Letters to the editors: Testing the heterogeneity of F values

An allele-sharing, moment-based estimator of global, population-specific and population-pair FST under a general model of population structure

Being able to properly quantify genetic differentiation is key to understanding the evolutionary potential of a species. One central parameter in this context is FST, the mean coancestry within populations relative to the mean coancestry between populations. Researchers have been estimating FST globally or between pairs of populations for a long time. More recently, it has been proposed to estimate population-specific FST values, and population-pair mean relative coancestry. Here, we review the several definitions and estimation methods of FST, and stress that they provide values relative to a reference population. We show the good statistical properties of an allele-sharing, method of moments based estimator of FST (global, population-specific and population-pair) under a very general model of population structure. We point to the limitation of existing likelihood and Bayesian estimators when the populations are not independent. Last, we show that recent attempts to estimate absolute, rather than relative, mean coancestry fail to do so.

The genomic scale of fluctuating selection in a natural plant population

This study characterizes evolution at ≈1.86 million Single Nucleotide Polymorphisms (SNPs) within a natural population of yellow monkeyflower (Mimulus guttatus). Most SNPs exhibit minimal change over a span of 23 generations (less than 1% per year), consistent with neutral evolution in a large population. However, several thousand SNPs display strong fluctuations in frequency. Multiple lines of evidence indicate that these 'Fluctuating SNPs' are driven by temporally varying selection. Unlinked loci exhibit synchronous changes with the same allele increasing consistently in certain time intervals but declining in others. This synchrony is sufficiently pronounced that we can roughly classify intervals into two categories, "green" and "yellow," corresponding to conflicting selection regimes. Alleles increasing in green intervals are associated with early life investment in vegetative tissue and delayed flowering. The alternative alleles that increase in yellow intervals are associated with rapid progression to flowering. Selection on the Fluctuating SNPs produces a strong ripple effect on variation across the genome. Accounting for estimation error, we estimate the distribution of allele frequency change per generation in this population. While change is minimal for most SNPs, diffuse hitchhiking effects generated by selected loci may be driving neutral SNPs to a much greater extent than classic genetic drift.

Detecting Local Adaptation between North and South European Atlantic Salmon Populations

Pollution and other anthropogenic effects have driven a decrease in Atlantic salmon (Salmo salar) in the Iberian Peninsula. The restocking effort carried out in the 1980s, with salmon from northern latitudes with the aim of mitigating the decline of native populations, failed, probably due to the deficiency in adaptation of foreign salmon from northern Europe to the warm waters of the Iberian Peninsula. This result would imply that the Iberian populations of Atlantic salmon have experienced local adaptation in their past evolutionary history, as has been described for other populations of this species and other salmonids. Local adaptation can occur by divergent selections between environments, favoring the fixation of alleles that increase the fitness of a population in the environment it inhabits relative to other alleles favored in another population. In this work, we compared the genomes of different populations from the Iberian Peninsula (Atlantic and Cantabric basins) and Scotland in order to provide tentative evidence of candidate SNPs responsible for the adaptive differences between populations, which may explain the failures of restocking carried out during the 1980s. For this purpose, the samples were genotyped with a 220,000 high-density SNP array (Affymetrix) specific to Atlantic salmon. Our results revealed potential evidence of local adaptation for North Spanish and Scottish populations. As expected, most differences concerned the comparison of the Iberian Peninsula with Scotland, although there were also differences between Atlantic and Cantabric populations. A high proportion of the genes identified are related to development and cellular metabolism, DNA transcription and anatomical structure. A particular SNP was identified within the NADP-dependent malic enzyme-2 (_mMEP-2*), previously reported by independent studies as a candidate for local adaptation in salmon from the Iberian Peninsula. Interestingly, the corresponding SNP within the _mMEP-2* region was consistent with a genomic pattern of divergent selection.

Low genome-wide divergence between two lizard populations with high adaptive phenotypic differentiation

Usually, adaptive phenotypic differentiation is paralleled by genetic divergence between locally adapted populations. However, adaptation can also happen in a scenario of nonsignificant genetic divergence due to intense gene flow and/or recent differentiation. While this phenomenon is rarely published, findings on incipient ecologically driven divergence or isolation by adaptation are relatively common, which could confound our understanding about the frequency at which they actually occur in nature. Here, we explore genome-wide traces of divergence between two populations of the lacertid lizard Psammodromus algirus separated by a 600 m elevational gradient. These populations seem to be differentially adapted to their environments despite showing low levels of genetic differentiation (according to previously studies of mtDNA and microsatellite data). We performed a search for outliers (i.e., loci subject to selection) trying to identify specific loci with FST statistics significantly higher than those expected on the basis of overall, genome-wide estimates of genetic divergence. We find that local phenotypic adaptation (in terms of a wide diversity of characters) was not accompanied by genome-wide differentiation, even when we maximized the chances of unveiling such differentiation at particular loci with FST-based outlier detection tests. Instead, our analyses confirmed the lack of genome-wide differentiation on the basis of more than 70,000 SNPs, which is concordant with a scenario of local adaptation without isolation by environment. Our results add evidence to previous studies in which local adaptation does not lead to any kind of isolation (or early stages of ecological speciation), but maintains phenotypic divergence despite the lack of a differentiated genomic background.

Analysis validation has been neglected in the Age of Reproducibility

Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call "analysis validation." We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.

Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model through Trimming the Distribution of F(ST)

Loci responsible for local adaptation are likely to have more genetic differentiation among populations than neutral loci. However, neutral loci can vary widely in their amount of genetic differentiation, even over the same geographic range. Unfortunately, the distribution of differentiation--as measured by an index such as F(ST)--depends on the details of the demographic history of the populations in question, even without spatially heterogeneous selection. Many methods designed to detect F(ST) outliers assume a specific model of demographic history, which can result in extremely high false positive rates for detecting loci under selection. We develop a new method that infers the distribution of F(ST) for loci unlikely to be strongly affected by spatially diversifying selection, using data on a large set of loci with unknown selective properties. Compared to previous methods, this approach, called OutFLANK, has much lower false positive rates and comparable power, as shown by simulation.

Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests

F_ST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to nonequilibrium demographic histories has been understudied. We developed a landscape genetics simulator to test the effects of isolation by distance (IBD) and range expansion on F_ST outlier methods. We evaluated the two most commonly used methods for the identification of F_ST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary nonindependence). Parameterization with a set of neutral loci (‘neutral parameterization’) always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary nonindependence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives suggesting balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical P-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published F_ST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially divergent selection.

Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America

Understanding the genetic underpinnings of adaptive change is a fundamental but largely unresolved problem in evolutionary biology. Drosophila melanogaster, an ancestrally tropical insect that has spread to temperate regions and become cosmopolitan, offers a powerful opportunity for identifying the molecular polymorphisms underlying clinal adaptation. Here, we use genome-wide next-generation sequencing of DNA pools ('pool-seq') from three populations collected along the North American east coast to examine patterns of latitudinal differentiation. Comparing the genomes of these populations is particularly interesting since they exhibit clinal variation in a number of important life history traits. We find extensive latitudinal differentiation, with many of the most strongly differentiated genes involved in major functional pathways such as the insulin/TOR, ecdysone, torso, EGFR, TGFβ/BMP, JAK/STAT, immunity and circadian rhythm pathways. We observe particularly strong differentiation on chromosome 3R, especially within the cosmopolitan inversion In(3R)Payne, which contains a large number of clinally varying genes. While much of the differentiation might be driven by clinal differences in the frequency of In(3R)P, we also identify genes that are likely independent of this inversion. Our results provide genome-wide evidence consistent with pervasive spatially variable selection acting on numerous loci and pathways along the well-known North American cline, with many candidates implicated in life history regulation and exhibiting parallel differentiation along the previously investigated Australian cline.

Detecting selection in population trees: the Lewontin and Krakauer test extended

Detecting genetic signatures of selection is of great interest for many research issues. Common approaches to separate selective from neutral processes focus on the variance of F(ST) across loci, as does the original Lewontin and Krakauer (LK) test. Modern developments aim to minimize the false positive rate and to increase the power, by accounting for complex demographic structures. Another stimulating goal is to develop straightforward parametric and computationally tractable tests to deal with massive SNP data sets. Here, we propose an extension of the original LK statistic (T(LK)), named T(F-LK), that uses a phylogenetic estimation of the population's kinship (F) matrix, thus accounting for historical branching and heterogeneity of genetic drift. Using forward simulations of single-nucleotide polymorphisms (SNPs) data under neutrality and selection, we confirm the relative robustness of the LK statistic (T(LK)) to complex demographic history but we show that T(F-LK) is more powerful in most cases. This new statistic outperforms also a multinomial-Dirichlet-based model [estimation with Markov chain Monte Carlo (MCMC)], when historical branching occurs. Overall, T(F-LK) detects 15-35% more selected SNPs than T(LK) for low type I errors (P < 0.001). Also, simulations show that T(LK) and T(F-LK) follow a chi-square distribution provided the ancestral allele frequencies are not too extreme, suggesting the possible use of the chi-square distribution for evaluating significance. The empirical distribution of T(F-LK) can be derived using simulations conditioned on the estimated F matrix. We apply this new test to pig breeds SNP data and pinpoint outliers using T(F-LK), otherwise undetected using the less powerful T(LK) statistic. This new test represents one solution for compromise between advanced SNP genetic data acquisition and outlier analyses.

Detecting selection in population trees: the Lewontin and Krakauer test extended

Detecting genetic signatures of selection is of great interest for many research issues. Common approaches to separate selective from neutral processes focus on the variance of F(ST) across loci, as does the original Lewontin and Krakauer (LK) test. Modern developments aim to minimize the false positive rate and to increase the power, by accounting for complex demographic structures. Another stimulating goal is to develop straightforward parametric and computationally tractable tests to deal with massive SNP data sets. Here, we propose an extension of the original LK statistic (T(LK)), named T(F-LK), that uses a phylogenetic estimation of the population's kinship (F) matrix, thus accounting for historical branching and heterogeneity of genetic drift. Using forward simulations of single-nucleotide polymorphisms (SNPs) data under neutrality and selection, we confirm the relative robustness of the LK statistic (T(LK)) to complex demographic history but we show that T(F-LK) is more powerful in most cases. This new statistic outperforms also a multinomial-Dirichlet-based model [estimation with Markov chain Monte Carlo (MCMC)], when historical branching occurs. Overall, T(F-LK) detects 15-35% more selected SNPs than T(LK) for low type I errors (P < 0.001). Also, simulations show that T(LK) and T(F-LK) follow a chi-square distribution provided the ancestral allele frequencies are not too extreme, suggesting the possible use of the chi-square distribution for evaluating significance. The empirical distribution of T(F-LK) can be derived using simulations conditioned on the estimated F matrix. We apply this new test to pig breeds SNP data and pinpoint outliers using T(F-LK), otherwise undetected using the less powerful T(LK) statistic. This new test represents one solution for compromise between advanced SNP genetic data acquisition and outlier analyses.

Genomic differentiation between temperate and tropical Australian populations of Drosophila melanogaster

Determining the genetic basis of environmental adaptation is a central problem of evolutionary biology. This issue has been fruitfully addressed by examining genetic differentiation between populations that are recently separated and/or experience high rates of gene flow. A good example of this approach is the decades-long investigation of selection acting along latitudinal clines in Drosophila melanogaster. Here we use next-generation genome sequencing to reexamine the well-studied Australian D. melanogaster cline. We find evidence for extensive differentiation between temperate and tropical populations, with regulatory regions and unannotated regions showing particularly high levels of differentiation. Although the physical genomic scale of geographic differentiation is small--on the order of gene sized--we observed several larger highly differentiated regions. The region spanned by the cosmopolitan inversion polymorphism In(3R)P shows higher levels of differentiation, consistent with the major difference in allele frequencies of Standard and In(3R)P karyotypes in temperate vs. tropical Australian populations. Our analysis reveals evidence for spatially varying selection on a number of key biological processes, suggesting fundamental biological differences between flies from these two geographic regions.

Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L. (Salicaceae)

PREMISE OF THE STUDY

During past episodes of climate change, many plant species experienced large-scale range expansions. Expanding populations likely encountered strong selection as they colonized new environments. In this study we examine the extent to which populations of the widespread forest tree Populus balsamifera L. have become locally adapted as the species expanded into its current range since the last glaciation.

METHODS

We tested for adaptive variation in 13 ecophysiology and phenology traits on clonally propagated genotypes originating from a range-wide sample of 20 subpopulations. The hypothesis of local adaption was tested by comparing among-population variation at ecologically important traits (Q(ST)) to expected variation based on demographic history (F(ST)) estimated from a large set of nuclear single nucleotide polymorphism loci.

KEY RESULTS

Evidence for divergence in excess of neutral expectations was present for eight of 13 traits. Bud phenology, petiole length, and leaf nitrogen showed the greatest divergence (all Q(ST) > 0.6), whereas traits related to leaf water usage showed the least (all Q(ST) ≤ 0.30) and were not different from neutrality. Strong correlations were present between traits, geography, and climate, and they revealed a general pattern of northern subpopulations adapted to shorter, drier growing seasons compared with populations in the center or eastern regions of the range.

CONCLUSIONS

Our study demonstrates pronounced adaptive variation in ecophysiology and phenology among balsam poplar populations. These results suggest that as this widespread forest tree species expanded its range since the end of the last glacial maximum, it evolved rapidly in response to geographically variable selection.

The neutral theory of molecular evolution in the genomic era

The neutral theory of molecular evolution has been widely accepted and is the guiding principle for studying evolutionary genomics and the molecular basis of phenotypic evolution. Recent data on genomic evolution are generally consistent with the neutral theory. However, many recently published papers claim the detection of positive Darwinian selection via the use of new statistical methods. Examination of these methods has shown that their theoretical bases are not well established and often result in high rates of false-positive and false-negative results. When the deficiencies of these statistical methods are rectified, the results become largely consistent with the neutral theory. At present, genome-wide analyses of natural selection consist of collections of single-locus analyses. However, because phenotypic evolution is controlled by the interaction of many genes, the study of natural selection ought to take such interactions into account. Experimental studies of evolution will also be crucial.

Genome-wide scans for footprints of natural selection

Detecting recent selected ‘genomic footprints’ applies directly to the discovery of disease genes and in the imputation of the formative events that molded modern population genetic structure. The imprints of historic selection/adaptation episodes left in human and animal genomes allow one to interpret modern and ancestral gene origins and modifications. Current approaches to reveal selected regions applied in genome-wide selection scans (GWSSs) fall into eight principal categories: (I) phylogenetic footprinting, (II) detecting increased rates of functional mutations, (III) evaluating divergence versus polymorphism, (IV) detecting extended segments of linkage disequilibrium, (V) evaluating local reduction in genetic variation, (VI) detecting changes in the shape of the frequency distribution (spectrum) of genetic variation, (VII) assessing differentiating between populations (F_ST), and (VIII) detecting excess or decrease in admixture contribution from one population. Here, we review and compare these approaches using available human genome-wide datasets to provide independent verification (or not) of regions found by different methods and using different populations. The lessons learned from GWSSs will be applied to identify genome signatures of historic selective pressures on genes and gene regions in other species with emerging genome sequences. This would offer considerable potential for genome annotation in functional, developmental and evolutionary contexts.

Detecting loci under selection in a hierarchically structured population

Patterns of genetic diversity between populations are often used to detect loci under selection in genome scans. Indeed, loci involved in local adaptations should show high F(ST) values, whereas loci under balancing selection should rather show low F(ST) values. Most tests of selection based on F(ST) use a null distribution generated under a simple island model of population differentiation. Although this model has been shown to be robust, many species have a more complex genetic structure, with some populations sharing a recent ancestry or due to the presence of barriers to gene flow between different parts of a species range. In this paper, we propose the use of a hierarchical island model, in which demes exchange more migrants within groups than between groups, to generate the joint distribution of genetic diversity within and between populations. We show that tests not accounting for a hierarchical structure, when it exists, do generate a large excess of false positive loci, whereas the hierarchical island model is robust to uncertainties about the exact number of groups and demes per group in the system. Our approach also explicitly takes into account the mutational process, and does not just rely on allele frequencies, which is important for short tandem repeat (STR) data. An application to human and stickleback STR data sets reveals a much lower number of significant loci than previously obtained under a non-hierarchical model. The elimination of false positive loci from genome scans should allow us to better determine on which specific class of genes selection is operating.

LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method

BACKGROUND

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.

RESULTS

Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores.

CONCLUSION

LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method

Background

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.

Results

Here we present LOSITAN, a selection detection workbench based on a well evaluated F_st-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral F_st), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores.

Conclusion

LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

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.

Population genetics of Collisella subrugosa (Patellogastropoda: Acmaeidae): evidence of two scales of population structure

Marine invertebrate populations usually show high levels of genetic variability that has frequently been associated with spatial and temporal environmental heterogeneity. One of the most heterogeneous marine environments is the intertidal zone, the habitat of Collisella subrugosa, the most widespread and abundant Brazilian limpet. C. subrugosa has planktonic larvae that can disperse over long distances, what can promote gene flow among shores, working against interpopulational differentiation. In this study we investigated the genetic variability and populational substructure of C. subrugosa through analysis of 24 allozyme loci in 14 samples (590 individuals) collected along 2,700 km of the Brazilian coast. The genetic variability was high ([Formula: see text] and [Formula: see text]), as expected for intertidal species. Genetic differentiation among samples was low (F (ST) = 0.03) what may reflect intensive gene flow associated with larval dispersal. However, we detected an isolation-by-distance pattern of population substructure in one sampled region. High levels of heterozygote deficiency were also observed for many loci in each sample. Alternative hypothesis are discussed, and the "breeding groups" is suggested to explain these pattern, indicating the main cause as environmental heterogeneity.

Selective sweep mapping of genes with large phenotypic effects

Many domestic dog breeds have originated through fixation of discrete mutations by intense artificial selection. As a result of this process, markers in the proximity of genes influencing breed-defining traits will have reduced variation (a selective sweep) and will show divergence in allele frequency. Consequently, low-resolution genomic scans can potentially be used to identify regions containing genes that have a major influence on breed-defining traits. We model the process of breed formation and show that the probability of two or three adjacent marker loci showing a spurious signal of selection within at least one breed (i.e., Type I error or false-positive rate) is low if highly variable and moderately spaced markers are utilized. We also use simulations with selection to demonstrate that even a moderately spaced set of highly polymorphic markers (e.g., one every 0.8 cM) has high power to detect regions targeted by strong artificial selection in dogs. Further, we show that a gene responsible for black coat color in the Large Munsterlander has a 40-Mb region surrounding the gene that is very low in heterozygosity for microsatellite markers. Similarly, we survey 302 microsatellite markers in the Dachshund and find three linked monomorphic microsatellite markers all within a 10-Mb region on chromosome 3. This region contains the FGFR3 gene, which is responsible for achondroplasia in humans, but not in dogs. Consequently, our results suggest that the causative mutation is a gene or regulatory region closely linked to FGFR3.

Interpretation of variation across marker loci as evidence of selection

Population structure and history have similar effects on the genetic diversity at all neutral loci. However, some marker loci may also have been strongly influenced by natural selection. Selection shapes genetic diversity in a locus-specific manner. If we could identify those loci that have responded to selection during the divergence of populations, then we may obtain better estimates of the parameters of population history by excluding these loci. Previous attempts were made to identify outlier loci from the distribution of sample statistics under neutral models of population structure and history. Unfortunately these methods depend on assumptions about population structure and history that usually cannot be verified. In this article, we define new population-specific parameters of population divergence and construct sample statistics that are estimators of these parameters. We then use the joint distribution of these estimators to identify outlier loci that may be subject to selection. We found that outlier loci are easier to recognize when this joint distribution is conditioned on the total number of allelic states represented in the pooled sample at each locus. This is so because the conditional distribution is less sensitive to the values of nuisance parameters.

On the potential for estimating the effective number of breeders from heterozygote-excess in progeny

The important parameter of effective population size is rarely estimable directly from demographic data. Indirect estimates of effective population size may be made from genetic data such as temporal variation of allelic frequencies or linkage disequilibrium in cohorts. We suggest here that an indirect estimate of the effective number of breeders might be based on the excess of heterozygosity expected in a cohort of progeny produced by a limited number of males and females. In computer simulations, heterozygote excesses for 30 unlinked loci having various numbers of alleles and allele-frequency profiles were obtained for cohorts produced by samples of breeders drawn form an age-structured population and having known variance in reproductive success and effective number. The 95% confidence limits around the estimate contained the true effective population size in 70 of 72 trials and the Spearman rank correlation of estimated and actual values was 0.991. An estimate based on the heterozygote excess might have certain advantages over the previous estimates, requiring only single-locus and single-cohort data, but the sampling error among individuals and the effect of departures from random union of gametes still need to be explored.

Geographical patterns of karyotype polymorphism in Italian populations of Ornithogalum montanum (Liliaceae)

The geographical patterns of quinacrine banding polymorphism at 20 sites, and of numbers of accessory (B) chromosomes, were studied in nine Italian populations of Ornithogalum montanum Cyr. ex Ten. (Liliaceae). Eight banding sites appear monomorphic. The standardized gene frequency variance, Fst, is heterogeneous among the remaining 12 sites; variation is greatest for four polymorphisms whose frequency is correlated with the winter temperature of the localities studied. A strong negative association is apparent between numbers of B chromosomes and Q bands. Spatial autocorrelation shows three distinct modes of geographical variation: (i) random distributions; (ii) patterns with positive short-range autocorrelation; (iii) patterns with negative intermediate-range autocorrelation. Some microevolutionary implications of these findings are discussed.

Genetic and population structure of four Sardinian villages

Data on microgeographic population structure on four neighbouring villages of Sardinia island (Italy) are presented and discussed. Two villages are located in the lowlands where malaria from Plasmodium falciparum was endemic until the eradication of paludism. The other two villages are located in the highlands and they were malaria-free because of the altitude. Census data, inbreeding, migration matrices and surname distributions have been collected. The genetic differentiation of the four villages, tested for 31 genetic polymorphisms (106 alleles), is only in part compatible with migration rates inferred from demographic data. The possible adaptive nature of some genetic markers with respect to malarial resistance is discussed. Ambiguous results from population genetics quantitative methods do not support definite answers.