Estimation of genetic distance and coefficient of gene diversity from single-probe multilocus DNA fingerprinting data

DNA fingerprinting exhibits multilocus genotypes of individuals, detected by the use of a single multilocus probe. Consequently, population data on DNA fingerprinting do not provide a complete characterization of the genetic variation in terms of allele-frequency distributions, since neither the number of loci nor the locus affiliation of alleles is directly observable. Yet DNA fingerprinting has been proved to be a cost-effective method of detecting hypervariable polymorphisms in several organisms, where the traditional loci fail to detect enough variation for microevolutionary studies. In the present paper we demonstrate that the above-mentioned features of DNA fingerprinting data do not cause any serious problem when they are used in evolutionary studies. Bias-corrected estimators of Nei's standard and minimum genetic distances are derived, and, by an application of this theory to data on seven short tandem repeat loci in three major human populations, it is shown that these modified measures of genetic distances based on DNA fingerprint patterns are quite close to Nei's distances based on locus-specific allele frequencies. Empirical as well as theoretical support of the adequacy of such genetic distances from DNA fingerprinting data is also discussed, and it indicates that the technical limitations of DNA fingerprinting should not deter the use of the method for short-term evolutionary studies.      

A model-based method for identifying species hybrids using multilocus genetic data

We present a statistical method for identifying species hybrids using data on multiple, unlinked markers. The method does not require that allele frequencies be known in the parental species nor that separate, pure samples of the parental species be available. The method is suitable for both markers with fixed allelic differences between the species and markers without fixed differences. The probability model used is one in which parentals and various classes of hybrids (F(1)'s, F(2)'s, and various backcrosses) form a mixture from which the sample is drawn. Using the framework of Bayesian model-based clustering allows us to compute, by Markov chain Monte Carlo, the posterior probability that each individual belongs to each of the distinct hybrid classes. We demonstrate the method on allozyme data from two species of hybridizing trout, as well as on two simulated data sets.     

Statistical analysis of binary data generated from multilocus dominant DNA markers

The use of methodologies such as RAPD and AFLP for studying genetic variation in natural populations is widespread in the ecology community. Because data generated using these methods exhibit dominance, their statistical treatment is less straightforward. Several estimators have been proposed for estimating population genetic parameters, assuming simple random sampling and the Hardy-Weinberg (HW) law. The merits of these estimators remain unclear because no comparative studies of their theoretical properties have been carried out. Furthermore, ascertainment bias has not been explicitly modelled. Here, we present a comparison of a set of candidate estimators of null allele frequency (q), locus-specific heterozygosity (h) and average heterozygosity () in terms of their bias, standard error, and root mean square error (RMSE). For estimating q and h, we show that none of the estimators considered has the least RMSE over the parameter space. Our proposed zero-correction procedure, however, generally leads to estimators with improved RMSE. Assuming a beta model for the distribution of null homozygote proportions, we show how correction for ascertainment bias can be carried out using a linear transform of the sample average of h and the truncated beta-binomial likelihood. Simulation results indicate that the maximum likelihood and empirical Bayes estimator of have negligible bias and similar RMSE. Ascertainment bias in estimators of is most pronounced when the beta distribution is J-shaped and negligible when the latter is inverse J-shaped. The validity of the current findings depends importantly on the HW assumption-a point that we illustrate using data from two published studies.     

A framework for detecting and characterizing genetic background-dependent imprinting effects

Genomic imprinting, where the effects of alleles depend on their parent-of-origin, can be an important component of the genetic architecture of complex traits. Although there has been a rapidly increasing number of studies of genetic architecture that have examined imprinting effects, none have examined whether imprinting effects depend on genetic background. Such effects are critical for the evolution of genomic imprinting because they allow the imprinting state of a locus to evolve as a function of genetic background. Here we develop a two-locus model of epistasis that includes epistatic interactions involving imprinting effects and apply this model to scan the mouse genome for loci that modulate the imprinting effects of quantitative trait loci (QTL). The inclusion of imprinting leads to nine orthogonal forms of epistasis, five of which do not appear in the usual two-locus decomposition of epistasis. Each form represents a change in the imprinting status of one locus across different classes of genotypes at the other locus. Our genome scan identified two different locus pairs that show complex patterns of epistasis, where the imprinting effect at one locus changes across genetic backgrounds at the other locus. Thus, our model provides a framework for the detection of genetic background-dependent imprinting effects that should provide insights into the background dependence and evolution of genomic imprinting. Our application of the model to a genome scan supports this assertion by identifying pairs of loci that show reciprocal changes in their imprinting status as the background provided by the other locus changes.     

Taxonomic status and genetic differentiation of Hyrcanian Castanea based on noncoding chloroplast DNA sequences data

The taxonomy and phylogenetic relationships among Castanea species were investigated using sequence data from the chloroplast trnL-F and trnH-psbA intergenic spacer regions. Samples included Castanea specimens of uncertain taxonomic affinity that were collected in the Hyrcanian forest of northern Iran. The trnL-F data were more informative than trnH-psbA, having seven parsimony-informative sites. A low level of haplotype diversity was detected within Hyrcanian samples and the whole species of the genus Castanea. In the trnL-F dataset, Castanea sativa and Castanea mollissima have unique character states that differentiate them from other species of Castanea. The genus Castanea was recovered as a monophyletic with high to moderate support when inferred from combined trnH-psbA and trnL-F spacer data. Two main lineages received minimal support in the trnL-F analysis, whereas trnH-psbA could not distinguish different species of Castanea from each other. Finally, low levels of haplotype diversity was found within small remnant stands of Castanea in the Hyrcanian forest, indicating that genetic erosion may increase the extinction risk for these valuable trees.     

Genomic imprinting and genetic effects on muscle traits in mice

ABSTRACT: BACKGROUND: Genomic imprinting refers to parent-of-origin dependent gene expression caused by differential DNA methylation of the paternally and maternally derived alleles. Imprinting is increasingly recognized as an important source of variation in complex traits, however, its role in explaining variation in muscle and physiological traits, especially those of commercial value, is largely unknown compared with genetic effects. RESULTS: We investigated both genetic and genomic imprinting effects on key muscle traits in mice from the Berlin Muscle Mouse population, a key model system to study muscle traits. Using a genome scan, we first identified loci with either imprinting or genetic effects on phenotypic variation. Next, we established the proportion of phenotypic variation explained by additive, dominance and imprinted QTL and characterized the patterns of effects. In total, we identified nine QTL, two of which show large imprinting effects on glycogen content and potential, and body weight. Surprisingly, all imprinting patterns were of the bipolar type, in which the two heterozygotes are different from each other but the homozygotes are not. Most QTL had pleiotropic effects and explained up to 40% of phenotypic variance, with individual imprinted loci accounting for 4-5% of variation alone. CONCLUSION: Surprisingly, variation in glycogen content and potential was only modulated by imprinting effects. Further, in contrast to general assumptions, our results show that genomic imprinting can impact physiological traits measured at adult stages and that the expression does not have to follow the patterns of paternal or maternal expression commonly ascribed to imprinting effects.     

Chromosomal inversion polymorphism leads to extensive genetic structure: a multilocus survey in Drosophila subobscura

The adaptive character of inversion polymorphism in Drosophila subobscura is well established. The O(ST) and O(3+4) chromosomal arrangements of this species differ by two overlapping inversions that arose independently on O(3) chromosomes. Nucleotide variation in eight gene regions distributed along inversion O(3) was analyzed in 14 O(ST) and 14 O(3+4) lines. Levels of variation within arrangements were quite similar along the inversion. In addition, we detected (i) extensive genetic differentiation between arrangements in all regions, regardless of their distance to the inversion breakpoints; (ii) strong association between nucleotide variants and chromosomal arrangements; and (iii) high levels of linkage disequilibrium in intralocus and also in interlocus comparisons, extending over distances as great as approximately 4 Mb. These results are not consistent with the higher genetic exchange between chromosomal arrangements expected in the central part of an inversion from double-crossover events. Hence, double crossovers were not produced or, alternatively, recombinant chromosomes were eliminated by natural selection to maintain coadapted gene complexes. If the strong genetic differentiation detected along O(3) extends to other inversions, nucleotide variation would be highly structured not only in D. subobscura, but also in the genome of other species with a rich chromosomal polymorphism.     

Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data

Summary .   In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients.     

Variogram analysis of the spatial genetic structure of continuous populations using multilocus microsatellite data

A geostatistical perspective on spatial genetic structure may explain methodological issues of quantifying spatial genetic structure and suggest new approaches to addressing them. We use a variogram approach to (i) derive a spatial partitioning of molecular variance, gene diversity, and genotypic diversity for microsatellite data under the infinite allele model (IAM) and the stepwise mutation model (SMM), (ii) develop a weighting of sampling units to reflect ploidy levels or multiple sampling of genets, and (iii) show how variograms summarize the spatial genetic structure within a population under isolation-by-distance. The methods are illustrated with data from a population of the epiphytic lichen Lobaria pulmonaria, using six microsatellite markers. Variogram-based analysis not only avoids bias due to the underestimation of population variance in the presence of spatial autocorrelation, but also provides estimates of population genetic diversity and the degree and extent of spatial genetic structure accounting for autocorrelation.     

Modeling population genetic data in autotetraploid species

Allozyme and PCR-based molecular markers have been widely used to investigate genetic diversity and population genetic structure in autotetraploid species. However, an empirical but inaccurate approach was often used to infer marker genotype from the pattern and intensity of gel bands. Obviously, this introduces serious errors in prediction of the marker genotypes and severely biases the data analysis. This article developed a theoretical model to characterize genetic segregation of alleles at genetic marker loci in autotetraploid populations and a novel likelihood-based method to estimate the model parameters. The model properly accounts for segregation complexities due to multiple alleles and double reduction at autotetrasomic loci in natural populations, and the method takes appropriate account of incomplete marker phenotype information with respect to genotype due to multiple-dosage allele segregation at marker loci in tetraploids. The theoretical analyses were validated by making use of a computer simulation study and their utility is demonstrated by analyzing microsatellite marker data collected from two populations of sycamore maple (Acer pseudoplatanus L.), an economically important autotetraploid tree species. Numerical analyses based on simulation data indicate that the model parameters can be adequately estimated and double reduction is detected with good power using reasonable sample size.     

Inference of hazel grouse population structure using multilocus data: a landscape genetic approach

In conservation and management of species it is important to make inferences about gene flow, dispersal and population structure. In this study, we used 613 georeferenced tissue samples from hazel grouse (Bonasa bonasia) where each individual was genotyped at 12 microsatellite loci to make inference on population genetic structure, gene flow and dispersal in northern Sweden. Observed levels of genetic diversity suggest that Swedish hazel grouse do not suffer loss of genetic diversity compared with other grouse species. We found significant F(IS) (deviation from Hardy-Weinberg expectations) over the entire sample using jack-knifed estimators over loci, which is most likely explained by a Wahlund effect. With the use of spatial autocorrelation methods, we detected significant isolation by distance among individuals. Neighbourhood size was estimated in the order of 62-158 individuals corresponding to a dispersal distance of 950-1500 m. Using a spatial statistical model for landscape genetics to infer the number of populations and the spatial location of genetic discontinuities between these populations we found indications that Swedish hazel grouse are divided into a northern and a southern population. We could not find a sharp border between these two populations and none of the observed borders appeared to coincide with any potential geographical barriers.These results imply that gene flow appears somewhat unrestricted in the boreal taiga forests of northern Sweden and that the two populations of hazel grouse in Sweden may be explained by the post-glacial reinvasion history of the Scandinavian Peninsula.     

Analysis of a genetic hitchhiking model, and its application to DNA polymorphism data from Drosophila melanogaster

Begun and Aquadro have demonstrated that levels of nucleotide variation correlate with recombination rate among 20 gene regions from across the genome of Drosophila melanogaster. It has been suggested that this correlation results from genetic hitchhiking associated with the fixation of strongly selected mutants. The hitchhiking process can be described as a series of two-step events. The first step consists of a strongly selected substitution wiping out linked variation in a population; this is followed by a recovery period in which polymorphism can build up via neutral mutations and random genetic drift. Genetic hitchhiking has previously been modeled as a steady-state process driven by recurring selected substitutions. We show here that the characteristic parameter of this steady-state model is alpha v, the product of selection intensity (alpha = 2Ns) and the frequency of beneficial mutations v (where N is population size and s is the selective advantage of the favored allele). We also demonstrate that the steady-state model describes the hitchhiking process adequately, unless the recombination rate is very low. To estimate alpha v, we use the data of DNA sequence variation from 17 D. melanogaster loci from regions of intermediate to high recombination rates. We find that alpha v is likely to be > 1.3 x 10(-8). Additional data are needed to estimate this parameter more precisely. The estimation of alpha v is important, as this parameter determines the shape of the frequency distribution of strongly selected substitutions.      

Improved error bounds for genetic distances from DNA sequences

The genetic distance between two DNA sequences may be measured by the average number of nucleotide substitutions per position that has occurred since the two sequences diverged from a common ancestor. Estimates of this quantity can be derived from Markov models for the substitution process, while the variances are estimated using the delta method and confidence intervals calculated assuming normality. However, when the sampling distribution of the estimator deviates from normality, such intervals will not be accurate. For simple one-parameter models of nucleotide substitution, we propose a transformation of normal confidence intervals, which yields an almost exact approximation to the true confidence intervals of the distance estimators. To calculate confidence intervals for more complicated models, we propose the saddlepoint approximation. A simulation study shows that the saddlepoint-derived confidence intervals are a real improvement over existing methods.     

DNA fingerprinting to detect genetic variation in rice using hypervariable DNA sequences

Theoretical and Applied Genetics - The suitability of miniand microsatellite related DNA sequences capable of detecting multiple loci was investigated for their ability to generate DNA fingerprints...     

DNA conformational effects on the interaction of netropsin with A-tract sequences

The influence of cosolutes and DNA sequence on the interaction of netropsin with three duplexes has been studied by isothermal titration calorimetry. In buffer, netropsin forms two complexes with a net stoichiometry of 1:1 in the minor groove of the oligonucleotide (GCGCGAATTCGCGC)2. One complex has a weaker affinity and is more enthalpically favored relative to the other one, consistent with previous studies [Freyer, M. W., et al. (2006) Biophys. Chem. 126, 186-196]. With the cosolutes betaine and 2-methyl-2,4-pentanediol, the enthalpy and heat capacity changes indicate that the complex with weaker affinity is disfavored relative to the complex with higher affinity. With (CGCGCAATTGCGCG)2, netropsin has one binding mode in buffer, and complex formation is not influenced by the cosolutes. The similarities of the enthalpy and heat capacity changes suggest that netropsin interacts similarly with these two oligonucleotides in the presence of cosolutes. The oligonucleotide (GCGCAAATTTGCGC)2 also forms two complexes with netropsin, and the complex with weaker affinity is again disfavored by the cosolutes. Thus, the interaction of netropsin with these A/T binding sites is influenced both by the bases adjacent to the binding site and by cosolutes. We suggest that these two factors influence the conformation of the minor-groove binding site of DNA.     

Molecular cloning of human satellite DNA sequences and their use in detecting DNA polymorphism

A approximately 400 bp HaeIII human genomic satellite DNA band was cloned into pUC18 to construct a partial library. A fragment of bacteriophage M13 containing a sequence homologous to the human minisatellite core was cloned in pUC18 and was used as a probe to isolate a approximately 350 bp human satellite clone (pTRF5.6) from the partial library. Other clones from this library showed a wide variation in terms of size and hybridization to the pTRF5.6 clone. Human DNA from different individuals was digested with restriction enzymes, Southern transferred and probed with TRF5.6. Individual-specific complex pattern of DNA bands was produced. TRF5.6, therefore, could be useful as a probe for detecting genetic polymorphism.     

Monitoring genetic diversity in tropical trees with multilocus dominant markers

Since no universal codominant markers are currently available, dominant genetic markers, such as amplified fragment length polymorphism (AFLP), are valuable tools for assessing genetic diversity in tropical trees. However, the measurement of genetic diversity (H) with dominant markers depends on the frequency of null homozygotes (Q) and the fixation index (F) of populations. While Q can be estimated for AFLP loci, F is less accessible. Through a modelling approach, we show that the monolocus estimation of genetic diversity is strongly dependent on the value of F, but that the multilocus diversity estimate is surprisingly robust to variations in F. The robustness of the estimate is due to a mechanistic effect of compensation between negative and positive biases of H by different AFLP loci exhibiting contrasting frequency profiles of Q. The robustness was tested across contrasting theoretical frequency profiles of Q and verified for 10 neotropical species. Practical recommendations for the implementation of this analytical method are given for genetic surveys in tropical trees, where such markers are widely applied.