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

A method of historical inference that accounts for ascertainment bias is developed and applied to single-nucleotide polymorphism (SNP) data in humans. The data consist of 84 short fragments of the genome that were selected, from three recent SNP surveys, to contain at least two polymorphisms in their respective ascertainment samples and that were then fully resequenced in 47 globally distributed individuals. Ascertainment bias is the deviation, from what would be observed in a random sample, caused either by discovery of polymorphisms in small samples or by locus selection based on levels or patterns of polymorphism. The three SNP surveys from which the present data were derived differ both in their protocols for ascertainment and in the size of the samples used for discovery. We implemented a Monte Carlo maximum-likelihood method to fit a subdivided-population model that includes a possible change in effective size at some time in the past. Incorrectly assuming that ascertainment bias does not exist causes errors in inference, affecting both estimates of migration rates and historical changes in size. Migration rates are overestimated when ascertainment bias is ignored. However, the direction of error in inferences about changes in effective population size (whether the population is inferred to be shrinking or growing) depends on whether either the numbers of SNPs per fragment or the SNP-allele frequencies are analyzed. We use the abbreviation "SDL," for "SNP-discovered locus," in recognition of the genomic-discovery context of SNPs. When ascertainment bias is modeled fully, both the number of SNPs per SDL and their allele frequencies support a scenario of growth in effective size in the context of a subdivided population. If subdivision is ignored, however, the hypothesis of constant effective population size cannot be rejected. An important conclusion of this work is that, in demographic or other studies, SNP data are useful only to the extent that their ascertainment can be modeled.     

An integrated framework for the inference of viral population history from reconstructed genealogies

We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.     

Global changes explain the long-term demographic trend of the Eurasian common lizard (Squamata: Lacertidae)

The demographic trend of a species depends on the dynamics of its local populations, which can be compromised by local or by global phenomena. However, the relevance of local and global phenomena has rarely been investigated simultaneously. Here, we tested whether local phenomena compromised a species’ demographic trend using the Eurasian common lizard Zootoca vivipara, the terrestrial reptile exhibiting the widest geographic distribution, as a model species. We analyzed the species’ ancient demographic trend using genetic data from its 6 allopatric genetic clades and tested whether its demographic trend mainly depended on single clades or on global phenomena. Zootoca vivipara’s effective population size increased since 2.3 million years ago and started to increase steeply and continuously from 0.531 million years ago. Population growth rate exhibited 2 maxima, both occurring during global climatic changes and important vegetation changes on the northern hemisphere. Effective population size and growth rate were negatively correlated with global surface temperatures, in line with global parameters driving long-term demographic trends. Zootoca vivipara’s ancient demography was neither driven by a single clade, nor by the 2 clades that colonized huge geographic areas after the last glaciation. The low importance of local phenomena, suggests that the experimentally demonstrated high sensitivity of this species to short-term ecological changes is a response in order to cope with short-term and local changes. This suggests that what affected its long-term demographic trend the most, were not these local changes/responses, but rather the important and prolonged global climatic changes and important vegetation changes on the northern hemisphere, including the opening up of the forest by humans.     

Environmental stochasticity and extinction risk in a population of a small songbird, the great tit

Using a long-term demographic data set, we estimated the separate effects of demographic and environmental stochasticity in the growth rate of the great tit population in Wytham Wood, United Kingdom. Assuming logistic density regulation, both the demographic (sigma2d = 0.569) and environmental (sigma2e = 0.0793) variance, with interactions included, were significantly greater than zero. The estimates of the demographic variance seemed to be relatively insensitive to the length of the study period, whereas reliable estimates of the environmental variance required long time series (at least 15 yr of data). The demographic variance decreased significantly with increasing population density. These estimates are used in a quantitative analysis of the demographic factors affecting the risk of extinction of this population. The very long expected time to extinction of this population (approximately 10(19) yr) was related to a relatively large population size (>/=120 pairs during the study period). However, for a given population size, the expected time to extinction was sensitive to both variation in population growth rate and environmental stochasticity. Furthermore, the form of the density regulation strongly affected the expected time to extinction. Time to extinction decreased when the maximum density regulation approached K. This suggests that estimates of viability of small populations should be given both with and without inclusion of density dependence.     

Detecting ancient admixture in humans using sequence polymorphism data

A debate of long-standing interest in human evolution centers around whether archaic human populations (such as the Neanderthals) have contributed to the modern gene pool. A model of ancient population structure with recent mixing is introduced, and it is determined how much information (i.e., sequence data from how many unlinked nuclear loci) would be necessary to distinguish between different demographic scenarios. It is found that approximately 50-100 loci are necessary if plausible parameter estimates are used. There are not enough data available at the present to support either the "single origin" or the "multiregional" model of modern human evolution. However, this information should be available in a few years.     

Integral projection models perform better for small demographic data sets than matrix population models: a case study of two perennial herbs

1. Matrix population models are widely used to describe population dynamics, conduct population viability analyses and derive management recommendations for plant populations. For endangered or invasive species, management decisions are often based on small demographic data sets. Hence, there is a need for population models which accurately assess population performance from such small data sets.
2. We used demographic data on two perennial herbs with different life histories to compare the accuracy and precision of the traditional matrix population model and the recently developed integral projection model (IPM) in relation to the amount of data.
3. For large data sets both matrix models and IPMs produced identical estimates of population growth rate (λ). However, for small data sets containing fewer than 300 individuals, IPMs often produced smaller bias and variance for λ than matrix models despite different matrix structures and sampling techniques used to construct the matrix population models.
4. Synthesis and applications . Our results suggest that the smaller bias and variance of λ estimates make IPMs preferable to matrix population models for small demographic data sets with a few hundred individuals. These results are likely to be applicable to a wide range of herbaceous, perennial plant species where demographic fate can be modelled as a function of a continuous state variable such as size. We recommend the use of IPMs to assess population performance and management strategies particularly for endangered or invasive perennial herbs where little demographic data are available.     

Reconstructing the dynamics of ancient human populations from radiocarbon dates: 10 000 years of population growth in Australia

Measuring trends in the size of prehistoric populations is fundamental to our understanding of the demography of ancient people and their responses to environmental change. Archaeologists commonly use the temporal distribution of radiocarbon dates to reconstruct population trends, but this can give a false picture of population growth because of the loss of evidence from older sites. We demonstrate a method for quantifying this bias, and we use it to test for population growth through the Holocene of Australia. We used model simulations to show how turnover of site occupation across an archaeological landscape, interacting with erasure of evidence at abandoned sites, can create an increase in apparent site occupation towards the present when occupation density is actually constant. By estimating the probabilities of abandonment and erasure from archaeological data, we then used the model to show that this effect does not account for the observed increase in occupation through the Holocene in Australia. This is best explained by population growth, which was low for the first part of the Holocene but accelerated about 5000 years ago. Our results provide new evidence for the dynamism of non-agricultural populations through the Holocene.     

Spatial data replacing temporal data in population viability analyses: An empirical investigation for plants

In conservation management, there is an urgent need for estimates of population viability and for knowledge of the contributions of different life-history stages to population growth rates. Collection of long-term demographic data from a study population is time-consuming and may considerably delay the start of proper management actions. We examined the possibility of replacing a long-term temporal data set (demographic data from several years within a population) with a short-term spatial data set (demographic data from different populations for the same subset of two continuous years) for stochastic estimates of population viability. Using matrix population models for ten perennial plant species, we found that the matrix elements of spatial data sets often deviated from those of temporal data sets and that matrix elements generally varied more spatially than temporally. The appropriateness of replacing temporal data with spatial data depended on the subset of years and populations used to estimate stochastic population growth rates (log λ_s). Still, the precision of log λ_s estimates measured as variation in the yearly change of logarithmic population size rarely differed significantly between the spatial and temporal data sets. Since a spatiotemporal comparison of matrix elements and their variation cannot be used to assess whether spatial and temporal data sets are interchangeable, we recommend further research on the topic.     

Effects of ascertainment bias on recovering human demographic history.

In recent years multilocus data sets have been used to study the demographic history of human populations. In this paper (1) analyses previously done on 60 short tandem repeat (STR) loci are repeated on 30 restriction site polymorphism (RSP) markers; (2) relative population weights are estimated from the RSP data set and compared to previously published estimates from STR and craniometric data sets; and (3) computer simulations are performed to show the effects of ascertainment bias on relative population weight estimates. Not surprisingly, given that the RSP markers were originally identified in a small panel of Caucasians, estimates of relative population weights are biased and the European population weight is artificially inflated. However, the effects of ascertainment bias are not apparent in a principal components plot or estimates of FST. Ascertainment bias can have a large effect in other genetic systems with inherently low heterozygosity such as Alus or single nucleotide polymorphisms (SNPs), and care must be taken to have prior knowledge of how polymorphic markers in a given data set were originally identified. Otherwise, results can be skewed and interpretations faulty.     

Inferring population decline and expansion from microsatellite data: a simulation-based evaluation of the Msvar method

Reconstructing the demographic history of populations is a central issue in evolutionary biology. Using likelihood-based methods coupled with Monte Carlo simulations, it is now possible to reconstruct past changes in population size from genetic data. Using simulated data sets under various demographic scenarios, we evaluate the statistical performance of Msvar, a full-likelihood Bayesian method that infers past demographic change from microsatellite data. Our simulation tests show that Msvar is very efficient at detecting population declines and expansions, provided the event is neither too weak nor too recent. We further show that Msvar outperforms two moment-based methods (the M-ratio test and Bottleneck) for detecting population size changes, whatever the time and the severity of the event. The same trend emerges from a compilation of empirical studies. The latest version of Msvar provides estimates of the current and the ancestral population size and the time since the population started changing in size. We show that, in the absence of prior knowledge, Msvar provides little information on the mutation rate, which results in biased estimates and/or wide credibility intervals for each of the demographic parameters. However, scaling the population size parameters with the mutation rate and scaling the time with current population size, as coalescent theory requires, significantly improves the quality of the estimates for contraction but not for expansion scenarios. Finally, our results suggest that Msvar is robust to moderate departures from a strict stepwise mutation model.     

Likelihood and Bayes estimation of ancestral population sizes in hominoids using data from multiple loci

Polymorphisms in an ancestral population can cause conflicts between gene trees and the species tree. Such conflicts can be used to estimate ancestral population sizes when data from multiple loci are available. In this article I extend previous work for estimating ancestral population sizes to analyze sequence data from three species under a finite-site nucleotide substitution model. Both maximum-likelihood (ML) and Bayes methods are implemented for joint estimation of the two speciation dates and the two population size parameters. Both methods account for uncertainties in the gene tree due to few informative sites at each locus and make an efficient use of information in the data. The Bayes algorithm using Markov chain Monte Carlo (MCMC) enjoys a computational advantage over ML and also provides a framework for incorporating prior information about the parameters. The methods are applied to a data set of 53 nuclear noncoding contigs from human, chimpanzee, and gorilla published by Chen and Li. Estimates of the effective population size for the common ancestor of humans and chimpanzees by both ML and Bayes methods are approximately 12,000-21,000, comparable to estimates for modern humans, and do not support the notion of a dramatic size reduction in early human populations. Estimates published previously from the same data are several times larger and appear to be biased due to methodological deficiency. The divergence between humans and chimpanzees is dated at approximately 5.2 million years ago and the gorilla divergence 1.1-1.7 million years earlier. The analysis suggests that typical data sets contain useful information about the ancestral population sizes and that it is advantageous to analyze data of several species simultaneously.     

Heterogeneity of microsatellite mutations within and between loci,and implications for human demographic histories.

Microsatellites have been widely used to reconstruct human evolution. However, the efficient use of these markers relies on information regarding the process producing the observed variation. Here, we present a novel approach to the locus-by-locus characterization of this process. By analyzing somatic mutations in cancer patients, we estimated the distributions of mutation size for each of 20 loci. The same loci were then typed in three ethnically diverse population samples. The generalized stepwise mutation model was used to test the predicted relationship between population and mutation parameters under two demographic scenarios: constant population size and rapid expansion. The agreement between the observed and expected relationship between population and mutation parameters, even when the latter are estimated in cancer patients, confirms that somatic mutations may be useful for investigating the process underlying population variation. Estimated distributions of mutation size differ substantially amongst loci, and mutations of more than one repeat unit are common. A new statistic, the normalized population variance, is introduced for multilocus estimation of demographic parameters, and for testing demographic scenarios. The observed population variation is not consistent with a constant population size. Time estimates of the putative population expansion are in agreement with those obtained by other methods.     

The effect of ancient DNA damage on inferences of demographic histories

The field of ancient DNA (aDNA) is casting new light on many evolutionary questions. However, problems associated with the postmortem instability of DNA may complicate the interpretation of aDNA data. For example, in population genetic studies, the inclusion of damaged DNA may inflate estimates of diversity. In this paper, we examine the effect of DNA damage on population genetic estimates of ancestral population size. We simulate data using standard coalescent simulations that include postmortem damage and show that estimates of effective population sizes are inflated around, or right after, the sampling time of the ancestral DNA sequences. This bias leads to estimates of increasing, and then decreasing, population sizes, as observed in several recently published studies. We reanalyze a recently published data set of DNA sequences from the Bison (Bison bison/Bison priscus) and show that the signal for a change in effective population size in this data set vanishes once the effects of putative damage are removed. Our results suggest that population genetic analyses of aDNA sequences, which do not accurately account for damage, should be interpreted with great caution.     

突变积累实验中极大似然法和距法的比较

最近,人们突变积累实验（MA）中测定有害基因突变（DGM）的兴趣大增。在MA实验中有两种常见的DGM估计方法（极大似然法ML和距法MM）,依靠计算机模拟和处理真实数据的应用软件来比较这两种方法。结论是：ML法难于得到最大似然估计（MLEs),所以ML法不如MM法估计有效;即使MLEs可得,也因其具严重的微样误差（据偏差和抽样差异）而产生估计偏差;似然函数曲线较平坦而难于区分高峰态和低峰态的分布。     

Using maximum likelihood to estimate population size from temporal changes in allele frequencies.

We develop a maximum-likelihood framework for using temporal changes in allele frequencies to estimate the number of breeding individuals in a population. We use simulations to compare the performance of this estimator to an F-statistic estimator of variance effective population size. The maximum-likelihood estimator had a lower variance and smaller bias. Taking advantage of the likelihood framework, we extend the model to include exponential growth and show that temporal allele frequency data from three or more sampling events can be used to test for population growth.     

Model-based comparisons of phylogeographic scenarios resolve the intraspecific divergence of cactophilic Drosophila mojavensis

The cactophilic fly Drosophila mojavensis exhibits considerable intraspecific genetic structure across allopatric geographic regions and shows associations with different host cactus species across its range. The divergence between these populations has been studied for more than 60years, yet their exact historical relationships have not been resolved. We analysed sequence data from 15 intronic X-linked loci across populations from Baja California, mainland Sonora-Arizona and Mojave Desert regions under an isolation-with-migration model to assess multiple scenarios of divergence. We also compared the results with a pre-existing sequence data set of eight autosomal loci. We derived a population tree with Baja California placed at its base and link their isolation to Pleistocene climatic oscillations. Our estimates suggest the Baja California population diverged from an ancestral Mojave Desert/mainland Sonora-Arizona group around 230,000-270,000years ago, while the split between the Mojave Desert and mainland Sonora-Arizona populations occurred one glacial cycle later, 117,000-135,000years ago. Although we found these three populations to be effectively allopatric, model ranking could not rule out the possibility of a low level of gene flow between two of them. Finally, the Mojave Desert population showed a small effective population size, consistent with a historical population bottleneck. We show that model-based inference from multiple loci can provide accurate information on the historical relationships of closely related groups allowing us to set into historical context a classic system of incipient ecological speciation.     

The effects of demography and linkage on the estimation of selection and mutation parameters

We explore the effects of demography and linkage on a maximum-likelihood (ML) method for estimating selection and mutation parameters in a reversible mutation model. This method assumes free recombination between sites and a randomly mating population of constant size and uses information from both polymorphic and monomorphic sites in the sample. Two likelihood-ratio test statistics were constructed under this ML framework: LRTγ for detecting selection and LRTκ for detecting mutational bias. By carrying out extensive simulations, we obtain the following results. When mutations are neutral and population size is constant, LRTγ and LRTκ follow a chi-square distribution with 1 d.f. regardless of the level of linkage, as long as the mutation rate is not very high. In addition, LRTγ and LRTκ are relatively insensitive to demographic effects and selection at linked sites. We find that the ML estimators of the selection and mutation parameters are usually approximately unbiased and that LRTκ usually has good power to detect mutational bias. Finally, with a recombination rate that is typical for Drosophila, LRTγ has good power to detect weak selection acting on synonymous sites. These results suggest that the method should be useful under many different circumstances.     

Methods for Human Demographic Inference Using Haplotype Patterns From Genomewide Single-Nucleotide Polymorphism Data

We propose a novel approximate-likelihood method to fit demographic models to human genomewide single-nucleotide polymorphism (SNP) data. We divide the genome into windows of constant genetic map width and then tabulate the number of distinct haplotypes and the frequency of the most common haplotype for each window. We summarize the data by the genomewide joint distribution of these two statistics—termed the HCN statistic. Coalescent simulations are used to generate the expected HCN statistic for different demographic parameters. The HCN statistic provides additional information for disentangling complex demography beyond statistics based on single-SNP frequencies. Application of our method to simulated data shows it can reliably infer parameters from growth and bottleneck models, even in the presence of recombination hotspots when properly modeled. We also examined how practical problems with genomewide data sets, such as errors in the genetic map, haplotype phase uncertainty, and SNP ascertainment bias, affect our method. Several modifications of our method served to make it robust to these problems. We have applied our method to data collected by Perlegen Sciences and find evidence for a severe population size reduction in northwestern Europe starting 32,500–47,500 years ago.     

Population growth of human Y chromosomes: a study of Y chromosome microsatellites

We use variation at a set of eight human Y chromosome microsatellite loci to investigate the demographic history of the Y chromosome. Instead of assuming a population of constant size, as in most of the previous work on the Y chromosome, we consider a model which permits a period of recent population growth. We show that for most of the populations in our sample this model fits the data far better than a model with no growth. We estimate the demographic parameters of this model for each population and also the time to the most recent common ancestor. Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes and estimate the variance in mutation size simultaneously with the demographic parameters of interest. Our finding of a recent common ancestor (probably in the last 120,000 years), coupled with a strong signal of demographic expansion in all populations, suggests either a recent human expansion from a small ancestral population, or natural selection acting on the Y chromosome.     

Origin and number of founders in an introduced insular primate: estimation from nuclear genetic data

Cynomolgus macaques (Macaca fascicularis) were introduced on the island of Mauritius between 400 and 500 years ago and underwent a strong population expansion after a probable initial founding event. However, in practice, little is known of the geographical origin of the individuals that colonized the island, on how many individuals were introduced, and of whether the following demographic expansion erased any signal of this putative bottleneck. In this study, we asked whether the current nuclear genome of the Mauritius population retained a signature that would allow us to answer these questions. Altogether, 21 polymorphic autosomal and sex-linked microsatellites were surveyed from 81 unrelated Mauritius individuals and 173 individuals from putative geographical sources in Southeast Asia: Java, the Philippines islands and the Indochinese peninsula. We found that (i) the Mauritius population was closer to different populations depending on the markers we used, which suggests a possible mixed origin with Java playing most probably a major role; and (ii) the level of diversity was lower than the other populations but there was no clear and consistent bottleneck signal using either summary statistics or full-likelihood methods. However, summary statistics strongly suggest that Mauritius is not at mutation-drift equilibrium and favours an expansion rather than a bottleneck. This suggests that on a short time scale, population decline followed by growth can be difficult to deduce from genetic data based on mutation-drift theory. We then used a simple Bayesian rejection algorithm to estimate the number of founders under different demographic models (exponential, logistic and logistic with lag) and pure genetic drift. This new method uses current population size estimates and expected heterozygosity of Mauritius and source population(s). Our results indicate that a simple exponential growth is unlikely and that, under the logistic models, the population may have expanded from an initial effective number of individuals of 10-15. The data are also consistent with a logistic growth with different lag values, indicating that we cannot exclude past population fluctuation.