A model for genetic relationship in areally continous plant populations.

Representations are based on plant populations, continuously distributed over their habitats according to specified density patterns. Migration of genetic material takes place via pollen and seed dispersal. Monoecious plants with arbitrary rates of self-fertilization and dioecious plants are considered. The model was constructed with the intention of determining coefficients of inbreeding and kinship for all locations within the seed population after its dispersal over the habitat, assuming the respective genetic relationships of the parental generation to be known. To display the consequences of single components hidden in the general result, the following specifications have been treated: finite population size combined with random dispersal of seed, equilibrium states for hypothetically infinite population size with “limited” dispersal of pollen and seed, random dispersal of pollen, and random dispersal of seed.     

Inequality constrained estimation of genetic parameters

Summary A method is presented for computing estimates of genetic parameters under linear inequality constraints such that solutions are within theoretical limits. The method produces biased estimators, yet a small scale numerical study, also presented, shows that the inequality constrained estimators have a small mean squared error of prediction than the best of unbiased estimators. The increase in efficiency of estimation is particularly useful for traits where heritability is near the boundary values of zero or one.     

Power studies in the estimation of genetic parameters and the localization of quantitative trait loci for backcross and doubled haploid populations

A statistical model for doubled haploids and backcrosses based on the interval-mapping methodology has been used to carry out power studies to investigate the effects of different experimental designs, heritabilities of the quantitative trait, and types of gene action, using two test statistics, the F of Fisher-Snedecor and the LOD score. The doubled haploid experimental design is more powerful than backcrosses while keeping actual type I errors similar to nominal ones. For the doubled haploid design, individual QTLs, showing heritabilities as low as 5% were detected in about 90% of the cases using only 250 individuals. The power to detect a given QTL is related to its contribution to the heritability of the trait. For a given nominal type I error, tests using F values are more powerful than with LOD scores. It seems that more conservative levels should be used for the LOD score in order to increase the power and obtain type I errors similar to nominal ones.     

Multilocus estimation of genetic structure within populations

Spatial structure of genetic variation within populations is well measured by statistics based on the distribution of pairs of individual genotypes, and various such statistics have been widely used in experimental studies. However, the problem of uncharacterized correlations among statistics for different alleles has limited the applications of multiallelic, multilocus summary measures, since these had unknown sampling distributions. Usually multiple alleles and/or multiple loci are required in order to precisely measure spatial structures, and to provide precise indirect estimates of the amount of dispersal in samples of reasonable size. This article examines the correlations among pair-wise statistics, including Moran I-statistics and various measures of conditional kinship, for different alleles of a locus. First the correlations are mathematically derived for random spatial distributions, which allow averages over alleles and loci to be used as more powerful yet exact test statistics for the null hypothesis. Then extensive computer simulations are conducted to examine the correlations among values for different alleles under isolation by distance processes. For loci with more than three alleles, the results show that the correlations are remarkably and perhaps surprisingly small, establishing the principle that then alleles behave as nearly independent realizations of space-time stochastic processes. The results also show that the correlations are largely robust with respect to the degree of spatial structure, and they can be used in a straightforward manner to form confidence intervals for averages. The results allow a precise connection between observations in experimental studies and levels of dispersal in theoretical models.     

A model for genetic relationship in areally continuous plant populations

Representations are based on plant populations, continuously distributed over their habitats according to specified density patterns. Migration of genetic material takes place via pollen and seed dispersal. Monoecious plants with arbitrary rates of self-fertilization and dioecious plants are considered. The model was constructed with the intention of determining coefficients of inbreeding and kinship for all locations within the seed population after its dispersal over the habitat, assuming the respective genetic relationships of the parental generation to be known. To display the consequences of single components hidden in the general result, the following specifications have been treated: finite population size combined with random dispersal of seed, equilibrium states for hypothetically infinite population size with “limited” dispersal of pollen and seed, random dispersal of pollen, and random dispersal of seed.     

Statistical experimental designs for the production of secondary metabolites in plant cell suspension cultures

Statistical experimental designs, also known as the “design of experiments” (DoE) approach, are widely used to improve not only technical processes but also to answer questions in the agricultural, medical and social sciences. Although many articles have been published about the application of DoE in these fields, few studies have addressed the use of DoE in the plant sciences, particularly in the context of plant cell suspension cultures (PCSCs). Compounds derived from PCSCs can be developed as pharmaceuticals, chemical feedstocks and cosmetic ingredients, and statistical experimental designs can be used to improve the productivity of the cells and the yield and/or quality of the target compounds in a cost efficient manner. In this article, we summarize recent findings concerning the application of statistical approaches to improve the performance of PCSCs and discuss the potential future applications of this approach.     

Optimal sampling strategy for estimation of spatial genetic structure in tree populations

Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.     

Comparison of quantitative genetic parameters between two natural populations of a selfing plant species, Medicago truncatula Gaertn.

 In this paper we compare mean values, heritability estimates, coefficient of genetic variation, and genetic correlations among several fitness components of two natural populations of a selfing plant species, Medicago truncatula L. It is shown that the population that had been found most polymorphic for molecular markers in a previous study was also the most variable for quantitative characters. Depending on the traits, the larger heritabilities in this population were due to either larger coefficients of genetic variances or smaller coefficients of environmental variances. Whereas genetic and phenotypic correlation matrices were very similar within each population, they were quite different between populations. In particular, although a positive correlation between age and size at maturity was found in both populations, the correlation between age at maturity and reproductive success was negative in the more variable population (late flowering plant, with a larger size at flowering, produced fewer pods), whereas no correlation was observed in the less variable population. We suggest that while in the less variable population all individuals have a high reproductive effort, several strategies coexist in the more variable population, with some early-flowering genotypes showing a high reproductive effort and other late-flowering genotypes showing a larger competitive ability through increased vegetative growth. Received: 25 June 1996 / Accepted: 11 October 1996     

Cautions on direct gene flow estimation in plant populations

Through simulations we have investigated the statistical properties of two of the main approaches for directly estimating pollen gene flow (m) in plant populations: genotypic exclusion and mating models. When the assumptions about accurately known background pollen pool allelic frequencies are met, both methods provide unbiased results with comparable variances across a range of true m values. However, when presumed allelic frequencies differ from actual ones, which is more likely in research practice, both estimators are biased. We demonstrate that the extent and direction of bias largely depend on the difference (measured as genetic distance) between the presumed and actual pollen pools, and on the degree of genetic differentiation between the local population and the actual background pollen sources. However, one feature of the mating model is its ability to estimate pollen gene flow simultaneously with background pollen pool allelic frequencies. We have found that this approach gives nearly unbiased pollen gene flow estimates, and is practical because it eliminates the necessity of providing independent estimates of background pollen pool allelic frequencies. Violations of the mating model assumptions of random mating within local population affect the precision of the estimates only to a limited degree.     

Mapping the genetic architecture of complex traits in experimental populations

SUMMARY: Understanding how interactions among set of genes affect diverse phenotypes is having a greater impact on biomedical research, agriculture and evolutionary biology. Mapping and characterizing the isolated effects of single quantitative trait locus (QTL) is a first step, but we also need to assemble networks of QTLs and define non-additive interactions (epistasis) together with a host of potential environmental modulators. In this article, we present a full-QTL model with which to explore the genetic architecture of complex trait in multiple environments. Our model includes the effects of multiple QTLs, epistasis, QTL-by-environment interactions and epistasis-by-environment interactions. A new mapping strategy, including marker interval selection, detection of marker interval interactions and genome scans, is used to evaluate putative locations of multiple QTLs and their interactions. All the mapping procedures are performed in the framework of mixed linear model that are flexible to model environmental factors regardless of fix or random effects being assumed. An F-statistic based on Henderson method III is used for hypothesis tests. This method is less computationally greedy than corresponding likelihood ratio test. In each of the mapping procedures, permutation testing is exploited to control for genome-wide false positive rate, and model selection is used to reduce ghost peaks in F-statistic profile. Parameters of the full-QTL model are estimated using a Bayesian method via Gibbs sampling. Monte Carlo simulations help define the reliability and efficiency of the method. Two real-world phenotypes (BXD mouse olfactory bulb weight data and rice yield data) are used as exemplars to demonstrate our methods. AVAILABILITY: A software package is freely available at http://ibi.zju.edu.cn/software/qtlnetwork     

Modified full-sib selection and estimation of genetic parameters

Summary A cycle of full-sib selection is completed in three seasons while that of a modified method is completed in two seasons. In modified full-sib selection, selected families can be recombined and new families generated following a partial-diallel cross. The components of genetic variance can be estimated from the partial-diallel analysis of such families. Thus, in addition to performing selection, genetic parameters can be estimated.     

The effects of a known family-size distribution on the estimation of genetic parameters.

We consider the question: In a segregation analysis, can knowledge of the family-size distribution (FSD) in the population from which a sample is drawn improve the estimators of genetic parameters? In other words, should one incorporate the population FSD into a segregation analysis if one knows it? If so, then under what circumstances? And how much improvement may result? We examine the variance and bias of the maximum likelihood estimators both asymptotically and in finite samples. We consider Poisson and geometric FSDs, as well as a simple two-valued FSD in which all families in the population have either one or two children. We limit our study to a simple genetic model with truncate selection. We find that if the FSD is completely specified, then the asymptotic variance of the estimator may be reduced by as much as 5%-10%, especially when the FSD is heavily skewed toward small families. Results in small samples are less clear-cut. For some of the simple two-valued FSDs, the variance of the estimator in small samples of one- and two-child families may actually be increased slightly when the FSD is included in the analysis. If one knows only the statistical form of the FSD, but not its parameter, then the estimator is improved only minutely. Our study also underlines the fact that results derived from asymptotic maximum likelihood theory do not necessarily hold in small samples. We conclude that in most practical applications it is not worth incorporating the FSD into a segregation analysis. However, this practice may be justified under special circumstances where the FSD is completely specified, without error, and the population consists overwhelmingly of small families.     

Multivariate genetic analysis of the neurodynamic and psychodynamic parameters in human populations

The first attempt at using multivariate genetic analysis to study the neurodynamic and psychodynamic levels of individual organization in Daghestan isolates is described. The absence of close similarity between dendrograms of templates of phenotypical and genotypical correlations is explained by lower heritability of the parameters under study. Interpopulation differences revealed by the multivariate genetic analysis are the result of differences in the genetic and social structures of the populations.