

Isolation of microsatellite markers in Cannabis sativa L. (marijuana)

We have identified 15 variable microsatellite loci in Cannabis sativa. In 48 samples from five fibre crop seed accessions, we detected an average of 10 alleles per locus (range 2–28) with mean heterozygosity of 0.68 (range 0.28–0.94). Significant genetic differentiation was found between accessions (F_ST = 0.12, P < 0.001). These markers have utility for characterizing genetic diversity in cultivated and naturalized Cannabis populations.     

Microsatellite Variability of Pacific Herring <Emphasis Type="Italic">Clupea pallasii</Emphasis> Valenciennes, 1847 from the Sea of Okhotsk and Bering Sea

The genetic variability of ten microsatellite loci was examined in samples of the herring from the Sea of Okhotsk and the Bering Sea. All loci were polymorphic; the expected heterozygosity estimates varied in the range of 0.3–94.3% (mean 66.7%). The degree of genetic differentiation of the herring was statistically significant (θ = 1.38%). The level of pairwise genetic differentiation F_ST was–0.002–0.046; R_ST was–0.003–0.166. Genetic differentiation of the herring from the Sea of Okhotsk and the Bering Sea correlated with the spatial-geographic structure of the species in the studied range on the basis of F_ST (P = 0.001).     

Generalization of the QST framework in hierarchically structured populations: Impacts of inbreeding and dominance

Q_ST is a differentiation parameter based on the decomposition of the genetic variance of a trait. In the case of additive inheritance and absence of selection, it is analogous to the genic differentiation measured on individual loci, F_ST. Thus, Q_ST?F_ST comparison is used to infer selection: selective divergence when Q_ST > F_ST, or convergence when Q_ST < F_ST. The definition of Q‐statistics was extended to two‐level hierarchical population structures with Hardy–Weinberg equilibrium. Here, we generalize the Q‐statistics framework to any hierarchical population structure. First, we developed the analytical definition of hierarchical Q‐statistics for populations not at Hardy–Weinberg equilibrium. We show that the Q‐statistics values obtained with the Hardy–Weinberg definition are lower than their corresponding F‐statistics when F_IS > 0 (higher when F_IS < 0). Then, we used an island model simulation approach to investigate the impact of inbreeding and dominance on the Q_ST?F_ST framework in a hierarchical population structure. We show that, while differentiation at the lower hierarchical level (Q_SR) is a monotonic function of migration, differentiation at the upper level (Q_RT) is not. In the case of additive inheritance, we show that inbreeding inflates the variance of Q_RT, which can increase the frequency of Q_RT > F_RT cases. We also show that dominance drastically reduces Q‐statistics below F‐statistics for any level of the hierarchy. Therefore, high values of Q‐statistics are good indicators of selection, but low values are not in the case of dominance.     

Analysis of Genetic Variation in Natural Populations of Medicago truncatula of Southern Tunisian Ecological Areas, Using Morphological Traits and SSR Markers

We used 19 quantitative traits and 14 microsatellite markers (SSRs) to analyze the genetic variation in four natural populations of the model legume Medicago truncatula sampled in southern Tunisia. The greatest genetic variation of quantitative traits and molecular markers occurred within populations (>71%). In contrast to quantitative population differentiation (Q _ST ?=?0.09), a high level of molecular differentiation (F _ST ?=?0.23) was found among populations. The majority of quantitative traits exhibited Q _ST values significantly less than F _ST values, suggesting that selection may be acting to suppress differentiation for these traits. There was no significant correlation between genetic variation of quantitative traits and molecular markers within populations. On the other hand, significant correlations were found between measured quantitative characters and the site-of-origin environmental factors. The eco-geographical factors with the greatest influence on the variation of measured traits among populations were altitude, followed by soil texture, assimilated phosphorus (P₂O₅) and organic matter. Nevertheless, there were no consistent patterns of associations between gene diversity (He) and eco-geographical factors.     

Testing for Spatially Divergent Selection: Comparing QST to FST

Q_ST is a standardized measure of the genetic differentiation of a quantitative trait among populations. The distribution of Q_ST''s for neutral traits can be predicted from the F_ST for neutral marker loci. To test for the neutral differentiation of a quantitative trait among populations, it is necessary to ask whether the Q_ST of that trait is in the tail of the probability distribution of neutral traits. This neutral distribution can be estimated using the Lewontin–Krakauer distribution and the F_ST from a relatively small number of marker loci. We develop a simulation method to test whether the Q_ST of a given trait is consistent with the null hypothesis of selective neutrality over space. The method is most powerful with small mean F_ST, strong selection, and a large number (>10) of measured populations. The power and type I error rate of the new method are far superior to the traditional method of comparing Q_ST and F_ST.IN 1993, Spitze (1993) and Prout and Barker (1993) introduced Q_ST, a quantitative genetic analog of Wright''s F_ST. Just as F_ST gives a standardized measure of the genetic differentiation among populations for a genetic locus, Q_ST measures the amount of genetic variance among populations relative to the total genetic variance. In the years since, Q_ST has been frequently used to test for the effects of spatially divergent (or less commonly, spatially uniform) selection (see reviews in Lynch et al. 1999; Merilä and Crnokrak 2001; McKay and Latta 2002; Howe et al. 2003; Leinonen et al. 2008; Whitlock 2008). In principle, the average Q_ST of a neutral additive quantitative trait is expected to be equal to the mean value of F_ST for neutral genetic loci. F_ST can be readily measured on commonly available genetic markers, and Q_ST can be measured as well with an appropriate breeding design in a common-garden setting. As a result, Q_ST promises to be an index of the effect of selection on the quantitative trait. If Q_ST is higher than F_ST, then this is taken as evidence of spatially divergent selection on the trait. If Q_ST is much smaller than F_ST, then this has been taken as evidence of spatially uniform stabilizing selection, which makes the trait diverge less than expected by chance.The comparison with F_ST is essential to rule out genetic drift as an alternative mechanism for phenotypic divergence among populations. Because finite populations may diverge genetically in the absence of selection, divergence must be greater than expected by drift alone if we are to conclusively demonstrate that divergent selection has played a role in genetic differentiation among populations. Therefore it has become common practice to use F_ST of putatively neutral markers as a control for the effects of genetic drift and to compare observed Q_ST values for traits to these neutral F_ST values.These comparisons follow two separate methods, to address related but distinct questions. First, many studies of quantitative genetic differentiation measure the Q_ST of many traits and the F_ST of many loci, followed by a comparison of the mean Q_ST to the mean F_ST. Such a comparison may judge whether the conditions are suitable in that species for local adaptation, that is, whether selective differences between populations are large enough relative to gene flow to allow adaptive differentiation (Whitlock 2008). We do not consider this sort of comparison in this article.The other type of comparison asks whether the Q_ST of a single trait is greater than expected by drift, as measured by F_ST. This type of comparison is most common, but it is statistically difficult. Unfortunately, as emphasized in a recent review by Whitlock (2008), there is great variation in the expected F_ST among neutral loci and among the Q_ST of different neutral traits (see Figure 1). The majority of this variation results from evolutionary differences between loci and not sampling error in the observations. Rogers and Harpending (1983) imply that the distribution of Q_ST of a single neutral trait should be approximately equivalent to that for F_ST of a single neutral locus, and this has been confirmed by simulation for traits determined by additive loci compared to biallelic marker loci (Whitlock 2008). The two distributions are similar, but there is great heterogeneity among traits or loci. As a result, to show that selection is acting on a trait, it is necessary to show that the value of Q_ST has a low probability of being observed given the distribution of neutral Q_ST.Open in a separate windowFigure 1.—The distribution of F_ST for neutral loci and Q_ST for neutral quantitative traits. The histograms show the results of simulations of a set of 10 local populations each of 100 individuals, connected by 5% migration following island model assumptions. The solid line shows the distribution predicted by the Lewontin–Krakauer distribution. The distribution of Q_ST for neutral traits is very similar to the distribution of F_ST for single neutral loci, as can be seen by their mutual good fit to the Lewontin–Krakauer distribution (Figure modified from Whitlock 2008).Comparing Q_ST to the distribution inferred from F_ST is difficult for two reasons. First, typical data sets rarely include enough loci to directly infer the distribution of F_ST without extra inferential steps. In our approach, we use the distribution of Q_ST predicted from the mean F_ST and the χ² distribution by Lewontin and Krakauer (1973) to bridge this gap. Whitlock (2008) has shown that this distribution is appropriate for nearly all realistic situations for traits determined by additive genetic effects. Second, Q_ST for a trait is rarely measured with high precision, so the position of a given estimated Q_ST value in the distribution cannot be known without error.To test the null hypothesis that the spatial distribution of a particular trait is not affected by selection, we wish to compare the observed of that trait (marked with a hat to indicate it is an estimate) to the distribution of Q_ST expected for neutral traits. Unfortunately, calculating the distribution of Q_ST for neutral traits is not straightforward, because the estimate of Q_ST for a particular trait is variable for several reasons. The estimate of Q_ST is subject to measurement error, caused by the finite samples of families and individuals in the quantitative genetic experiment. These cause error in the estimate of the additive genetic variance within populations (V_A,within) and the genetic variance among populations (V_G,among), which translate into error of the estimate of Q_ST. In addition, there is another source of variation in Q_ST among neutral traits, caused by the idiosyncrasies of the evolutionary process in each local population in the study. The true value of Q_ST for the set of populations being studied can vary tremendously around its expectation, even for neutral traits, because by chance a finite set of populations may drift in a similar direction (Whitlock 2008). As a result, measurements of Q_ST can vary because of both statistical and evolutionary variation.Fortunately, these two sources of variation are fairly well understood individually. The sampling error for the estimates of the variance components can be estimated from standard approaches, and this variation can be well approximated using information from the mean squares of the analysis of the breeding experiment (O''Hara and Merilä 2005). The variation in neutral Q_ST that results from heterogeneity of evolutionary history can be approximated by the Lewontin–Krakauer distribution (Lewontin and Krakauer 1973), if information is available on the mean Q_ST of neutral traits (Whitlock 2008). This approximation does not depend on the demographic details of the populations in question (Whitlock 2008), but the effects of deviations from assumptions of additive gene effect have not yet been tested. The mean of the distribution of values of Q_ST for neutral traits is usually not known, but fortunately the mean of the distribution of F_ST of neutral loci is expected to be approximately equal to the mean Q_ST of neutral traits (Spitze 1993), and this does not depend on demographic details (Whitlock 1999). Therefore the mean F_ST measured from a series of genetic markers thought to be selectively neutral can be combined with the Lewontin–Krakauer distribution to predict the distribution of true neutral Q_ST across the range of possible evolutionary trajectories.Given that the mean value of of neutral traits is expected to equal the mean F_ST of neutral markers under certain assumptions (discussed later), we will use as a test statistic and compare the observed quantity to the zero value proposed by the null hypothesis. We will use a traditional hypothesis testing approach, which means that we need to specify the sampling distribution of under the assumption of neutrality. Traditionally, the sampling distribution of is inferred from the data on the trait itself, for example, using bootstrapping to infer the sampling distribution. This is appropriate when calculating a confidence interval for Q_ST but is a biased measure of the sampling variance of neutral Q_ST. The variance of the sampling distribution of varies with its expected value; larger values of true Q_ST have more variable sampling distributions than traits with smaller true Q_ST. This association between Q_ST and its sampling error is quite strong, as shown in Figure 2. As a result, if the sampling properties of neutral are inferred from a trait with high Q_ST, the estimate of the variance of the null distribution will be too high, and the hypothesis test comparing to F_ST will be conservative. On the other hand, if a low Q_ST is used to estimate the variance of the null distribution, the estimated error will be too small, and the test will reject true null hypotheses too often.Open in a separate windowFigure 2.—The width of the estimated sampling distribution of varies with mean Q_ST. The solid line shows the sampling distribution of Q_ST when the true mean Q_ST value is 0.05. The dotted line shows the sampling distribution that would be estimated for Q_ST from a trait that by chance was at the first percentile of this distribution, and the dashed line shows the sampling distribution that would be inferred from a value taken at the 99th percentile. If the Q_ST of a trait differs from the expectation by chance, then the width of the sampling distribution will also be estimated with substantial error. In particular, the error variance of is overestimated with Q_ST estimates that are too high and underestimated for small Q_ST values.We address this problem by using F_ST from putatively neutral maker loci in combination with estimates of the additive genetic variance within populations to predict the sampling variance that would be expected for the Q_ST of a neutral trait. We show that the power and type I error rate of this test are greatly superior to traditional methods.     

Genetic diversity and structure of local apple cultivars from Northeastern Spain assessed by microsatellite markers

A set of 493 old and local Spanish accessions of apple (Malus x domestica Borkh) maintained at three collections in Northeastern Spain was studied using 16 simple sequence repeats in order to estimate their genetic diversity and to identify the genetic structure and relationships among their accessions. An additional diverse set of 45 apple cultivars, including old Spanish and international cultivars, was added as reference. Genetic analyses performed by Bayesian model-based clustering revealed a very strong differentiation of two major groups. The first one clustered 159 individuals (52?% of unique genotypes) including local accessions and six old Spanish cultivars. The second major group was formed by 145 individuals, including 38 international reference cultivars and one old Spanish cultivar. Nested Bayesian clustering was applied to those two groups and two and four sub-groups were found at each one, respectively. The identification of private and unique alleles, and the remarkable differences in allelic richness among groups and sub-groups constitute further evidence of a clear genetic structure. The results obtained through the factorial correspondence and analyses of molecular variance confirmed those obtained by Bayesian analyses, revealing moderate but significant differentiation among the two major groups (F _ST?=?0.076) and the six sub-groups (F _ST?=?0.111). Our results highlight that the genetic diversity encompassed by currently cultivated apple accounts only for a small fraction of that existing within the species, and that an important part (??60?%) of the local material analyzed constitutes a good example of genetic distinctness with respect to the main cultivars used in European orchards.     

Genetic and Morphological Divergence in Three Strains of Brook Trout Salvelinus fontinalis Commonly Stocked in Lake Superior

Fitness related traits often show spatial variation across populations of widely distributed species. Comparisons of genetic variation among populations in putatively neutral DNA markers and in phenotypic traits susceptible to selection (Q_ST F_ST analysis) can be used to determine to what degree differentiation among populations can be attributed to selection or genetic drift. Traditionally, Q_ST F_ST analyses require a large number of populations to achieve sufficient statistical power; however, new methods have been developed that allow Q_ST F_ST comparisons to be conducted on as few as two populations if their pedigrees are informative. This study compared genetic and morphological divergence in three strains of brook trout Salvelinus fontinalis that were historically or currently used for stocking in the Lake Superior Basin. Herein we examined if morphological divergence among populations showed temporal variation, and if divergence could be attributed to selection or was indistinguishable from genetic drift. Multivariate Q_ST F_ST analysis showed evidence for divergent selection between populations. Univariate analyses suggests that the pattern observed in the multivariate analyses was largely driven by divergent selection for length and weight, and moreover by divergence between the Assinica strain and each of the Iron River and Siskiwit strains rather than divergent selection between each population pair. While it could not be determined if divergence was due to natural selection or inadvertent artificial selection in hatcheries, selected differences were consistent with patterns of domestication commonly found in salmonids.     

QST < FST As a signature of canalization

A key aim of evolutionary biology – inferring the action of natural selection on wild species – can be achieved by comparing neutral genetic differentiation between populations (F_ST) with quantitative genetic variation (Q_ST). Each of the three possible outcomes of comparisons of Q_ST and F_ST (Q_ST > F_ST, Q_ST = F_ST, Q_ST < F_ST) is associated with an inference (diversifying selection, genetic drift, uniform selection, respectively). However, published empirical and theoretical studies have focused on the Q_ST > F_ST outcome. We believe that this reflects the absence of a straightforward biological interpretation of the Q_ST < F_ST pattern. We here report recent evidence of this neglected evolutionary pattern, provide guidelines to its interpretation as either a canalization phenomenon or a consequence of uniform selection and discuss the significant importance this issue will have for the area of evolutionary biology.     

Heritability and quantitative genetic divergence of serotiny,a fire-persistence plant trait

Background and Aims

Although it is well known that fire acts as a selective pressure shaping plant phenotypes, there are no quantitative estimates of the heritability of any trait related to plant persistence under recurrent fires, such as serotiny. In this study, the heritability of serotiny in Pinus halepensis is calculated, and an evaluation is made as to whether fire has left a selection signature on the level of serotiny among populations by comparing the genetic divergence of serotiny with the expected divergence of neutral molecular markers (Q_ST–F_ST comparison).

Methods

A common garden of P. halepensis was used, located in inland Spain and composed of 145 open-pollinated families from 29 provenances covering the entire natural range of P. halepensis in the Iberian Peninsula and Balearic Islands. Narrow-sense heritability (h²) and quantitative genetic differentiation among populations for serotiny (Q_ST) were estimated by means of an ‘animal model’ fitted by Bayesian inference. In order to determine whether genetic differentiation for serotiny is the result of differential natural selection, Q_ST estimates for serotiny were compared with F_ST estimates obtained from allozyme data. Finally, a test was made of whether levels of serotiny in the different provenances were related to different fire regimes, using summer rainfall as a proxy for fire regime in each provenance.

Key Results

Serotiny showed a significant narrow-sense heritability (h²) of 0·20 (credible interval 0·09–0·40). Quantitative genetic differentiation among provenances for serotiny (Q_ST = 0·44) was significantly higher than expected under a neutral process (F_ST = 0·12), suggesting adaptive differentiation. A significant negative relationship was found between the serotiny level of trees in the common garden and summer rainfall of their provenance sites.

Conclusions

Serotiny is a heritable trait in P. halepensis, and selection acts on it, giving rise to contrasting serotiny levels among populations depending on the fire regime, and supporting the role of fire in generating genetic divergence for adaptive traits.     

Potential of SNP markers for the characterization of Brazilian cassava germplasm

Key message

High-throughput markers, such as SNPs, along with different methodologies were used to evaluate the applicability of the Bayesian approach and the multivariate analysis in structuring the genetic diversity in cassavas.

Abstract

The objective of the present work was to evaluate the diversity and genetic structure of the largest cassava germplasm bank in Brazil. Complementary methodological approaches such as discriminant analysis of principal components (DAPC), Bayesian analysis and molecular analysis of variance (AMOVA) were used to understand the structure and diversity of 1,280 accessions genotyped using 402 single nucleotide polymorphism markers. The genetic diversity (0.327) and the average observed heterozygosity (0.322) were high considering the bi-allelic markers. In terms of population, the presence of a complex genetic structure was observed indicating the formation of 30 clusters by DAPC and 34 clusters by Bayesian analysis. Both methodologies presented difficulties and controversies in terms of the allocation of some accessions to specific clusters. However, the clusters suggested by the DAPC analysis seemed to be more consistent for presenting higher probability of allocation of the accessions within the clusters. Prior information related to breeding patterns and geographic origins of the accessions were not sufficient for providing clear differentiation between the clusters according to the AMOVA analysis. In contrast, the F _ST was maximized when considering the clusters suggested by the Bayesian and DAPC analyses. The high frequency of germplasm exchange between producers and the subsequent alteration of the name of the same material may be one of the causes of the low association between genetic diversity and geographic origin. The results of this study may benefit cassava germplasm conservation programs, and contribute to the maximization of genetic gains in breeding programs.