Population size estimation of men who have sex with men through the network scale-up method in Japan

Background

Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan.

Methods and Findings

An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM.

Conclusions

The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods.     

Evaluation of two methods to estimate and monitor bird populations

Background

Effective management depends upon accurately estimating trends in abundance of bird populations over time, and in some cases estimating abundance. Two population estimation methods, double observer (DO) and double sampling (DS), have been advocated for avian population studies and the relative merits and short-comings of these methods remain an area of debate.

Methodology/Principal Findings

We used simulations to evaluate the performances of these two population estimation methods under a range of realistic scenarios. For three hypothetical populations with different levels of clustering, we generated DO and DS population size estimates for a range of detection probabilities and survey proportions. Population estimates for both methods were centered on the true population size for all levels of population clustering and survey proportions when detection probabilities were greater than 20%. The DO method underestimated the population at detection probabilities less than 30% whereas the DS method remained essentially unbiased. The coverage probability of 95% confidence intervals for population estimates was slightly less than the nominal level for the DS method but was substantially below the nominal level for the DO method at high detection probabilities. Differences in observer detection probabilities did not affect the accuracy and precision of population estimates of the DO method. Population estimates for the DS method remained unbiased as the proportion of units intensively surveyed changed, but the variance of the estimates decreased with increasing proportion intensively surveyed.

Conclusions/Significance

The DO and DS methods can be applied in many different settings and our evaluations provide important information on the performance of these two methods that can assist researchers in selecting the method most appropriate for their particular needs.     

Estimation of coalescence probabilities and population divergence times from SNP data

We present a method called the G(A|B) method for estimating coalescence probabilities within population lineages from genome sequences when one individual is sampled from each population. Population divergence times can be estimated from these coalescence probabilities if additional assumptions about the history of population sizes are made. Our method is based on a method presented by Rasmussen et al. (2014) to test whether an archaic genome is from a population directly ancestral to a present-day population. The G(A|B) method does not require distinguishing ancestral from derived alleles or assumptions about demographic history before population divergence. We discuss the relationship of our method to two similar methods, one introduced by Green et al. (2010) and called the F(A|B) method and the other introduced by Schlebusch et al. (2017) and called the TT method. When our method is applied to individuals from three or more populations, it provides a test of whether the population history is treelike because coalescence probabilities are additive on a tree. We illustrate the use of our method by applying it to three high-coverage archaic genomes, two Neanderthals (Vindija and Altai) and a Denisovan.Subject terms: Rare variants, Evolutionary genetics

One of the goals of population genetics is to estimate the divergence time of isolated populations. We will review several methods that have been proposed and present a new method that is closely related to two existing methods. We will emphasize the assumptions made when using different methods. It will be useful to make the distinction between estimating coalescence probabilities within populations and estimating population divergence times. We will also introduce a test for a treelike population history based on our method.For distantly related populations, the numbers of mutational differences between sequences indicate relative times of divergence. Relative times are converted to absolute times by assuming a mutation rate. This method traces to Zuckerkandl and Pauling (1962, 1965) and has been used and refined extensively. This class of methods estimates genomic divergence times. Using it to estimate population or species divergence times assumes that those times are so large that the difference between them can be ignored.For recently diverged populations, the numbers of mutational differences probably do not provide a reliable estimate of population divergence times both because there may be too few mutations that differentiate populations and because the difference between the genomic and population divergence times may be substantial. To overcome this problem, Green et al. (2010) (in Supplement 14) introduced a method that accounts for the difference between genomic and population divergence. This method was used in later papers from the same group (Meyer et al. 2012; Prüfer et al. 2014, 2017).The Green et al. (2010) method is applicable when one genome is sampled from each of two populations. It depends on the statistic F(A|B), which is the fraction of sites in population A that carry the derived allele when that site is heterozygous in population B. Green et al. (2010) showed by simulation that the expectation of F(A|B) decreases roughly exponentially with the separation time of A and B. The rate of decrease depends on the history of population sizes both in B and in the population ancestral to A and B. Green et al. (2010) estimated population divergence times by interpolating their simulation results.More recently, Schlebusch et al. (2017), in Section 9.1 of their supplementary materials, introduced a similar method, called the TT method. Their method is based on analytic expressions for the configuration probabilities of SNPs that are polymorphic in the two populations. The TT method assumes that ancestral and derived alleles can be distinguished and the population before divergence was of constant size. The TT method is developed and elaborated on by Sjödin et al. (2020).In the present paper, we present a new method that is closely related to the F(A|B) and TT methods. We call it the G(A|B) method to emphasize its similarity to F(A|B). Our method is based on a method presented by Rasmussen et al. (2014) to test whether an ancient DNA sequence is from a population directly ancestral to a present-day population. We will show that our method provides a way to test whether the history of three or more populations is accurately represented by a population tree even if the demographic histories of those populations are not known.     

Bayesian analysis of genetic differentiation between populations

We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.     

Maximum Likelihood Estimation of Genetic Parameters in Cell Populations

In this paper we consider a cell population such as bacteria consisting of two types of cells, mutant and nonmutant. Under the mutation and homogeneous pure birth processes, this paper derives a maximum likelihood estimation procedure for estimating mutation rate and birth rate. The method is applied to Newcombe's data; further some Monte Carlo studies are generated. The numerical results indicate that the method is quite efficient for estimating genetic parameters in cell populations.     

Estimation of quantitative genetic parameters using marker-inferred relatedness in Japanese flounder: a case study of upward bias

Marker-based methods for estimating heritability have been proposed as an effective means to study quantitative traits in long-lived organisms and natural populations. However, practical examinations to evaluate the usefulness and robustness of a regression method are limited. Using several quantitative traits of Japanese flounder Paralichthys olivaceus, the present study examined the influence of relatedness estimator and population structure on the estimation of heritability and genetic correlation under a regression method with 7 microsatellite loci. Significant heritability and genetic correlation were detected for several quantitative traits in 2 laboratory populations but not in a natural population. In the laboratory populations, upward bias in heritability appeared depending on the relatedness estimators and the populations. Upward bias in heritability increased with decreasing the actual variance of relatedness, suggesting that the estimates of heritability under the regression method tend to be overestimated due to the underestimation of the actual variance of relatedness. Therefore, relationship structure and precise estimation of relatedness are critical for applying this method.     

Estimating relative fitness in asexually reproducing plant pathogen populations

Summary A mathematical model is presented and analysed to find the conditions under which changes in gene frequencies can be used in asexually reproducing populations for estimating fitness of single genes, for example, for estimating the fitnesses of unnecessary virulence genes relative to their corresponding avirulence genes. It is concluded that the underlying distribution of relative fitness of clones (genotypes) has to be unimodal and that many populations consisting of a mixture of distinguishable clones then provide the best experimental data for estimating relative fitness of single genes. An improved statistical test procedure, i.e. generalized logistic regression, is suggested for analysing changes in gene frequencies in population experiments with a mixture of distinguishable clones. A population study of Erysiphe graminis f.sp. hordei (Klug-Andersen 1980) provides data to illustrate the procedure in the case where the population consists of a large number of genotypes. A bimodal distribution of genotypes possessing the virulence gene is indicated here.     

Multi-list methods using incomplete lists in closed populations

Multi-list methods have become a common application of capture-recapture methodology to estimate the size of human populations, and have been successfully applied to estimating prevalence of diabetes, human immunodeficiency virus (HIV), and drug abuse. A key assumption in multi-list methods is that individuals have a unique "tag" that allows them to be matched across all lists. This article develops multi-list methodology that relaxes the assumption of a single tag common to all lists. Estimates are found using estimating functions. An example illustrates its application for estimating the prevalence of diabetes, and a simulation study investigates conditions under which the methodology is robust to different list and population sizes.     

Moment estimation of population diversity and genetic distance from data on recessive markers

A moment-based method for estimating a measure of population diversity, theta or Wright's FST, is given for dominant markers such as amplified fragment length polymorphisms (AFLPs) or RAPDs in noninbred populations. Basic assumptions are that there is random mating, Hardy-Weinberg equilibrium, linkage equilibrium, no mutation from common ancestor and equally distant populations. It is based on the variances between and within populations of genotype frequencies, whereas previously moment methods for dominant markers have been indirect in that they have been based on first estimating allele frequencies and then using the variances of those frequencies. The use of genotype frequencies directly appears to be more robust. Approximate sampling errors of the estimates are given. Methods are extended to estimate genetic distances and their sampling errors. The AFLP data from samples of breeds of pig are used for illustration.     

New method of estimating inbreeding in large semi-isolated populations with application to historic Britain

The purpose of this paper is to introduce a new method of estimating inbreeding in large, relatively isolated, populations over historic times, to demonstrate its application, and indicate some of its limitations and future developments. The method is based on the "paradox" of genealogy, and requires only that the variation of the population size be known, at least reasonably well, over an extended historic period. In this study a method has been developed to model this "paradox" which allows an estimation of the minimum level of inbreeding necessary for a given population curve in terms of values of Pearl's coefficients for each generation. As an example, the method is applied to the population of Britain. It is found that the frequency of siblings occurring in the same generation of a pedigree varies with the population size according to the Fermi-Dirac equation of statistical physics. The effect of introducing a single known estimate of inbreeding into the model is to make the otherwise diverse results for both the actual numbers of ancestors in a generation and the corresponding coefficients of inbreeding to converge.     

A Multilevel Model for Continuous Time Population Estimation

Summary .  Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation.     

Use of known Coefficients of Variation in the Estimation of the Common mean of two Normal Populations

The problem of estimating the common mean of two normal populations N(?, a1?2) and N(?, a2?2) where the coefficients of variation of two populations respectively, are known constants, on the basis of two independent random samples, one from each population, is considered. The minimum mean square estimator is proposed. It is also shown that the proposed estimator is Best Asymptotic Normal (BAN) estimator. It is pointed out that the result can be generalized to k population problem. It is remarked that the same method works, also for the problem of estimating the common standard deviation of k normal populations when coefficients of variation are known.     

Overestimates of maternity and population growth rates in multi-annual breeders

There has been limited attention to estimating maternity rate because it appears to be relatively simple. However, when used for multi-annual breeder species, such as the largest carnivores, the most common estimators introduce an upward bias by excluding unproductive females. Using a simulated dataset based on published data, we compare the accuracy of maternity estimates derived from standard methods against estimates derived from an alternative method. We show that standard methods overestimate maternity rates in the presence of unsuccessful pregnancies. Importantly, population growth rates derived from a matrix model parameterized with the biased estimates may indicate increasing populations although the populations are stable or even declining. We recommend the abandonment of the biased standard methods and to instead use the unbiased alternative method for population projections and assessments of population viability.     

Estimating divergence times

A statistical method of estimating population splitting times is developed in this paper. We consider three populations, with an assumed known tree topology for their phylogenetic tree. From simulation studies, we find that the method of moments performs very well, in particular for both large number of loci and divergence times, in estimating genetic divergence times. The bias decreases as the number of loci increases. The maximum likelihood method proves to be a good method for constructing an unknown phylogenetic tree, in particular for large divergence times.     

Global analysis of regional differences in craniometric diversity and population substructure.

Estimates of genetic diversity in major geographic regions are frequently made by pooling all individuals into regional aggregates. This method can potentially bias results if there are differences in population substructure within regions, since increased variation among local populations could inflate regional diversity. A preferred method of estimating regional diversity is to compute the mean diversity within local populations. Both methods are applied to a global sample of craniometric data consisting of 57 measurements taken on 1734 crania from 18 local populations in six geographic regions: sub-Saharan Africa, Europe, East Asia, Australasia, Polynesia, and the Americas. Each region is represented by three local populations. Both methods for estimating regional diversity show sub-Saharan Africa to have the highest levels of phenotypic variation, consistent with many genetic studies. Polynesia and the Americas both show high levels of regional diversity when regional aggregates are used, but the lowest mean local population diversity. Regional estimates of F(ST) made using quantitative genetic methods show that both Polynesia and the Americas also have the highest levels of differentiation among local populations, which inflates regional diversity. Regional differences in F(ST) are directly related to the geographic dispersion of samples within each region; higher F(ST) values occur when the local populations are geographically dispersed. These results show that geographic sampling can affect results, and suggest caution in making inferences regarding regional diversity when population substructure is ignored.     

Estimating population divergence time and phylogeny from single-nucleotide polymorphisms data with outgroup ascertainment bias

The inference of population divergence times and branching patterns is of fundamental importance in many population genetic analyses. Many methods have been developed for estimating population divergence times, and recently, there has been particular attention towards genome-wide single-nucleotide polymorphisms (SNP) data. However, most SNP data have been affected by an ascertainment bias caused by the SNP selection and discovery protocols. Here, we present a modification of an existing maximum likelihood method that will allow approximately unbiased inferences when ascertainment is based on a set of outgroup populations. We also present a method for estimating trees from the asymmetric dissimilarity measures arising from pairwise divergence time estimation in population genetics. We evaluate the methods by simulations and by applying them to a large SNP data set of seven East Asian populations.     

Sequencing of pooled DNA samples (Pool-Seq) uncovers complex dynamics of transposable element insertions in Drosophila melanogaster

Transposable elements (TEs) are mobile genetic elements that parasitize genomes by semi-autonomously increasing their own copy number within the host genome. While TEs are important for genome evolution, appropriate methods for performing unbiased genome-wide surveys of TE variation in natural populations have been lacking. Here, we describe a novel and cost-effective approach for estimating population frequencies of TE insertions using paired-end Illumina reads from a pooled population sample. Importantly, the method treats insertions present in and absent from the reference genome identically, allowing unbiased TE population frequency estimates. We apply this method to data from a natural Drosophila melanogaster population from Portugal. Consistent with previous reports, we show that low recombining genomic regions harbor more TE insertions and maintain insertions at higher frequencies than do high recombining regions. We conservatively estimate that there are almost twice as many "novel" TE insertion sites as sites known from the reference sequence in our population sample (6,824 novel versus 3,639 reference sites, with on average a 31-fold coverage per insertion site). Different families of transposable elements show large differences in their insertion densities and population frequencies. Our analyses suggest that the history of TE activity significantly contributes to this pattern, with recently active families segregating at lower frequencies than those active in the more distant past. Finally, using our high-resolution TE abundance measurements, we identified 13 candidate positively selected TE insertions based on their high population frequencies and on low Tajima's D values in their neighborhoods.     

Finding confidence limits on population growth rates

Recent concern with the survival of endangered species has renewed interest in estimating the growth rates of natural populations. Estimates of population growth rate are subject to uncertainties because of both sampling and experimental errors incurred when estimating rates of fecundity and survivorship. In recent years, a variety of methods have been proposed for placing confidence limits on estimated growth rates. The commonly used analytical approximation assumes that errors are relatively small. There are several computer-intensive methods, including methods based on jackknife and bootsrap procedures, that test the robustness of that approximation. In addition, several computer simulations of hypothetical populations have led to some generalizations about the performance of different methods. In general, it is possible to find confidence intervals for estimates of population growth rates but the appropriate method for doing so depends on the kind of data available and on the magnitude and correlation structure of the errors.     

Distinguishing migration from isolation: a Markov chain Monte Carlo approach

A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).     

Namesakes or relatives? Approaches to investigating the relationship between Y chromosomal haplogroups and surnames

Population genetics successfully applies surnames as quasi-genetic markers when estimating similarity between populations and calculating a measure of random inbreeding. These calculations are based on an isonomy coefficient which assumes that every surname is monophyletic: that it originated from single common ancestor and all namesakes are therefore relatives. On the other hand, there is a general opinion that a typical Russian surname is polyphyletic: it originated multiple times and most namesakes are therefore not related to each other. Combined studies of Y chromosomes and surnames now allow us to address this issue. In this study, we discuss approaches for statistical evaluation of Y chromosomal haplogroup frequencies in groups of people bearing the same surname (namesakes). We propose an 'Index of Accumulated Haplogroup Frequency', which allows for errors due to random (artifactual) effects increasing a haplogroup frequency in a group of namesakes by subtracting the population frequency of this haplogroup. This population frequency is calculated as the weighted average of the frequencies of this haplogroup in the populations that the carriers of this surname come from. Fom the total sample (comprising 1244 persons from 13 populations of the historical Russian area) we chose 123 persons carrying 14 surnames which were the most frequent in the total sample. Haplogroup frequencies in these 14 "surname" groups were compared with the respective 14 "population" control groups compiled from the total sample as described above. We found that even these widespread surnames exhibit non-random accumulation of specific Y chromosomal haplogroups. More detailed analyses of the relationships between namesakes could be carried out using Y-STR haplotypes rather than Y-SNP haplogroups, and will be the subject of a future study.