Effective Population Size of Korean Populations

Signatures of past changes in population size have been detected in genome-wide variation in many species. However, the causes of such demographic changes and the extent to which they are shared across co-distributed species remain poorly understood. During Pleistocene glacial maxima, many temperate European species were confined to southern refugia. While vicariance and range expansion processes associated with glacial cycles have been widely documented, it is unclear whether refugial populations of co-distributed species have experienced shared histories of population size change. We analyse whole-genome sequence data to reconstruct and compare demographic histories during the Quaternary for Iberian refuge populations in a single ecological guild (seven species of chalcid parasitoid wasps associated with oak cynipid galls). For four of these species, we find support for large changes in effective population size (Ne ) through the Pleistocene that coincide with major climate events. However, there is little evidence that the timing, direction and magnitude of demographic change are shared across species, suggesting that demographic histories in this guild are largely idiosyncratic, even at the scale of a single glacial refugium.

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for stepwise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.

The fluctuation of population size has not been well studied in the previous studies of theoretical linkage disequilibrium (LD) expectation. In this study, an improved theoretical prediction of LD decay was derived to account for the effects of changes in effective population sizes. The equation was used to estimate effective population size (Ne) assuming a constant Ne and LD at equilibrium, and these Ne estimates implied the past changes of Ne for a certain number of generations until equilibrium, which differed based on recombination rate. As the influence of recent population history on the Ne estimates is larger than old population history, recent changes in population size can be inferred more accurately than old changes. The theoretical predictions based on this improved expression showed accurate agreement with the simulated values. When applied to human genome data, the detailed recent history of human populations was obtained. The inferred past population history of each population showed good correspondence with historical studies. Specifically, four populations (three African ancestries and one Mexican ancestry) showed population growth that was significantly less than that of other populations, and two populations originated from China showed prominent exponential growth. During the examination of overall LD decay in the human genome, a selection pressure on chromosome 14, the gephyrin gene, was observed in all populations.

The intersection of historiography and archaeology has long pondered over the impact of known historical events on census size. In recent times, genetic methods have successfully traced changes over time in the genetic size of a given population. Moreover, the correlation between genetic and census sizes of a population is contingent on several demographic assumptions that are relatively simple for our species. Our research endeavours to examine the changes in effective population size (Ne) in all human populations in the 1000 Genomes Project over the past two millennia. We compared our findings with estimates from historical censuses where available. Our investigation confirms what was already observed in France and reveals a common pattern found in most European populations, which manifests as a drastic population decrease beginning around the year 1300 and growth after the year 1600. This profile aligns well with known wars, famines, and epidemics that characterized these trying times in Europe. The most notable among them being the second plague epidemic, caused by Y. pestis, which in Europe commenced in 1347/8 and is also known as the "Black Death". Our findings demonstrate that changes in genetic population size through time can serve as a dependable proxy for census size, which is independent of potential biases in the written historical record. Consequently, we provide a robust estimate of the impact caused by the population crisis that followed the year 1300 on the European genomic landscape in light of previous results. Our study offers a new paradigm for interpreting the past and underscores the potential of genetic methods in reconstructing historical events.

Understanding and predicting population abundance is a major challenge confronting scientists. Several genetic models have been developed using microsatellite markers to estimate the present and ancestral effective population sizes. However, to get an overview on the evolution of population requires that past fluctuation of population size be traceable. To address the question, we developed a new model estimating the past changes of effective population size from microsatellite by resolving coalescence theory and using approximate likelihoods in a Monte Carlo Markov Chain approach. The efficiency of the model and its sensitivity to gene flow and to assumptions on the mutational process were checked using simulated data and analysis. The model was found especially useful to provide evidence of transient changes of population size in the past. The times at which some past demographic events cannot be detected because they are too ancient and the risk that gene flow may suggest the false detection of a bottleneck are discussed considering the distribution of coalescence times. The method was applied on real data sets from several Atlantic salmon populations. The method called VarEff (Variation of Effective size) was implemented in the R package VarEff and is made available at https://qgsp.jouy.inra.fr and at http://cran.r-project.org/web/packages/VarEff.

It is known that methods to estimate the rate of adaptive evolution, which are based on the McDonald–Kreitman test, can be biased by changes in effective population size. Here, we demonstrate theoretically that changes in population size can also generate an artifactual correlation between the rate of adaptive evolution and any factor that is correlated to the strength of selection acting against deleterious mutations. In this context, we have investigated whether several site-level factors influence the rate of adaptive evolution in the divergence of humans and chimpanzees, two species that have been inferred to have undergone population size contraction since they diverged. We find that the rate of adaptive evolution, relative to the rate of mutation, is higher for more exposed amino acids, lower for amino acid pairs that are more dissimilar in terms of their polarity, volume, and lower for amino acid pairs that are subject to stronger purifying selection, as measured by the ratio of the numbers of nonsynonymous to synonymous polymorphisms (pN/pS). All of these correlations are opposite to the artifactual correlations expected under contracting population size. We therefore conclude that these correlations are genuine.

Profound knowledge of demographic history is a prerequisite for the understanding and inference of processes involved in the evolution of population differentiation and speciation. Together with new coalescent-based methods, the recent availability of genome-wide data enables investigation of differentiation and divergence processes at unprecedented depth. We combined two powerful approaches, full Approximate Bayesian Computation analysis (ABC) and pairwise sequentially Markovian coalescent modeling (PSMC), to reconstruct the demographic history of the split between two avian speciation model species, the pied flycatcher and collared flycatcher. Using whole-genome re-sequencing data from 20 individuals, we investigated 15 demographic models including different levels and patterns of gene flow, and changes in effective population size over time. ABC provided high support for recent (mode 0.3 my, range <0.7 my) species divergence, declines in effective population size of both species since their initial divergence, and unidirectional recent gene flow from pied flycatcher into collared flycatcher. The estimated divergence time and population size changes, supported by PSMC results, suggest that the ancestral species persisted through one of the glacial periods of middle Pleistocene and then split into two large populations that first increased in size before going through severe bottlenecks and expanding into their current ranges. Secondary contact appears to have been established after the last glacial maximum. The severity of the bottlenecks at the last glacial maximum is indicated by the discrepancy between current effective population sizes (20,000–80,000) and census sizes (5–50 million birds) of the two species. The recent divergence time challenges the supposition that avian speciation is a relatively slow process with extended times for intrinsic postzygotic reproductive barriers to evolve. Our study emphasizes the importance of using genome-wide data to unravel tangled demographic histories. Moreover, it constitutes one of the first examples of the inference of divergence history from genome-wide data in non-model species.

Single nucleotide polymorphisms (SNPs) in receptor tyrosine kinase-like orphan receptor 2 (ROR2) gene were analyzed and compared between Han Chinese in Beijing (CHB) and Japanese in Tokyo (JPT) using the HapMap data, to provide basis for SNP determination. ROR2 gene related etiologic studies were conducted in the above mentioned two populations. Monotonic and un-monotonic SNPs of ROR2 gene were distinguished by Haploview program. Minor allele frequency (MAF), haplotype blocks and haplotype frequencies were analyzed in eligible SNPs and tag SNPs respectively with genotyping call rate > 80%, MAF > 1%, H-W equilibrium (P > 0.01) and no gender difference (P > 0.05). Tag SNPs were determined under the criteria of r(2) ≥ 0.8 or logarithm of the odd score (LOD) ≥ 3 for pairwise eligible SNPs in CHB and JPT. Common tag SNPs for CHB and JPT were directly reported by Haploview program or being identified from those which were higly related to tag SNPs reported by haploview program under SPSS 13.0 software. A total of 404 common SNPs were provided for both CHB and JPT samples by HapMap, where 101 common monotonic SNPs between CHB and JPT had the common minor alleles. The common SNPs between CHB and JPT were 257. In the 257 common eligible SNPs, 224 (87.2%) had common minor alleles. Among the 18 and 27 haplotype blocks identified in 257 common eligible SNPs between CHB and JPT, except for 2 independent haplotype blocks identified only in JPT. Other haplotype blocks between CHB and JPT were overlapped partly or completely. A number of 50 common tag SNPs between CHB and JPT were determined and the proportions in CHB and JPT were 64.9% and 70.4% respectively. Analysis of HapMap data provided an opportunity to avoid monotonic SNPs that had been included in ROR2 gene related etiologic studies. SNPs in ROR2 gene had common features in alleles, MAF, haplotype blocks and haplotype frequencies between CHB and JPT populations, which were consistent with the geographic and ethnic origins of the two populations.

Genetic analysis can provide valuable information for conservation programmes by unravelling the demographic trajectory of populations, estimating effective population size or inferring genetic differentiation between populations. Here, we investigated the genetic differentiation within Snowy Owls Bubo scandiacus in North America, a species identified as vulnerable by the IUCN, to (1) quantify connectivity among wintering areas, (2) evaluate current genetic diversity and effective population size, and (3) infer changes in the historical effective population size changes from the last millennia to the recent past. The Snowy Owl, a highly mobile top predator, breeds across the Arctic tundra, a region especially sensitive to current climate change. Using single‐nucleotide polymorphism (SNP)‐based analyses on Snowy Owls sampled across the North American non‐breeding range, we found an absence of genetic differentiation among individuals located up to 4650 km apart. Our results suggest high genetic intermixing and effective dispersal at the continental scale despite documented philopatry to non‐breeding sites in winter. Reconstructing the population demographic indicated that North American Snowy Owls have been steadily declining since the Last Glacial Maximum c. 20 000 years ago, and concurrently with global increases in temperature. Conservation programmes should now consider North American Snowy Owls a single, genetically homogeneous continental‐wide population which is probably sensitive to the long‐term global warming occurring since the Last Glacial Maximum.

In human populations, the relative levels of neutral diversity on the X and autosomes differ markedly from each other and from the naïve theoretical expectation of 3/4. Here we propose an explanation for these differences based on new theory about the effects of sex-specific life history and given pedigree-based estimates of the dependence of human mutation rates on sex and age. We demonstrate that life history effects, particularly longer generation times in males than in females, are expected to have had multiple effects on human X-to-autosome (X:A) diversity ratios, as a result of male-biased mutation rates, the equilibrium X:A ratio of effective population sizes, and the differential responses to changes in population size. We also show that the standard approach of using divergence between species to correct for male mutation bias results in biased estimates of X:A effective population size ratios. We obtain alternative estimates using pedigree-based estimates of the male mutation bias, which reveal that X:A ratios of effective population sizes are considerably greater than previously appreciated. Finally, we find that the joint effects of historical changes in life history and population size can explain the observed X:A diversity ratios in extant human populations. Our results suggest that ancestral human populations were highly polygynous, that non-African populations experienced a substantial reduction in polygyny and/or increase in the male-to-female ratio of generation times around the Out-of-Africa bottleneck, and that current diversity levels were affected by fairly recent changes in sex-specific life history.

Effective population size reflects the history of population growth, contraction, and structuring. When the effect of structuring is negligible, the inferred trajectory of the effective population size can be informative about the key events in the history of a population. We used the IBDNe and DoRIS approaches, which exploit the data on IBD sharing between genomes, to reconstruct the recent effective population size in two population datasets of Russians from Eastern European plain: (1) ethnic Russians sampled from the westernmost part of Russia; (2) ethnic Russians, Bashkirs, and Tatars sampled from the Volga-Ural region. In this way, we examined changes in effective population size among ethnic Russians that reside in their historical area at the West of the plain, and that expanded eastward to come into contact with the indigenous peoples at the East of the plain. We compared the inferred demographic trajectories of each ethnic group to written historical data related to demographic events such as migration, war, colonization, famine, establishment, and collapse of empires. According to IBDNe estimations, 200 generations (~6000 years) ago, the effective size of the ancestral populations of Russians, Bashkirs, and Tatars hovered around 3,000, 30,000, and 8,000 respectively. Then, the ethnic Russians exponentially grew with increasing rates for the last 115 generations and become the largest ethnic group of the plain. Russians do not show any drop in effective population size after the key historical conflicts, including the Mongol invasion. The only exception is a moderate drop in the 17th century, which is well known in Russian history as The Smuta. Our analyses suggest a more eventful recent population history for the two small ethnic groups that came into contact with ethnic Russians in the Volga-Ural region. We found that the effective population size of Bashkirs and Tatars started to decrease during the time of the Mongol invasion. Interestingly, there is an even stronger drop in the effective population size that coincides with the expansion of Russians to the East. Thus, 15–20 generations ago, i.e. in the 16–18th centuries in the trajectories of Bashkirs and Tatars, we observe the bottlenecks of four and twenty thousand, respectively. Our results on the recent effective population size correlate with the key events in the history of populations of the Eastern European plain and have importance for designing biomedical studies in the region.

Evaluation of genetic variability loss in a captive population of Pacific white shrimp Penaeus (Litopenaeus) vannamei using microsatellite and pedigree information

Dingoes (Canis familiaris) are an iconic Australian species and the top land predator. Much interest exists in their radiation process and evolutionary history in Australia. Recent research indicated that two evolutionarily independent units exist and that detected effective population size changes are due to the active control of this species. However, these conclusions have been critiqued because they were not explicitly tested or because the model assumptions may not be met in dingoes. We set out to statistically test these hypotheses by comparing alternative migration models and carrying out demographic analyses. We conclude that there is strong statistical support for the existence of the two evolutionary units. However, the analysis carried out to estimate the time of the effective population size changes does not have the required power to conclusively demonstrate whether the current management is having an impact on dingo populations. Future studies and different approaches will be needed to test this hypothesis.

The Discovery of Single-Nucleotide Polymorphisms—and Inferences about Human Demographic History

Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa.