Genetic differences among language families in Europe

We investigated whether 59 allele frequencies and 10 cranial variables differed among speakers of the 12 modern language families in Europe. Although this is a classical analysis of variance design, special techniques had to be developed for the analysis because of spatial autocorrelation of both biological and language data. The method examines pooled sums of squares within language families. These are compared with the same quantities obtained by randomly partitioning the available data points in Europe into internally cohesive subsets representing the same sample sizes for each language family as in the originally observed data. Our results suggest that for numerous genetic systems, population samples differ more among language families than they do within families. These findings are considered in relation to two contrasting models: a model of random spatial differentiation of gene frequencies unrelated to language and a model of aboriginal genetic differences among speakers of different language groups. Our observed findings suggest partial validity of both models.     

Genetic population structure of Italy. II. Physical and cultural barriers to gene flow.

Three approaches were employed to evaluate the relative importance of geographic and linguistic factors in maintaining genetic differentiation of Italian populations as shown by blood groups and erythrocyte and serum markers. Genetic distances are closer to linguistic than to geographic distances. Gene-frequency change across 12 linguistic boundaries is significantly more rapid than at random locations. The zones of sharp genetic variation correspond to physical barriers to gene flow and to boundaries between dialect families, which overlap widely. However, two linguistically differentiated populations appear genetically differentiated despite the absence of physical obstacles to gene flow around them. The Po River is associated with abrupt genetic change only in the area where it corresponds to a dialect boundary. At most loci the genetic population structure seems affected by linguistic rather than geographic factors; exceptions are the systems that were subject to malarial selection in geographically close but linguistically heterogeneous localities. Gene flow appears to homogenize gene frequencies within regions corresponding to dialect families but not between them, leading to the patchy distributions of allele frequencies that were detected in an earlier study.     

Ancient movement patterns determine modern genetic variances in Europe

A summary ethnohistory database on population movements in Europe between 2000 B.C. and A.D. 1970 was related to genetic variances and distances based on 26 genetic systems. For the purposes of these analyses, Europe was divided into 85 terrestrial quadrats measuring 5 degrees x 5 degrees. Counts, stratified by time, were taken of the number of movements out of and into each quadrat (called source and target counts, respectively) and between each pair of quadrats. The source and target counts have distinct and different patterns in Europe and vary significantly over time. Central Europe and the Pontic area have the quadrats with the highest source counts, and the Balkans have the highest target counts. Modern genetic variances per quadrat are significantly correlated with source and target counts, somewhat more prominently with source counts. Genetic distances between pairs of quadrats are correlated strongly with geographic distances and moderately and negatively correlated with the total number of movements between these quadrats. Partial correlations of genetic distances with total number of movements, holding geographic distance constant, are small and mostly nonsignificant. These results are interpreted in light of our knowledge of the history and biology of the populations concerned.     

The detection of non-random patterns of distribution of species along a gradient

Summary A simple method for detecting non-random patterns of distribution of the boundaries of species is described. The method uses transects running across a community, where the number of upper and lower boundaries of species in each quadrat is recorded. The expected number of quadrats containing one or more boundaries can be calculated from the binomial distribution. The mean deviation of observed from expected number of such quadrats, for a set of transects, can be tested for departures from zero. Significant departures greater than zero indicate regular dispersion of boundaries. A mean deviation significantly less than zero indicates clustering of the boundaries. The method is unbiased and thus corrects previously published methods.     

Moving window analysis and riparian boundary delineation on the Northern Plains of Kruger National Park,South Africa

Landscapes commonly comprise of mosaics, patches and boundaries. Riparian boundaries are complex to delineate and characterize, with a multitude of variables available for delineation. Multiple methods exist for boundary delineation such as two-dimensional wombling, constrained classification techniques and discontinuity detection. One method that has proven to be reliable in boundary delineation with one-dimensional transect data is the moving split window (MSW) analysis. This study demonstrates the efficacy of MSW to delineate grass species turnover and environmental boundaries across two geologically dissimilar riparian zones in the Kruger National Park, South Africa. There are few studies that have delineated riparian boundaries of Kruger National Park, and none that have used the MSW analysis. MSW detects significant changes in dissimilarity indices of variables along gradients. Significant shifts in dissimilarity designate boundaries at various spatial scales dictated by window sizes. Significant boundaries emerge by altering window sizes, increasing quadrat width and removing infrequent herbaceous species. By utilizing these three methods, MSW background variance was reduced and riparian and wetland/upland boundaries were sharper and more easily defined.     

Patterns of gene flow inferred from genetic distances in the Mediterranean region.

The analysis of population structure may lead to inferences about demographic phenomena. In particular, regions of sharp genetic differentiation suggest the existence of factors that impaired gene flow and increased the evolutionary role of genetic drift. Here, we present an analysis of a data set of 10 allele frequencies in 39 populations of the Mediterranean region. As a preliminary step, we describe spatial patterns of allele frequencies using spatial autocorrelation analysis. We then construct a network connecting localities and estimate genetic distances along the edges of the network. By applying specific algorithms, we locate on the map the areas of sharpest genetic differentiation, or genetic boundaries. The main boundaries separate the northern and the southern coasts, especially in their western portions; in addition, several localities appear genetically isolated. The comparatively high genetic differentiation across the western Mediterranean, where the sea distances between localities are shorter, strongly suggests that the sea distance by itself can hardly be regarded as a major isolating factor among these populations. On the contrary, the decrease in genetic resemblance between populations of the 2 coasts as one proceeds westward may reflect an increased genetic exchange in the eastern Mediterranean basin or independent human dispersal along the 2 coasts or both.     

The effect of index selection on allele frequencies and future genetic gains when traits are correlated

The application of the selection index in the case of an additive two-trait model in which the genetic effect on each trait is determined by a finite number of loci is examined. Simulation results indicate that the direction of change in the frequency of favourable alleles is not necessarily in the positive direction at all loci when index selection is used as the basis for truncation selection. When the genetic correlation was positive (or favourable with respect to the economic weights), there was little difference (<5%) in genetic gain over 20 generations and no difference in the direction of change in allele frequencies or genetic correlation whether or not updated values for the genetic (co)variances were used in constructing the selection index. However, when the genetic correlation was negative or unfavourable, the effect of using genetic parameters which were not updated had unexpected effects on the allele frequencies and genetic correlation and reduced the genetic gain by a greater amount (< 12%).     

Population distribution of the methionine allele at the PRNP codon 129 polymorphism in Europe and the Middle East

Methionine homozygosity at codon 129 of the prion protein gene is a risk factor for Creutzfeldt-Jakob disease. Knowledge of M129V polymorphism in normal populations may contribute to a better understanding of prion diseases. M129V polymorphism was studied in 2201 normal subjects, originating from 15 populations from Europe and the Middle East. Mean heterozygosity in these populations is 38.9%, and there is some significant geographic heterogeneity between them. A comparison of M129 allele frequencies in these 15 populations to those already published for 8 European countries plus Turkey shows significant correlations with both latitude (r = -0.77) and longitude (r = 0.69). The geographic map of methionine allele frequencies indicates an east-west gradient of decreasing methionine allele values from the Middle East to Western Europe.     

岷江上游景观边界网络格局分析

格局与过程是景观生态学研究的核心内容,与基于斑块的研究相比,基于边界的研究是格局与过程研究的一个新的切入点.以岷江上游地区为例,基于TM影像数据,应用RS、GIS、Fragstats等软件,选用边界长度、边界密度、边界海拔、结点数量、网眼大小、网络连接度等指数,研究了景观边界网络格局的变化,并分析了与之相关的生态过程.结果表明,在1974～2000年期间,由于人为干扰强度的加剧,岷江上游景观边界网络结构变得更加复杂,在早期以边界长度增加为主,网络连接度变大,在后期以边界数量和结点数量增加为主,网络连接度变小;森林景观与低坡位景观类型间的边界减少,森林下线上移;农田与林地的边界在早期增加,1986年后减少,而与灌木林地的边界持续增加;随着林地的蚀退,森林结构变得简单化,而农田、灌木林地、草地等景观类型的结构变得更加复杂.     

The Use of Multiple Markers in a Bayesian Method for Mapping Quantitative Trait Loci

Information on multiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitative trait loci (QTL). Bayesian parameter estimation and hypothesis testing were implemented via Markov chain Monte Carlo algorithms. Variables sampled were the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage or nonlinkage, and the parameters. The parameter vector included allele frequencies at the markers and the QTL, map distances of the markers and the QTL, QTL substitution effect, and polygenic and residual variances. The criterion for QTL detection was the marginal posterior probability of a QTL being located on the chromosome carrying the markers. The method was evaluated empirically by analyzing simulated granddaughter designs consisting of 2000 sons, 20 related sires, and their ancestors.     

Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms

A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.     

The Ecological Genetics of Introduced Populations of the Giant Toad BUFO MARINUS. II. Effective Population Size

The allele frequencies are described at ten polymorphic enzyme loci (of a total of 22 loci sampled) in 15 populations of the neotropical giant toad, Bufo marinus, introduced to Hawaii and Australia in the 1930s. The history of establishment of the ten populations is described and used as a framework for the analysis of allele frequency variances. The variances are used to determine the effective sizes of the populations. The estimates obtained (390 and 346) are reasonably precise, homogeneous between localities and much smaller than estimates of neighborhood size obtained previously using ecological methods. This discrepancy is discussed, and it is concluded that the estimates obtained here using genetic methods are the more reliable.     

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.     

Population genetics of the Malm? polymorphism of coagulation factor IX.

The distribution of 1,198 Malm? alleles was examined in 822 men from 16 indigenous populations and 188 women from 7 of the ethnic groups. Subjects were from several European countries, the Mediterranean, East Asia, and the USA (Anglo- and African-Americans). The frequencies of the rarer (Malm? B) allele were approximately equal across Europe, the highest frequencies (0.36) being in the French and Anglo-Americans; no population was observed with clearly the highest frequency. They diminished slightly at moderate distances from Europe (Tunisia, Ethiopia) and greatly at longer distances (East Asia and West Africa). In Orientals, the frequencies ranged from 0.07 (East Indians) to 0.03 (the Chinese) and from 0.0 to 0.15 in African-Americans. Assuming selective neutrality, the data are consistent with the European origin of the 'B' allele when the population was small and outward spread.     

Transect-based patch size frequency analysis

Abstract. A new, transect-based patch size detection method for species pattern is proposed which improves results obtained with methods described earlier. The method was tested on an extensive artificial data set together with three of the existing methods considered best: Two and Three Term Local Quadrat Variance (T2LQV and T3LQV) and New Local Variance (NLV). The TLQV methods recovered only some of the existing patterns and were heavily dependent on inter-patch distances, whereas NLV almost always produced curves with oscillations. In addition a significance test is proposed, while such a test is seldom found in the earlier methods. Our method, PASFRAN, determines the frequencies of runs with 1, 2, 3, etc. quadrats containing a certain species and compares those with frequencies based on Monte Carlo simulated random configurations. The comparison is performed for each run length and the significance of the deviation between observed and expected frequencies can be calculated on the basis of a large number of simulations. Because this approach may be considered a case of multiple testing, a Bonferroni correction on the significance level was applied. The method can also be used for the detection of inter-patch distances. In addition, run lengths can be grouped and the test can be applied to the frequencies of combinations of run lengths. The method can detect dominance patches when quantitative data on the occurrence of plant species are available. In the same way, it can detect multi-species patterns using sample scores from an ordination analysis such as correspondence analysis. An extension towards composite, higher-order patterns is under investigation. The new method appeared to be effective in recovering artificial patterns, while it is not influenced by the relative values of patch size and inter-patch distance. When applied to the distribution of cow dung patches and certain plant species along a transect of 500 quadrats of 10 cm × 10 cm in an alvar limestone grassland, it produced straightforward and realistic results as compared with other methods and field impressions.     

Association analysis of population-based quantitative trait data: an assessment of ANOVA

The classical analysis of variance (ANOVA) compares the means of different groups under the assumption that the variances within each of the groups are equal. However, for genetic studies of complex disorders, it is not reasonable to assume that variance of a quantitative trait within each genotype at the trait locus will be equal. Thus, the use of ANOVA may lead to misleading association inferences. In this article, we perform a simulation-based study to assess the rate of false positives and the power of ANOVA under various probability distributions of the quantitative trait and different genetic parameters such as allele frequencies and coefficient of linkage disequilibrium.     

A gene frequency model for QTL mapping using Bayesian inference

Background

Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR).

Results

To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.

Conclusions

In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.     

Monitoring vegetation change caused by trampling: a study in the Cairngorms,Scotland

Summary

A study was made in the Cairngorms, Scotland to make recommendations for a monitoring scheme capable of detecting changes in the vegetation caused by recreational pressure following the development of a funicular railway. Four methods were used in field trials to assess percentage cover of plant species and gravel, rock and bare ground, where appropriate, in two vegetation types (open and closed). The methods used were visual estimates in 50 × 40 cm quadrats (Q), the mean of visual estimates in twenty 10 × 10 cm sub-quadrats of the 50 × 40 cm quadrats (Q₂₀), a modified point intercept method (RL) and photography. Variances between observers and between-quadrats were estimated for the different methods. The sampling design for detecting change was based on a model of variance, constructed from field trial data.

Between-observer and between-quadrat variances were related to mean percentage cover and approximated to a binomial distribution. The between-quadrat variance was larger than observer variance. The Q₂₀ method achieved appreciably better precision than the other methods. Analysis of half of the 10 × 10 cmsub-quadrats (1/2Q₂₀) selected in a checker board design achieved a relative efficiency of 78% compared with the Q₂₀. This result suggests that comparable precision to the Q₂₀ method could be achieved by choosing about 14 sub-quadrats in a larger quadrat, thus saving some time. Variation between quadrats also suggested that the Q₂₀ method was the one of choice for maximising precision. The precision of the photographic method was based on fewer data points, so is less accurate than other estimates.

Minimum sample sizes were estimated for detecting a 10% relative change of a species in open vegetation with 30% cover (i.e. a change from 30% to <27 or to >33% cover). With a 10 % Type II error rate and 5 % Type I error rate the minimum sample sizes were 47 quadrats for Q, 18 for Q ₂₀, 43 for RL, and 23 for the means of ten 10 × 10 cm sub-quadrats in open vegetation.

The most time-efficient field recording appeared to be the use of Q despite the required sample size being 2.6 times higher than that of Q₂₀. The far lower time requirement per quadrat, however, compensated for the higher numbers. The number of quadrats would depend on the specified change in percentage cover and on the statistical significance level used. For example, to detect a 10% absolute change in cover (i.e. from 30% to either <20 % or >40 % cover) at 95 % probability the net effective recording time is estimated at 5 h per vegetation type while to detect a 5 % change at 99 % probability would require c. 25 h. Larger samples may be required for other species or for species with a low initial cover.     

Detecting adaptive evolution based on association with ecological gradients: Orientation matters!

Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.     

Quantifying selection acting on a complex trait using allele frequency time series data

When selection is acting on a large genetically diverse population, beneficial alleles increase in frequency. This fact can be used to map quantitative trait loci by sequencing the pooled DNA from the population at consecutive time points and observing allele frequency changes. Here, we present a population genetic method to analyze time series data of allele frequencies from such an experiment. Beginning with a range of proposed evolutionary scenarios, the method measures the consistency of each with the observed frequency changes. Evolutionary theory is utilized to formulate equations of motion for the allele frequencies, following which likelihoods for having observed the sequencing data under each scenario are derived. Comparison of these likelihoods gives an insight into the prevailing dynamics of the system under study. We illustrate the method by quantifying selective effects from an experiment, in which two phenotypically different yeast strains were first crossed and then propagated under heat stress (Parts L, Cubillos FA, Warringer J, et al. [14 co-authors]. 2011. Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res). From these data, we discover that about 6% of polymorphic sites evolve nonneutrally under heat stress conditions, either because of their linkage to beneficial (driver) alleles or because they are drivers themselves. We further identify 44 genomic regions containing one or more candidate driver alleles, quantify their apparent selective advantage, obtain estimates of recombination rates within the regions, and show that the dynamics of the drivers display a strong signature of selection going beyond additive models. Our approach is applicable to study adaptation in a range of systems under different evolutionary pressures.