The Allelic Correlation Structure of Gainj- and Kalam-Speaking People. II. the Genetic Distance between Population Subdivisions

The patterning of allele frequency variability among 18 local groups of Gainj and Kalam speakers of highland Papua New Guinea is investigated using new genetic distance methods. The genetic distances proposed here are obtained by decomposing Sewall Wright's coefficient FST into a set of coefficients corresponding to all pairs of population subdivisions. Two statistical methods are given to estimate these quantities. One method provides estimates weighted by sample sizes, while the other method does not use sample size weighting. Both methods correct for the within-individual and between-individual-within-groups sums of squares. Genetic distances among the Gainj and Kalam subdivisions are analyzed with respect to demographic, geographic, and linguistic variables. We find that a demographic feature, group size, has the greatest demonstrable association with the patterning of genetic distances. The pattern of geographic distances among groups displays a weak congruence with the pattern of genetic distances, and the association of genetic and linguistic diversity is very low. An effect of differences in group size on genetic distances is not surprising, from basic theoretical considerations, but genetic distances have not often been analyzed with respect to these variables in the past. The lack of correspondence between genetic distances and linguistic and geographic differences is an unusual feature that distinguishes the Gainj and Kalam from most other tribal populations.     

On the bias of recombination fractions, Kosambi's and Haldane's distances based on frequencies of gametes

The estimation of recombination frequencies is a crucial step in genetic mapping. For the construction of linkage maps, nonadditive recombination fractions must be transformed into additive map distances. Two of the most commonly used transformations are Kosambi's and Haldane's mapping functions. This paper reports on the calculation of the bias associated with estimation of recombination fractions, Kosambi's distances, and Haldane's distances. I calculated absolute and relative biases numerically for a wide range of recombination fractions and sample sizes. I assumed that the ratio of recombinant gametes to the total number of gametes can be adequately represented by a binomial function. I found that the bias in recombination fraction estimates is negative, i.e., the estimator is an underestimate. However, significant values were only obtained when recombination fractions were large and sample sizes were small. The relevant estimates of recombination fractions were, therefore, nearly unbiased. Haldane's and Kosambi's distances were found to be strongly biased, with positive bias for the most interesting values of recombination fractions and sample sizes. The bias of Kosambi's distance was considerably smaller than the bias of Haldane's distance.     

Do polymorphic loci require large sample sizes to estimate genetic distances?

The coefficient of variation of estimates of three genetic distances (standard genetic distance of Nei, chord distance, FST) was examined with computer simulation to determine if large samples (per population) are necessary to precisely estimate genetic distances at loci with high levels of polymorphism. These simulations showed that loci with high mutation rates produce estimates of genetic distance with lower coefficients of variation than loci with lower mutation rates--without requiring larger sample sizes from each population. In addition, the rate at which increasing sample sizes decreases the coefficient of variation of estimates of genetic distances was shown to be approximately determined by the value of FST between the populations being sampled. When FST was greater than 0.05, sampling fewer than 20 individuals (per population) should be sufficient. When FST was less than 0.01, sampling 100 individuals (per population) or more will be useful.     

Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce

This paper introduces a novel sampling method for obtaining core collections, entitled genetic distance sampling. The method incorporates information about distances between individual accessions into a random sampling procedure. A basic feature of the method is that automatically larger samples are obtained if accessions are further apart and smaller samples if accessions are closer together. Genetic distance sampling can be used in conjunction with predefined stratifications of the accessions. Sample sizes are determined automatically; they depend on the distances between accessions within strata. The method is applied to the collection of cultivated lettuce of the Centre for Genetic Resources, the Netherlands. In this paper, genetic distances between accessions are obtained using AFLP marker data. However, genetic distance sampling can be applied using any measure of genetic distance between accessions. Some properties of genetic distance sampling are discussed.     

Reconstruction of genetic distance matrix

A method for reconstruction of genetic distances' matrix, based on linear combination of physical distances' matrices among populations and mean sizes of the population matrices is proposed. The analogue of genetic distances' matrix obtained correlates with the matrix at the level 0.59. The reconstruction may be used for the populations of about 2-3 neighbour districts. An index xi is introduced, as a constant describing some big regions. Comparison of reconstructed matrix of genetic distances with some well-known matrices of genetic distances is performed.     

A comparison of individual‐based genetic distance metrics for landscape genetics

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual‐based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.     

Evolutionary and statistical properties of three genetic distances

Many genetic distances have been developed to summarize allele frequency differences between populations. I review the evolutionary and statistical properties of three popular genetic distances: DS, DA, and theta;, using computer simulation of two simple evolutionary histories: an isolation model of population divergence and an equilibrium migration model. The effect of effective population size, mutation rate, and mutation mechanism upon the parametric value between pairs of populations in these models explored, and the unique properties of each distance are described. The effect of these evolutionary parameters on study design is also investigated and similar results are found for each genetic distance in each model of evolution: large sample sizes are warranted when populations are relatively genetically similar; and loci with more alleles produce better estimates of genetic distance.     

A simple method of removing the effect of a bottleneck and unequal population sizes on pairwise genetic distances

In this paper, we derive the expectation of two popular genetic distances under a model of pure population fission allowing for unequal population sizes. Under the model, we show that conventional genetic distances are not proportional to the divergence time and generally overestimate it due to unequal genetic drift and to a bottleneck effect at the divergence time. This bias cannot be totally removed even if the present population sizes are known. Instead, we present a method to estimate the divergence times between populations which is based on the average number of nucleotide differences within and between populations. The method simultaneously estimates the divergence time, the ancestral population size and the relative sizes of the derived populations. A simulation study revealed that this method is essentially unbiased and that it leads to better estimates than traditional approaches for a very wide range of parameter values. Simulations also indicated that moderate population growth after divergence has little effect on the estimates of all three estimated parameters. An application of our method to a comparison of humans and chimpanzee mitochondrial DNA diversity revealed that common chimpanzees have a significantly larger female population size than humans.     

A Note on Two‐Sample Tests for Comparing Intra‐Individual Genetic Sequence Diversity between Populations

Summary Gilbert, Rossini, and Shankarappa (2005 , Biometrics 61 , 106‐117) present four U‐statistic based tests to compare genetic diversity between different samples. The proposed tests improved upon previously used methods by accounting for the correlations in the data. We find, however, that the same correlations introduce an unacceptable bias in the sample estimators used for the variance and covariance of the inter‐sequence genetic distances for modest sample sizes. Here, we compute unbiased estimators for these and test the resulting improvement using simulated data. We also show that, contrary to the claims in Gilbert et al., it is not always possible to apply the Welch–Satterthwaite approximate t‐test, and we provide explicit formulas for the degrees of freedom to be used when, on the other hand, such approximation is indeed possible.     

Median-joining networks for inferring intraspecific phylogenies.

Reconstructing phylogenies from intraspecific data (such as human mitochondrial DNA variation) is often a challenging task because of large sample sizes and small genetic distances between individuals. The resulting multitude of plausible trees is best expressed by a network which displays alternative potential evolutionary paths in the form of cycles. We present a method ("median joining" [MJ]) for constructing networks from recombination-free population data that combines features of Kruskal's algorithm for finding minimum spanning trees by favoring short connections, and Farris's maximum-parsimony (MP) heuristic algorithm, which sequentially adds new vertices called "median vectors", except that our MJ method does not resolve ties. The MJ method is hence closely related to the earlier approach of Foulds, Hendy, and Penny for estimating MP trees but can be adjusted to the level of homoplasy by setting a parameter epsilon. Unlike our earlier reduced median (RM) network method, MJ is applicable to multistate characters (e.g., amino acid sequences). An additional feature is the speed of the implemented algorithm: a sample of 800 worldwide mtDNA hypervariable segment I sequences requires less than 3 h on a Pentium 120 PC. The MJ method is demonstrated on a Tibetan mitochondrial DNA RFLP data set.     

Mitochondrial DNA phylogeography and population history of the grey wolf canis lupus

The grey wolf (Canis lupus) and coyote (C. latrans) are highly mobile carnivores that disperse over great distances in search of territories and mates. Previous genetic studies have shown little geographical structure in either species. However, population genetic structure is also influenced by past isolation events and population fluctuations during glacial periods. In this study, control region sequence data from a worldwide sample of grey wolves and a more limited sample of coyotes were analysed. The results suggest that fluctuating population sizes during the late Pleistocene have left a genetic signature on levels of variation in both species. Genealogical measures of nucleotide diversity suggest that historical population sizes were much larger in both species and grey wolves were more numerous than coyotes. Currently, about 300 000 wolves and 7 million coyotes exist. In grey wolves, genetic diversity is greater than that predicted from census population size, reflecting recent historical population declines. By contrast, nucleotide diversity in coyotes is smaller than that predicted by census population size, reflecting a recent population expansion following the extirpation of wolves from much of North America. Both species show little partitioning of haplotypes on continental or regional scales. However, a statistical parsimony analysis indicates local genetic structure that suggests recent restricted gene flow.     

Microsatellite null alleles and estimation of population differentiation

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on F(ST) and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating F(ST) and genetic distances, and we compared this method with a new method proposed here (for F(ST) only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, F(ST) and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of F(ST) and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. F(ST) estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results.     

Genetic variation within two linguistic Amerindian groups: relationship to geography and population size

Nine Carib and eight Tupi groups were studied for a minimum of eight common polymorphic systems and compared in terms of genetic distances using the methods of Nei and Edwards. Two levels of genetic information were distinguished, one with a maximum of 20 loci and another with a maximum of 12 loci considered. The dendrograms produced consistent, reproducible results, independent of the method used, when a minimum of ten polymorphic systems were included in the analysis. Irrespective of the number of systems or the method used, the Tupi showed two to three times higher average interpopulation genetic distances than the Carib groups, which may be due to their lower average population sizes, allowing for the action of genetic drift and/or founder effects, as these two sets of populations do not differ significantly in geographic range, years of contact with non-Indians, or degree of acculturation.     

ESTIMATING GENETIC CORRELATIONS FROM MEASUREMENTS OF FIELD-CAUGHT WATERSTRIDERS

Abstract Lynch (1999) proposed a method for estimation of genetic correlations from phenotypic measurements of individuals for which no pedigree information is available. This method assumes that shared environmental effects do not contribute to the similarity of relatives, and it is expected to perform best when sample sizes are large, many individuals in the sample are paired with close relatives, and heritability of the traits is high. We tested the practicality of this method for field biologists by using it to estimate genetic correlations from measurements of field‐caught waterstriders {Aquarius remigis). Results for sample sizes of less than 100 pairs were often unstable or undefined, and even with more than 500 pairs only half of those correlations that had been found to be significant in standard laboratory experiments were statistically significant in this study. Statistically removing the influence of environmental effects (shared between relatives) weakened the estimates, possibly by removing some of the genetic similarity between relatives. However, the method did generate statistically significant estimates for some genetic correlations. Lynch (1999) anticipated the problems found, and proposed another method that uses estimates of relatedness between members of pairs (from molecular marker data) to improve the estimates of genetic correlations, but that approach has yet to be tested in the field.     

AFLP marker analysis revealing genetic structure of the tree Parapiptadenia rigida (Benth.) Brenan (Leguminosae-Mimosoideae) in the southern Brazilian Tropical Rainforest

Parapiptadenia rigida is a tropical early secondary succession tree characteristic of the Tropical Atlantic Rainforest. This species is of great ecological importance in the recovery of degraded areas. In this study we investigated the variability and population genetic structure of eight populations of P. rigida. Five AFLP primer combinations were used in a sample of 159 individuals representing these eight populations, rendering a total of 126 polymorphic fragments. The averages of percentage of polymorphic loci, gene diversity, and Shannon index were 60.45%, 0.217, and 0.322, respectively. A significant correlation between the population genetic variability and the population sizes was observed. The genetic variability within populations (72.20%) was higher than between these (22.80%). No perfect correlation was observed between geographic and genetic distances, which might be explained by differences in deforestation intensities that occurred in these areas. A dendrogram constructed by the UPGMA method revealed the formation of two clusters, these also confirmed by Bayesian analysis for the number of K cluster. These results show that it is necessary to develop urgent management strategies for the conservation of certain populations of P. rigida, while other populations still preserve reasonably high levels of genetic variability.     

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the ‘true’ cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the ‘true’ costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the ‘true’ scenario (cost distance Mantel correlations), but this ‘true’ scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the ‘true’ scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.     

孙女设计中标记密度对QTL定位精确性的影响

采用蒙特卡罗方法分析了在孙女设计中不同的嫩体结构、性状遗传力、ＱＴＬ效应大小和ＱＴＬ在染色体上的位置中个因素不同水平组合下４种标记密度（标记间隔５ｃＭ,１０ｃＭ,２０ｃＭ、５０ｃＭ对ＱＴＬ定位精确性（以均方误ＭＳＥ为衡量指标）的影响,并从经济学角度探讨了应用于标记辅助选（ＭＡＳ）的ＱＴＬ定位的最佳标记密度。结果表明,一般说来,在各因素水平都较低时,ＭＳＥ随标记密度加大而下降的相对幅度也较 小,反之     

LIAN 3.0: detecting linkage disequilibrium in multilocus data. Linkage Analysis

SUMMARY: LIAN is a program to test the null hypothesis of linkage equilibrium for multilocus data. LIAN incorporates both a Monte Carlo method as well as a novel algebraic method to carry out the hypothesis test. The program further returns the genetic diversity of the sample and the pairwise distances between its members.     

On the biochemical systematics of selected mammalian taxa: empirical comparison of qualitative and quantitative approaches in the evaluation of protein electrophoretic data

Empirical data sets of Artiodactyla (Antilocapridae, Bovidae, Cervidae, Suidae), Carnivora (Mustelidae) and Rodentia (Sciuridae, Cricetidae, Arvicolidae, Muridae), obtained by horizontal starch el electrophoresis of 15–34 isoenzyme sstems, were used to calculate genetic distances and to construct phylogenetic trees by the following methods: Nei's D (corrected for small sample sizes) - UPGMA, FITCH, KITSCH (out of Felsenstein's PHYLIP-package); Rogers -distance - distance-Wanger tree; maximum likelihood approach (cavalli -Sforza -Edwards ); maximum parsimony method (wagner ); Hennigian cladogram. The results were re-examined using the statisticar methods of jackknife and bootstrap. The following problems became apparent and were studied in more detail: inconstancy of molecular evolutionary rate among taxa, non-uniformity of evolutionary rate among isoenzymes, possible convergence of alloenzymes, different evolutionary histories of taxa (radiations/bottlenecks), methodological influences sample sizes / rare alleles, comparability of data sets). The results show, that many branches of the various phylogenetic trees are fairly constant. The ambiguous position of the remaining OTU's is due to insufficient evidence in the primary data rather than to theroperties of cluster algorithms. However, since these problematic cases are also uncertain in phylogenies based on morphological characters and palaeontological results, even an increased data set may not lead to a cyear decision unless additional taxa of crucial importance are examined. Molecular evolutionary rate among taxa seems to be accelerated in some cases, possibly due to random fixation of different alleles during bottlenecks, when a highly polymorpic ancestral form underwent a series of adaptive radiations. Isoenzymes can be divided into groups with different evolutionary rates. Thus, data sets are only comparable with respect to genetic variability and differentiation, when they contain a similar amount of representatives of each of these categories.