A GENETIC PERSPECTIVE ON THE ORIGIN AND HISTORY OF HUMANS

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.

Recently, new methods have been developed for estimating the current and recent changes in effective population sizes. Based on the methods, the effective population sizes of Korean populations were estimated using data from the Korean Association Resource (KARE) project. The overall changes in the population sizes of the total populations were similar to CHB (Han Chinese in Beijing, China) and JPT (Japanese in Tokyo, Japan) of the HapMap project. There were no differences in past changes in population sizes with a comparison between an urban area and a rural area. Age-dependent current and recent effective population sizes represent the modern history of Korean populations, including the effects of World War II, the Korean War, and urbanization. The oldest age group showed that the population growth of Koreans had already been substantial at least since the end of the 19th century.

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.

Objective: To explore the trend of mortality rates for female breast cancer in China and quantify the impact of demographic and non-demographic factors on the burden of breast cancer mortality. Methods: Mortality data for female breast cancer from 2013 to 2021 were extracted from the Chinese Cause of Death Monitoring Dataset, and the 2000 Chinese population census data were used to standardize the mortality rates. The Joinpoint software was employed to analyze the mortality trends by calculating the Annual Percentage Change (APC) and Average Annual Percentage Change (AAPC). The population decomposition method was utilized to quantify the impact of changes in population age structure, population size, and non-demographic factors on the burden of breast cancer mortality. Results: From 2013 to 2021, the crude mortality rate for female breast cancer in China showed an increasing trend, with an AAPC of 2.3% (95% CI: 1.7%-2.9%). The standardized mortality rate remained relatively stable, with an AAPC of -0.2% (95% CI: -1.6%-1.3%). However, Joinpoint trend analysis indicated that the standardized mortality rate had a turning point in 2017, with a rapid increase before this year (APC: 3.9%, 95% CI: 1.1%-6.9%), and a rapid decline after this year (APC: -4.1%, 95% CI: -6.8% to -1.4%). The growth speed of crude mortality rates in rural areas was higher than that in urban areas, with AAPCs of 3.0% (95% CI: 2.4%-3.5%) and 1.3% (95% CI: 0.4%-2.2%), respectively. The standardized mortality rate in rural areas remained relatively stable, with an AAPC of 0.6% (95% CI: -1.0%-2.2%), while in urban areas, it showed a decreasing trend, with an AAPC of -1.1% (95% CI: -2.2%-0.0%). In the eastern, central, and western regions, the crude mortality rates all showed an increasing trend, with AAPCs of 1.7% (95% CI: 0.7%-2.8%), 3.8% (95% CI: 2.5%-5.2%), and 2.2% (95% CI: 0.5%-4.0%), respectively, while the standardized mortality rates remained relatively stable, with AAPCs of -0.3% (95% CI: -1.7%-1.2%), 0.6% (95% CI: -1.2%-2.4%), and 0.0% (95% CI: -2.2%-2.2%), respectively. Compared with 2013, the number of deaths in 2021 increased by 42.8%, of which changes in population age structure accounted for 21.3%, the age structure changes of urban and rural residents contributed 22.8% and 19.2%, respectively, to the whole changes caused by population age structure, while those in the eastern, central, and western regions contributed 20.6%, 24.3%, and 15.9%, respectively.Demographically, the changes in population size accounted for 18.3%, and non-demographic factors only accounted for 3.2%. Conclusions: From 2013 to 2021, the crude mortality rate for female breast cancer in China continued to rise, a trend mainly influenced by population age structure, with the fastest growth rates in crude mortality rates observed in rural areas and the central region. After adjusted for age structure, the standardized mortality rate for female breast cancer in China began to decline from 2017.

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.

The nearly neutral theory is a common framework to describe natural selection at the molecular level. This theory emphasizes the importance of slightly deleterious mutations by recognizing their ability to segregate and eventually get fixed due to genetic drift in spite of the presence of purifying selection. As genetic drift is stronger in smaller than in larger populations, a correlation between population size and molecular measures of natural selection is expected within the nearly neutral theory. However, this hypothesis was originally formulated under equilibrium conditions. As most natural populations are not in equilibrium, testing the relationship empirically may lead to confounded outcomes. Demographic nonequilibria, for instance following a change in population size, are common scenarios that are expected to push the selection–drift relationship off equilibrium. By explicitly modeling the effects of a change in population size on allele frequency trajectories in the Poisson random field framework, we obtain analytical solutions of the nonstationary allele frequency spectrum. This enables us to derive exact results of measures of natural selection and effective population size in a demographic nonequilibrium. The study of their time-dependent relationship reveals a substantial deviation from the equilibrium selection–drift balance after a change in population size. Moreover, we show that the deviation is sensitive to the combination of different measures. These results therefore constitute relevant tools for empirical studies to choose suitable measures for investigating the selection–drift relationship in natural populations. Additionally, our new modeling approach extends existing population genetics theory and can serve as foundation for methodological developments.

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.

Population size and population growth rate respond to changes in vital rates like survival and fertility. In deterministic environments change in population growth rate alone determines change in population size. In random environments, population size at any time t is a random variable so that change in population size obeys a probability distribution. We analytically show that, in a density-independent population, the proportional change in population size with respect to a small proportional change in a vital rate has an asymptotic normal distribution. Its mean grows linearly at a rate equal to the elasticity of the long-term stochastic growth rate λS while the standard deviation scales as \(\sqrt t\). Consequently, a vital rate with a larger elasticity of λS may produce a larger mean change in population size compared to one with a smaller elasticity of λS. But a given percentage change in population size may be more likely when the vital rate with smaller elasticity is perturbed. Hence, the response of population size to perturbation of a vital rate depends not only on the elasticity of the population growth rate but also on the variance in change in population size. Our results provide a formula to calculate the probability that population size changes by a given percentage that works well even for short time periods.

Climate-induced phenological shifts may have serious consequences for organisms and populations, but it is challenging to link such shifts to demographic change. Here, we present an overview of current methodological approaches for studying the demographic consequences of phenological shifts, based on a literature survey of 62 studies on diverse taxa. The majority of these studies (66%) were conducted using an approach that linked phenological shifts to demography through the measurement of vital rates (survival, growth, and fecundity). About 18% of the studies used a population-based approach that linked the phenological shifts to changes in population size, and 16% took a combined approach by considering changes in both vital rates and population size. Birds and mammals were overrepresented in studies of the demographic consequences of phenological shifts, compared to their occurrence in nature, while insects were heavily underrepresented. The effects of phenological shifts often varied according to the particular vital rate under consideration, in many cases even within a single species. In the few studies that examined changes in phenology together with both vital rate and population data, the changes in vital rates did not always predict changes in population size. To better understand the ultimate causes of population-level effects we argue that further study is needed on density-dependent aspects of population dynamics and on the sensitivity of population dynamics to perturbations in vital rates. We encourage re searchers to observe multiple vital rates throughout organisms' life-cycles in order to enable more meaningful examination of the consequences of phenological shifts for population dynamics. (Less)

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.

Monitoring changes in animal population size is rarely possible through complete censuses; frequently, the only feasible means of monitoring changes in population size is to use counts of animals obtained by skilled observers as indices to abundance. Analysis of changes in population size can be severely biased if factors related to the acquisition of data are not adequately controlled for. In particular, we identify two types of observer effects: these correspond to baseline differences in observer competence and to changes through time in the ability of individual observers. We present a family of models for count data in which the first of these observer effects is treated as a nuisance parameter. Conditioning on totals of negative binomial counts yields a Dirichlet compound multinomial vector for each observer. Quasi-likelihood is used to estimate parameters related to population trajectory and other parameters of interest; model selection is carried out on the basis of Akaike's information criterion. An example is presented using data on Wood thrush from the North American Breeding Bird Survey.

For a Wright-Fisher model with mutation whose population size fluctuates stochastically from generation to generation, a heterozygosity effective population size is defined by means of the equilibrium average heterozygosity of the population. It is shown that this effective population size is equal to the harmonic mean of population size if and only if the stochastic changes of population size are uncorrelated. The effective population size is larger (resp. smaller) than the harmonic mean when the stochastic changes of population size are positively (resp. negatively) autocorrelated. These results and those obtained so far for other stochastic models with fluctuating population size suggest that the property that effective population sizes are always larger than the harmonic mean under the fluctuation of population size holds only for continuous time models such as diffusion and coalescent models, whereas effective population sizes can be equal to or smaller than the harmonic mean for discrete time models.

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.

Recovery from imposex by a population of the dogwhelk, Nucella lapillus (Gastropoda: Caenogastropoda), on the southeastern coast of England since May 2004: A 52-month study

5 - Population Systems