A note on a quantitative genetic approach for modelling of differentiation tasks

Learning capacities and cognitive abilities of farm animals will become more important and they will have an impact on animal welfare and productivity. Scientific examinations of these aspects presuppose genetic models. This paper presents an approach for modelling differentiation tasks in animal learning on a quantitative genetic basis. The approach links the likelihood function, to make a correct decision, with the two-dimensional density function, capable to be to decide right. The proposed model is able to depict several situations in learning processes of populations. Its parameters characterize the theoretical course of the learning process and also the stage of learning which a population has reached. The approach can be used to estimate parameters of a learning process and of the genetic disposition to learn, to estimate breeding values of learning capacity and to fit models on population's learning capability under selection.     

Developmental quantitative genetic models of evolutionary change

Discussions about evolutionary change in developmental processes or morphological structures are predicated on specific quantitative genetic models whose parameters predict whether evolutionary change can occur, its relative rate and direction, and if correlated change will occur in other related and unrelated structures. The appropriate genetic model should reflect the relevant genetical and developmental biology of the organisms, yet be simple enough in its parameters so that deductions can be made and hypotheses tested. As a consequence, the choice of the most appropriate genetic model for polygenically controlled traits is a complex tissue and the eventual choice of model is often a compromise between completeness of the model and computational expediency. Herein, we discuss several developmental quantitative genetic models for the evolution of development and morphology. The models range from the classical direct effects model to complex epigenetic models. Further, we demonstrate the algebraic equivalency of the Cowley and Atchley epigenetic model and Wagner's developmental mapping model. Finally, we propose a new multivariate model for continuous growth trajectories. The relative efficacy of these various models for understanding evolutionary change in developmental and morphological traits is discussed. © 1994 Wiley-Liss, Inc.     

A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach

A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment.     

A biological marker model for predicting disease transitions

For patients with chronic myelogenous leukemia (CML), the effect of elevated blood levels of adenosine deaminase (ADA) is studied as a marker for transitions from stable disease to blast crisis and then to death. Data in the form of snapshots over time, with day, state of disease, and ADA level, are analyzed for 55 patients. A simple three-state Markov model with one-way transition probabilities dependent on ADA is used to determine if the marker has a significant effect on the prediction of changes from stable disease to blast crisis.     

A theoretical quantitative genetic study of negative ecological interactions and extinction times in changing environments

Background  

Rapid human-induced changes in the environment at local, regional and global scales appear to be contributing to population declines and extinctions, resulting in an unprecedented biodiversity crisis. Although in the short term populations can respond ecologically to environmental alterations, in the face of persistent change populations must evolve or become extinct. Existing models of evolution and extinction in changing environments focus only on single species, even though the dynamics of extinction almost certainly depend upon the nature of species interactions.     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

FISH-Flow: a quantitative molecular approach for describing mixed clade communities of Symbiodinium

Our understanding of reef corals and their fate in a changing climate is limited by our ability to monitor the diversity and abundance of the dinoflagellate endosymbionts that sustain them. This study combined two well-known methods in tandem: fluorescent in situ hybridization (FISH) for genotype-specific labeling of Symbiodinium and flow cytometry to quantify the abundance of each symbiont clade in a sample. This technique (FISH-Flow) was developed with cultured Symbiodinium representing four distinct clades (based on large subunit rDNA) and was used to distinguish and quantify these types with high efficiency and few false positives. This technique was also applied to freshly isolated symbionts of Orbicella faveolata and Orbicella annularis. Isolates from acutely bleached coral tissues had significantly lower labeling efficiency; however, isolates from healthy tissue had efficiencies comparable to cultured Symbiodinium trials. RNA degradation in bleaching samples may have interfered with labeling of cells. Nevertheless, we were able to determine that, with and without thermal stress, experimental columns of the coral O. annularis hosted a majority of clade B and B/C symbionts on the top and side of the coral column, respectively. We demonstrated that, for cultured Symbiodinium and Symbiodinium freshly isolated from healthy host tissues, the relative ratio of clades could be accurately determined for clades present at as low as 7 % relative abundance. While this method does not improve upon PCR-based techniques in identifying clades at background levels, FISH-Flow provides a high precision, flexible system for targeting, quantifying and isolating Symbiodinium genotypes of interest.     

A sibling-pair based approach for mapping genetic loci that influence quantitative measures of reading disability

Family and twin studies consistently demonstrate a significant role for genetic factors in the aetiology of the reading disorder dyslexia. However, dyslexia is complex at both the genetic and phenotypic levels, and currently the nature of the core deficit or deficits remains uncertain. Traditional approaches for mapping disease genes, originally developed for single-gene disorders, have limited success when there is not a simple relationship between genotype and phenotype. Recent advances in high-throughput genotyping technology and quantitative statistical methods have made a new approach to identifying genes involved in complex disorders possible. The method involves assessing the genetic similarity of many sibling pairs along the lengths of all their chromosomes and attempting to correlate this similarity with that of their phenotypic scores. We are adopting this approach in an ongoing genome-wide search for genes involved in dyslexia susceptibility, and have already successfully applied the method by replicating results from previous studies suggesting that a quantitative trait locus at 6p21.3 influences reading disability.     

A traveling salesman approach for predicting protein functions

Background  

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways.     

Adaptive evolution in ecological communities

Understanding how natural selection drives evolution is a key challenge in evolutionary biology. Most studies of adaptation focus on how a single environmental factor, such as increased temperature, affects evolution within a single species. The biological relevance of these experiments is limited because nature is infinitely more complex. Most species are embedded within communities containing many species that interact with one another and the physical environment. To understand the evolutionary significance of such ecological complexity, experiments must test the evolutionary impact of interactions among multiple species during adaptation. Here we highlight an experiment that manipulates species composition and tracks evolutionary responses within each species, while testing for the mechanisms by which species interact and adapt to their environment. We also discuss limitations of previous studies of adaptive evolution and emphasize how an experimental evolution approach can circumvent such shortcomings. Understanding how community composition acts as a selective force will improve our ability to predict how species adapt to natural and human-induced environmental change.     

An isoclinal approach to the comparative statics of biological communities

An understanding of the comparative statics of biological communities is important both as a means of explaining the long-term effects of changes in environmental conditions, and as a framework for viewing community time trajectories. A general formulation of community dynamics is presented here which, given full information about a particular community's dynamic behavior, describes the impact of a change in environmental conditions on the community steady state. However, since such full information is often lacking in studies of biological communities, various approaches to partial information analysis of comparative statics are presented and compared, including a generalized protocol for isocline analysis. The suggested isocline protocol is shown to be a useful tool for both full and partial information analyses, as well as for both general and partial equilibrium studies. This work was supported over several years through funding from the International Biological Program, the Oregon State University Sea Grant College Program, the U.S. Environmental Protection Agency, the U.S. Forest Service, the Electric Power Research Institute, and the Northwest and Alaska Fisheries Center.     

A p-Median approach for predicting drug response in tumour cells

Background

The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses.

Results

The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs.

Conclusion

The proposed learning framework represents a promising approach predicting drug response in tumour cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0353-7) contains supplementary material, which is available to authorized users.     

PCR bias in ecological analysis: a case study for quantitative Taq nuclease assays in analyses of microbial communities

Succession of ecotypes, physiologically diverse strains with negligible rRNA sequence divergence, may explain the dominance of small, red-pigmented (phycoerythrin-rich) cyanobacteria in the autotrophic picoplankton of deep lakes (C. Postius and A. Ernst, Arch. Microbiol. 172:69-75, 1999). In order to test this hypothesis, it is necessary to determine the abundance of specific ecotypes or genotypes in a mixed background of phylogenetically similar organisms. In this study, we examined the performance of Taq nuclease assays (TNAs), PCR-based assays in which the amount of an amplicon is monitored by hydrolysis of a labeled oligonucleotide (TaqMan probe) when hybridized to the amplicon. High accuracy and a 7-order detection range made the real-time TNA superior to the corresponding end point technique. However, in samples containing mixtures of homologous target sequences, quantification can be biased due to limited specificity of PCR primers and probe oligonucleotides and due to accumulation of amplicons that are not detected by the TaqMan probe. A decrease in reaction efficiency, which can be recognized by direct monitoring of amplification, provides experimental evidence for the presence of such a problem and emphasizes the need for real-time technology in quantitative PCR. Use of specific primers and probes and control of amplification efficiency allow correct quantification of target DNA in the presence of an up to 10(4)-fold excess of phylogenetically similar DNA and of an up to 10(7)-fold excess of dissimilar DNA.     

A theoretical approach to predicting the success of genetic manipulation of malaria mosquitoes in malaria control

Background  

Mosquitoes that have been genetically modified to better encapsulate the malaria parasite Plasmodium falciparum are being considered as a possible tool in the control of malaria. Hopes for this have been raised with the identification of genes involved in the encapsulation response and with advances in the tools required to transform mosquitoes. However, we have only very little understanding of the conditions that would allow such genes to spread in natural populations.     

Effects of global climate change on the patterns of terrestrial biological communities

Recent paleobiological research has shown that late-Quaternary global warming caused individual species distributions to change along environmental gradients in different directions, at different rates, and over different periods. The individualistic responses of the biota created new community patterns. Individualistic changes can be anticipated in the future and if, as predicted, the rate of warming caused by the 'greenhouse effect' is greater than in past events, then the individualistic responses may be even more profound.     

A search for maximum species abundances in ecological communities under conditional diversity optimization

We study a multispecies community of autotrophic microorganisms which grow in a batch culture regime with several perfectly complementary resources. A basic hypothesis is that a stationary phase of the polyculture corresponds to a maximum diversity under the constraints having the meaning of matter conservation laws. The corresponding conditional extremum problem is studied in detail. It is shown that a unique solution to this problem—a “species structure formula”—adequately describes the experimental data. We prove a number of strict statements concerning the domain of definition and maxima of the obtained solutions. These statements find an adequate interpretation as limitation principles in ecology and in the problems of community structure control.