Interlocus Gene Conversion, Natural Selection, and Paralog Homogenization.

Following a duplication, the resulting paralogs tend to diverge. While mutation and natural selection can accelerate this process, they can also slow it. Here, we quantify the paralog homogenization that is caused by point mutations and interlocus gene conversion (IGC). Among 164 duplicated teleost genes, the median percentage of postduplication codon substitutions that arise from IGC rather than point mutation is estimated to be between 7% and 8%. By differentiating between the nonsynonymous codon substitutions that homogenize the protein sequences of paralogs and the nonhomogenizing nonsynonymous substitutions, we estimate the homogenizing nonsynonymous rates to be higher for 163 of the 164 teleost data sets as well as for all 14 data sets of duplicated yeast ribosomal protein-coding genes that we consider. For all 14 yeast data sets, the estimated homogenizing nonsynonymous rates exceed the synonymous rates.

Massive Loss of Transcription Factors Promotes the Initial Diversification of Placental Mammals.

As one of the most successful group of organisms, mammals occupy a variety of niches on Earth as a result of macroevolution. Transcription factors (TFs), the fundamental regulators of gene expression, may also have evolved. To examine the relationship between TFs and mammalian macroevolution, we analyzed 140,821 de novo-identified TFs and their birth and death histories from 96 mammalian species. Gene tree vs. species tree reconciliation revealed that placental mammals experienced an upsurge in TF losses around 100 million years ago (Mya) and also near the Cretaceous-Paleogene boundary (K-Pg boundary, 66 Mya). Early Euarchontoglires, Laurasiatheria and marsupials appeared between 100 and 95 Mya and underwent initial diversification. The K-Pg boundary was associated with the massive extinction of dinosaurs, which lead to adaptive radiation of mammals. Surprisingly, TF loss decelerated, rather than accelerated, molecular evolutionary rates of their target genes. As the rate of molecular evolution is affected by the mutation rate, the proportion of neutral mutations and the population size, the decrease in molecular evolution may reflect increased functional constraints to survive target genes.

Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model.

The pattern of molecular evolution varies among gene sites and genes in a genome. By taking into account the complex heterogeneity of evolutionary processes among sites in a genome, Bayesian infinite mixture models of genomic evolution enable robust phylogenetic inference. With large modern data sets, however, the computational burden of Markov chain Monte Carlo sampling techniques becomes prohibitive. Here, we have developed a variational Bayesian procedure to speed up the widely used PhyloBayes MPI program, which deals with the heterogeneity of amino acid profiles. Rather than sampling from the posterior distribution, the procedure approximates the (unknown) posterior distribution using a manageable distribution called the variational distribution. The parameters in the variational distribution are estimated by minimizing Kullback-Leibler divergence. To examine performance, we analyzed three empirical data sets consisting of mitochondrial, plastid-encoded, and nuclear proteins. Our variational method accurately approximated the Bayesian inference of phylogenetic tree, mixture proportions, and the amino acid propensity of each component of the mixture while using orders of magnitude less computational time.

Viruses roll the dice: the stochastic behavior of viral genome molecules accelerates viral adaptation at the cell and tissue levels.

Recent studies on evolutionarily distant viral groups have shown that the number of viral genomes that establish cell infection after cell-to-cell transmission is unexpectedly small (1-20 genomes). This aspect of viral infection appears to be important for the adaptation and survival of viruses. To clarify how the number of viral genomes that establish cell infection is determined, we developed a simulation model of cell infection for tomato mosaic virus (ToMV), a positive-strand RNA virus. The model showed that stochastic processes that govern the replication or degradation of individual genomes result in the infection by a small number of genomes, while a large number of infectious genomes are introduced in the cell. It also predicted two interesting characteristics regarding cell infection patterns: stochastic variation among cells in the number of viral genomes that establish infection and stochastic inequality in the accumulation of their progenies in each cell. Both characteristics were validated experimentally by inoculating tobacco cells with a library of nucleotide sequence-tagged ToMV and analyzing the viral genomes that accumulated in each cell using a high-throughput sequencer. An additional simulation model revealed that these two characteristics enhance selection during tissue infection. The cell infection model also predicted a mechanism that enhances selection at the cellular level: a small difference in the replication abilities of coinfected variants results in a large difference in individual accumulation via the multiple-round formation of the replication complex (i.e., the replication machinery). Importantly, this predicted effect was observed in vivo. The cell infection model was robust to changes in the parameter values, suggesting that other viruses could adopt similar adaptation mechanisms. Taken together, these data reveal a comprehensive picture of viral infection processes including replication, cell-to-cell transmission, and evolution, which are based on the stochastic behavior of the viral genome molecules in each cell.

Phylogenetic skew: an index of community diversity.

The distribution of divergence times between member species of a community reflects the pattern of species composition. In this study, we contrast the species composition of a community against the meta-community, which we define as the species composition of a set of target communities. We regard the collection of species that comprise a community as a sample from the set of member species of the meta-community, and interpret the pattern of the community species composition in terms of the type of species sampled from the meta-community. A newly defined effective species sampling proportion explains the amount of the difference between the divergence time distributions of the community and that of the meta-community, assuming random sampling. We propose a new index of phylogenetic skew (PS), as the ratio of the maximum-likelihood estimate of the effective species sampling proportion to the observed sampling proportion. A PS value of 1 is interpreted as random sampling. If the value is >1, the sampling is suspected to be phylogenetically skewed. If it is <1, systematic thinning of species is likely. Unlike other indices, the PS does not depend on species richness as long as the community has more than a few members of a species. Because it is possible to compare partially observed communities, the index may be effectively used in exploratory analysis to detect candidate communities with unique species compositions from a large number of communities.

Spatial distribution of selection pressure on a protein based on the hierarchical Bayesian model.

Proteins adapt to novel environments and/or gain function by substitution in amino acid sequences. Therefore, mutations in protein-coding genes are subject to selection pressure. The strength and character of selection pressure may vary among the regions of the protein. Thus, the spatial distribution of selection pressure provides information on the adaptive evolution of the protein. We developed a hierarchical Bayesian model that detects the spatial distribution of selection pressure on a protein. We expressed selection pressure by the substitution rate ratio of nonsynonymous to synonymous substitutions in the DNA sequence. The Potts model describes the prior distribution of spatial aggregation of selection pressure. The hyperparameters that define the strength and range of spatial clustering are estimated by maximizing the marginal likelihood. Because our prior distribution is un-normalized, we calculated the log marginal likelihood by "thermodynamic integration." We applied the method to historical data on the influenza hemagglutinin protein, comparing the estimated spatial distribution of the substitution rate ratio with that of antigenic sites A-E. The amino acid residues with higher substitution rate ratios, representing diversifying selection pressure, overlapped the antigenic sites.

Coevolution and hierarchical interactions of Tomato mosaic virus and the resistance gene Tm-1.

During antagonistic coevolution between viruses and their hosts, viruses have a major advantage by evolving more rapidly. Nevertheless, viruses and their hosts coexist and have coevolved, although the processes remain largely unknown. We previously identified Tm-1 that confers resistance to Tomato mosaic virus (ToMV), and revealed that it encodes a protein that binds ToMV replication proteins and inhibits RNA replication. Tm-1 was introgressed from a wild tomato species Solanum habrochaites into the cultivated tomato species Solanum lycopersicum. In this study, we analyzed Tm-1 alleles in S. habrochaites. Although most part of this gene was under purifying selection, a cluster of nonsynonymous substitutions in a small region important for inhibitory activity was identified, suggesting that the region is under positive selection. We then examined the resistance of S. habrochaites plants to ToMV. Approximately 60% of 149 individuals from 24 accessions were resistant to ToMV, while the others accumulated detectable levels of coat protein after inoculation. Unexpectedly, many S. habrochaites plants were observed in which even multiplication of the Tm-1-resistance-breaking ToMV mutant LT1 was inhibited. An amino acid change in the positively selected region of the Tm-1 protein was responsible for the inhibition of LT1 multiplication. This amino acid change allowed Tm-1 to bind LT1 replication proteins without losing the ability to bind replication proteins of wild-type ToMV. The antiviral spectra and biochemical properties suggest that Tm-1 has evolved by changing the strengths of its inhibitory activity rather than diversifying the recognition spectra. In the LT1-resistant S. habrochaites plants inoculated with LT1, mutant viruses emerged whose multiplication was not inhibited by the Tm-1 allele that confers resistance to LT1. However, the resistance-breaking mutants were less competitive than the parental strains in the absence of Tm-1. Based on these results, we discuss possible coevolutionary processes of ToMV and Tm-1.

Taxon influence index: assessing taxon-induced incongruities in phylogenetic inference.

Understanding the evolutionary history of species is at the core of molecular evolution and is done using several inference methods. The critical issue is to quantify the uncertainty of the inference. The posterior probabilities in Bayesian phylogenetic inference and the bootstrap values in frequentist approaches measure the variability of the estimates due to the sampling of sites from genes and the sampling of genes from genomes. However, they do not measure the uncertainty due to taxon sampling. Taxa that experienced molecular homoplasy, recent selection, a spur of evolution, and so forth may disrupt the inference and cause incongruences in the estimated phylogeny. We define a taxon influence index to assess the influence of each taxon on the phylogeny. We found that although most taxa have a weak influence on the phylogeny, a small fraction of influential taxa strongly alter it even in clades only loosely related to them. We conclude that highly influential taxa should be given special attention and sampling them more thoroughly can lead to more dependable phylogenies.

Fractional parentage analysis and a scale-free reproductive network of brown trout.

In this study, we developed a method of fractional parentage analysis using microsatellite markers. We propose a method for calculating parentage probability, which considers missing data and genotyping errors due to null alleles and other causes, by regarding observed alleles as realizations of random variables which take values in the set of alleles at the locus and developing a method for simultaneously estimating the true and null allele frequencies of all alleles at each locus. We then applied our proposed method to a large sample collected from a wild population of brown trout (Salmo trutta). On analyzing the data using our method, we found that the reproductive success of brown trout obeyed a power law, indicating that when the parent-offspring relationship is regarded as a link, the reproductive system of brown trout is a scale-free network. Characteristics of the reproductive network of brown trout include individuals with large bodies as hubs in the network and different power exponents of degree distributions between males and females.

Codon pairs of the HIV-1 vif gene correlate with CD4+ T cell count.

BACKGROUND: The human APOBEC3G (A3G) protein activity is associated with innate immunity against HIV-1 by inducing high rates of guanosines to adenosines (G-to-A) mutations (viz., hypermutation) in the viral DNA. If hypermutation is not enough to disrupt the reading frames of viral genes, it may likely increase the HIV-1 diversity. To counteract host innate immunity HIV-1 encodes the Vif protein that binds A3G protein and form complexes to be degraded by cellular proteolysis. METHODS: Here we studied the pattern of substitutions in the vif gene and its association with clinical status of HIV-1 infected individuals. To perform the study, unique vif gene sequences were generated from 400 antiretroviral-naïve individuals. RESULTS: The codon pairs: 78-154, 85-154, 101-157, 105-157, and 105-176 of vif gene were associated with CD4+ T cell count lower than 500 cells per mm(3). Some of these codons were located in the (81)LGQGVSIEW(89) region and within the BC-Box. We also identified codons under positive selection clustered in the N-terminal region of Vif protein, between (21)WKSLVK(26) and (40)YRHHY(44) regions (i.e., 31, 33, 37, 39), within the BC-Box (i.e., 155, 159) and the Cullin5-Box (i.e., 168) of vif gene. All these regions are involved in the Vif-induced degradation of A3G/F complexes and the N-terminal of Vif protein binds to viral and cellular RNA. CONCLUSIONS: Adaptive evolution of vif gene was mostly to optimize viral RNA binding and A3G/F recognition. Additionally, since there is not a fully resolved structure of the Vif protein, codon pairs associated with CD4+ T cell count may elucidate key regions that interact with host cell factors. Here we identified and discriminated codons under positive selection and codons under functional constraint in the vif gene of HIV-1.

Genetic adaptations in the population history of Arabidopsis thaliana.

A population encounters a variety of environmental stresses, so the full source of its resilience can only be captured by collecting all the signatures of adaptation to the selection of the local environment in its population history. Based on the multiomic data of Arabidopsis thaliana, we constructed a database of phenotypic adaptations (p-adaptations) and gene expression (e-adaptations) adaptations in the population. Through the enrichment analysis of the identified adaptations, we inferred a likely scenario of adaptation that is consistent with the biological evidence from experimental work. We analyzed the dynamics of the allele frequencies at the 23,880 QTLs of 174 traits and 8,618 eQTLs of 1,829 genes with respect to the total SNPs in the genomes and identified 650 p-adaptations and 3,925 e-adaptations [false discovery rate (FDR) = 0.05]. The population underwent large-scale p-adaptations and e-adaptations along 4 lineages. Extremely cold winters and short summers prolonged seed dormancy and expanded the root system architecture. Low temperatures prolonged the growing season, and low light intensity required the increased chloroplast activity. The subtropical and humid environment enhanced phytohormone signaling pathways in response to the biotic and abiotic stresses. Exposure to heavy metals selected alleles for lower heavy metal uptake from soil, lower growth rate, lower resistance to bacteria, and higher expression of photosynthetic genes were selected. The p-adaptations are directly interpretable, while the coadapted gene expressions reflect the physiological requirements for the adaptation. The integration of this information characterizes when and where the population has experienced environmental stress and how the population responded at the molecular level.

ß-empirical Bayes inference and model diagnosis of microarray data.

BACKGROUND: Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, he data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models. RESULTS: As an extension of empirical Bayes (EB) procedures, we have developed the ß-empirical Bayes (ß-EB) approach based on a ß-likelihood measure which can be regarded as an 'evidence-based' weighted (quasi-) likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fß(yt|θ). Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of ß, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive ß0-likelihood by cross-validation. The proposed ß-EB approach identified six significant (p<10⁻5) contaminated transcripts as differentially expressed (DE) in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed ß-EB approach identified some potential master regulators that were missed by the EB approach. CONCLUSIONS: The simulation data and real gene expression data showed that the proposed ß-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model statistically. When ß-weights outside the range of the predicted distribution were observed, a detailed inspection of the data was carried out. The ß-weights described here can be applied to other likelihood-based statistical models for diagnosis, and may serve as a useful tool for transcriptome and proteome studies.

Molecular Evolutionary Rate Predicts Intraspecific Genetic Polymorphism and Species-Specific Selection.

It is unknown what determines genetic diversity and how genetic diversity is associated with various biological traits. In this work, we provide insight into these issues. By comparing genetic variation of 14,671 mammalian gene trees with thousands of individual human, chimpanzee, gorilla, mouse, and dog/wolf genomes, we found that intraspecific genetic diversity can be predicted by long-term molecular evolutionary rates rather than de novo mutation rates. This relationship was established during the early stage of mammalian evolution. Moreover, we developed a method to detect fluctuations of species-specific selection on genes based on the deviations of intraspecific genetic diversity predicted from long-term rates. We showed that the evolution of epithelial cells, rather than connective tissue, mainly contributed to morphological evolution of different species. For humans, evolution of the immune system and selective sweeps caused by infectious diseases are the most representative examples of adaptive evolution.

Exploratory analysis of multi-trait coadaptations in light of population history.

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation. To understand the whole history of populations, it is ideal to capture the history of their range expansion with reference to the series of surrounding environments and to infer the multitrait coadaptation. To this end, we propose a complementary approach; it is an exploratory analysis using up-to-date methods that integrate population genetic features and features of selection on multiple traits. First, we conduct correspondence analysis of site frequency spectra, traits, and environments with auxiliary information of population-specific fixation index (F ST). This visualizes the structure and the ages of populations and helps infer the history of range expansion, encountered environmental changes, and selection on multiple traits. Next, we further investigate the inferred history using an admixture graph that describes the population split and admixture. Finally, principal component analysis of the selection on edge-by-trait (SET) matrix identifies multitrait coadaptation and the associated edges of the admixture graph. We introduce a newly defined factor loadings of environmental variables in order to identify the environmental factors that caused the coadaptation. A numerical simulation of one-dimensional stepping-stone population expansion showed that the exploratory analysis reconstructed the pattern of the environmental selection that was missed by analysis of individual traits. Analysis of a public dataset of natural populations of black cottonwood in northwestern America identified the first principal component (PC) coadaptation of photosynthesis- vs growth-related traits responding to the geographical clines of temperature and day length. The second PC coadaptation of volume-related traits suggested that soil condition was a limiting factor for aboveground environmental selection.

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

BACKGROUND: Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance. RESULTS: We propose a flexible statistical framework to circumvent these problems. Inspired by spatial scan statistics for detecting clustering of disease occurrence in the field of epidemiology, we developed a scan statistic to extract disease-associated gene clusters from a whole gene pathway. Extracting one or a few significant gene clusters from a global pathway limits the overall false positive probability, which results in increased statistical power, and facilitates the interpretation of test results. In the present study, we applied our method to genome-wide association data for rare copy-number variations, which have been strongly implicated in common diseases. Application of our method to a simulated dataset demonstrated the high accuracy of this method in detecting disease-associated gene clusters in a whole gene pathway. CONCLUSIONS: The scan statistic approach proposed here shows a high level of accuracy in detecting gene clusters in a whole gene pathway. This study has provided a sound statistical framework for analyzing genome-wide rare CNV data by incorporating topological information on the gene pathway.

Structural considerations in the fitness landscape of a virus.

Viral fitness is determined by replication within hosts and transmission between them. We examine how pleiotropic mutations that have antagonistic effects (i.e., antibody evasion vs. receptor binding) on viral replication within hosts can impact viral immune escape in the host population. When the host population is vaccinated, the virus escapes from passive immunity by mutations in the antibody-binding region on the surface of the target protein. However, the reduced ability of the antibody to bind the virus is often accompanied by a reduced ability of the virus to bind the cell receptor because the antibody-binding region overlaps with the receptor-binding domain (RBD). The types of permitted mutations are limited. To investigate the causal relation between a mutation in a viral genome and adaptive evolution of a viral population, we developed a mathematical model that describes the population dynamics of viruses, antibodies, and normal/infected cells within a host. The coefficients describe the binding affinity between the virus and the induced antibody and that between the virus and its receptor. Our knowledge-based index enables us to estimate the effect of a mutation in a binding region on the binding affinity. Using population genetic theory, we evaluated the probability that a mutant is fixed in a host population. The mutations that can be fixed with high probabilities may determine how long a vaccine remains effective. We simulate the adaptive evolution of coronavirus, the etiological agent of severe acute respiratory syndrome, and show that some of mutations in the RBD may have high fixation probabilities in the vaccinated host population.

Estimation of the size of genetic bottlenecks in cell-to-cell movement of soil-borne wheat mosaic virus and the possible role of the bottlenecks in speeding up selection of variations in trans-acting genes or elements.

Genetic bottlenecks facilitate the fixation and extinction of variants in populations, and viral populations are no exception to this theory. To examine the existence of genetic bottlenecks in cell-to-cell movement of plant RNA viruses, we prepared constructs for Soil-borne wheat mosaic virus RNA2 vectors carrying two different fluorescent proteins, yellow fluorescent protein (YFP) and cyan fluorescent protein (CFP). Coinoculation of host plant leaves with the two RNA2 vectors and the wild-type RNA1 showed separation of the two vector RNA2s, mostly within seven to nine cell-to-cell movements from individual initially coinfected cells. Our statistical analysis showed that the number of viral RNA genomes establishing infection in adjacent cells after the first cell-to-cell movement from an initially infected cell was 5.97 +/- 0.22 on average and 5.02 +/- 0.29 after the second cell-to-cell movement. These results indicate that plant RNA viruses may generally face narrow genetic bottlenecks in every cell-to-cell movement. Furthermore, our model suggests that, rather than suffering from fitness losses caused by the bottlenecks, the plant RNA viruses are utilizing the repeated genetic bottlenecks as an essential element of rapid selection of their adaptive variants in trans-acting genes or elements to respond to host shifting and changes in the growth conditions of the hosts.

Nucleotide diversity and selection signature in the domesticated silkworm, Bombyx mori, and wild silkworm, Bombyx mandarina.

To investigate the patterns of nucleotide diversity in domesticated silkworm, Bombyx mori L. (Lepidoptera: Bombycidae) and its wild relative, Chinese wild silkworm, Bombyx mandarina Moore, we sequenced nine nuclear genes. Neutrality test and coalescent simulation for these genes were performed to look at bottleneck intensity and selection signature; linkage disequilibrium (LD) within and between loci was employed to investigate allele association. As a result, B. mori lost 33-49% of nucleotide diversity relative to wild silkworm, which is similar to the loss levels found in major cultivated crops. Diversity of B. mori is significantly lower than that of B. mandarina measured as π(total) (0.01166 vs. 0.1741) or θ(W)(0.01124 vs. 0.02206). Bottleneck intensity of domesticated silkworm is 1.5 (in terms of k = N(b) /d, N(b) -bottleneck population size; d-bottleneck duration) with different durations. Gene DefA showed signature of artificial selection by all analysis methods and might experience strong artificial selection in B. mori during domestication. For nine loci, both curves of LD decay rapidly within 200 bp and drop slowly when distance is > 200 bp, although that of B. mori decays slower than B. mandarina at loci investigated. However, LD could not be estimated at DefA in B. mori and at ER in both silkworms. Elevated LD observed in B. mori may be indicator of selection and demographic events.

Population structure of chum salmon and selection on the markers collected for stock identification.

Genetic stock identification (GSI) is a major management tool of Pacific salmon (Oncorhynchus Spp.) that has provided rich genetic baseline data of allozymes, microsatellites, and single-nucleotide polymorphisms (SNPs) across the Pacific Rim. Here, we analyzed published data sets for adult chum salmon (Oncorhynchus keta), namely 10 microsatellites, 53 SNPs, and a mitochondrial DNA locus (mtDNA3, control region, and NADH-3 combined) in samples from 495 locations in the same distribution range (n = 61,813). TreeMix analysis of the microsatellite loci identified the greatest convergence toward Japanese/Korean populations and suggested two admixture events from Japan/Korea to Russia and the Alaskan Peninsula. The SNPs had been purposively collected from rapidly evolving genes to increase the power of GSI. The largest expected heterozygosity was observed in Japanese/Korean populations for microsatellites, whereas it was largest in Western Alaskan populations for SNPs, reflecting the SNP discovery process. A regression of SNP population structures on those of microsatellites indicated the selection of the SNP loci according to deviations from the predicted structures. Specifically, we matched the sampling locations of the SNPs with those of the microsatellites and performed regression analyses of SNP allele frequencies on a 2-dimensional scaling (MDS) of matched locations obtained from microsatellite pairwise F ST values. The MDS first axis indicated a latitudinal cline in American and Russian populations, whereas the second axis showed differentiation of Japanese/Korean populations. The top five outlier SNPs included mtDNA3, U502241 (unknown), GnRH373, ras1362, and TCP178, which were identified by principal component analysis. We summarized the functions of 53 nuclear genes surrounding SNPs and the mtDNA3 locus by referring to a gene database system and propose how they may influence the fitness of chum salmon.

Understanding population structure in an evolutionary context: population-specific FST and pairwise FST.

Populations are shaped by their history. It is crucial to interpret population structure in an evolutionary context. Pairwise FST measures population structure, whereas population-specific FST measures deviation from the ancestral population. To understand the current population structure and a population's history of range expansion, we propose a representation method that overlays population-specific FST estimates on a sampling location map, and on an unrooted neighbor-joining tree and a multi-dimensional scaling plot inferred from a pairwise FST distance matrix. We examined the usefulness of our procedure using simulations that mimicked population colonization from an ancestral population and by analyzing published human, Atlantic cod, and wild poplar data. Our results demonstrated that population-specific FST values identify the source population and trace the evolutionary history of its derived populations. Conversely, pairwise FST values represent the current population structure. By integrating the results of both estimators, we obtained a new picture of the population structure that incorporates evolutionary history. The generalized least squares estimate of genome-wide population-specific FST indicated that the wild poplar population expanded its distribution to the north, where daylight hours are long in summer, to coastal areas with abundant rainfall, and to the south where summers are dry. Genomic data highlight the power of the bias-corrected moment estimators of FST, whether global, pairwise, or population-specific, that provide unbiased estimates of FST. All FST moment estimators described in this paper have reasonable processing times and are useful in population genomics studies.