Model fitting and model testing in the method of joint mapping of quantitative trait loci

A previous paper proposed a new method of QTL mapping called joint mapping (JM). Some problems have been found in model fitting and model testing due to the neglect of the correlations among different observations of the dependent variable in this model. The present paper reports a method of solving the problems. The coefficient of correlation between two observations of the dependent variable is derived. A generalized least square (GLS) approach is developed for model fitting and a strategy and procedure of model testing based on a chi-square test is suggested. A simulated example is given. The example shows that the JM method is quite efficient in mapping multiple linked QTLs.     

Non-linear selection response in Drosophila: a strategy for testing the rare-alleles model of quantitative genetic variability

Quantitative genetic theory predicts that variation due to rare alleles at many loci will generate a transient acceleration in the response to directional selection. We have tested this prediction by constructing experimental lines ofDrosophila melanogaster that carry positively selected ethanol resistance alleles at low frequencies, and then subjecting the lines to directional selection for ethanol resistance. Approximately 468,000 files were subjected to artificial selection over 30 generations. The predicted non-linear selection responses were observed in all experimental lines and replicates, on three genetic backgrounds. In contrast, un-selected controls and lines carrying random alleles at low frequencies on the same genetic backgrounds exhibited linear selection responses. These results demonstrate that non-linearities due to rare alleles are detectable and repeatable, provided that experiments are done on a sufficiently large scale. The results suggest that it may be possible to test for rare-alleles as a component of naturally occurring genetic variation by careful examination of selection response curves.     

FRET or no FRET: a quantitative comparison

Fluorescence resonance energy transfer (FRET) is a technique used to measure the interaction between two molecules labeled with two different fluorophores (the donor and the acceptor) by the transfer of energy from the excited donor to the acceptor. In biological applications, this technique has become popular to qualitatively map protein-protein interactions, and in biophysical projects it is used as a quantitative measure for distances between a single donor and acceptor molecule. Numerous approaches can be found in the literature to quantify and map FRET, but the measures they provide are often difficult to interpret. We propose here a quantitative comparison of these methods by using a surface FRET system with controlled amounts of donor and acceptor fluorophores and controlled distances between them. We support the system with a Monte Carlo simulation of FRET, which provides reference values for the FRET efficiency under various experimental conditions. We validate a representative set of FRET efficiencies and indices calculated from the different methods with different experimental settings. Finally, we test their sensitivity and draw conclusions for the preparation of FRET experiments in more complex and less-controlled systems.     

Model building and testing for the change-over design

The two-period cross-over design is discussed within the framework of univariate and multivariate analysis of variance technique; the relations between both procedures are explained. It is shown that all hypotheses of interest can be tested if the design is regarded as a special case of a repeated measurement design. Some features of the n-period change-over design are explained by discussing the model and hypotheses of a three-period design.     

Transient, highly populated, building blocks folding model

Protein folding is a hierarchical event, in which transiently formed local structural elements assemble to yield the native conformation. In principle, multiple paths glide down the energy landscape, but, in practice, only a few of the paths are highly traveled. Here, the literature is reviewed in this light, and, particularly, a hierarchical, building block protein-folding model is presented, putting it in the context of a broad range of experimental and theoretical results published over the past few years. The model is based on two premises: First, although the local building block elements may be unstable, they nevertheless have higher population times than all alternate conformations; and, second, protein folding progresses through a combinatorial assembly of these elements. Through the binding of the most favorable building block conformers, there is a redistribution of the conformers in solution, propagating the protein-folding reaction. We describe the algorithm, and illustrate its usefulness, then we focus on its utility in assigning simple vs complex folding pathways, on chaperonin-assisted folding, on its relevance to domain-swapping processes, and on its relevance and relationship to disconnectivity graphs and tree diagrams. Considering protein folding as initiating from local transient structural elements is consistent with available experimental and theoretical results. Here, we have shown that, early in the folding process, sequential interactions are likely to take place, even if the final native fold is a complex, nonsequential one. Such a route is favorable kinetically and entropically. Through the construction of anatomy trees, the model enables derivation of the major folding pathways and their bumps, and qualitatively explains the kinetics of protein folding.     

Comparative protein structure modeling by iterative alignment,model building and model assessment

Comparative or homology protein structure modeling is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model implied by it. This task is achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through re-alignment, model building and model assessment to optimize a model assessment score. During this iterative process: (i) new alignments are constructed by application of a number of operators, such as alignment mutations and cross-overs; (ii) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in our program MODELLER; (iii) the models are assessed by a variety of criteria, partly depending on an atomic statistical potential. When testing the procedure on a very difficult set of 19 modeling targets sharing only 4–27% sequence identity with their template structures, the average final alignment accuracy increased from 37 to 45% relative to the initial alignment (the alignment accuracy was measured as the percentage of positions in the tested alignment that were identical to the reference structure-based alignment). Correspondingly, the average model accuracy increased from 43 to 54% (the model accuracy was measured as the percentage of the Cα atoms of the model that were within 5 Å of the corresponding Cα atoms in the superposed native structure). The present method also compares favorably with two of the most successful previously described methods, PSI-BLAST and SAM. The accuracy of the final models would be increased further if a better method for ranking of the models were available.     

Phleomycin and bleomycin: molecular model building studies.

Molecular model building studies were conducted to simulate phleomycin-bleomycin (PB) antibiotics and thus assess a hypothetical polyphleomycin-DNA complex proposed earlier. While the latter model was not conclusively proved it was found to be quite consistent with the structure of PB compounds.     

DINAMO: interactive protein alignment and model building.

MOTIVATION: To facilitate the process of structure prediction by both comparative modeling and fold recognition, we describe DINAMO, an interactive protein alignment building and model evaluation tool that dynamically couples a multiple sequence alignment editor to a molecular graphics display. DINAMO allows the user to optimize the alignment and model to satisfy the known heuristics of protein structure by means of a set of analysis tools. The analysis tools return information to both the alignment editor and graphics model in the form of visual cues (color, shape), allowing for rapid evaluation. Several analysis tools may be employed, including residue conservation, residue properties (charge, hydrophobicity, volume), residue environmental preference, and secondary structure propensity. RESULTS: We demonstrate DINAMO by building a model for submission in the 3rd annual Critical Assessment of Techniques for Protein Structure Prediction (CASP3) contest. AVAILABILITY: DINAMO is freely available as a local application or Web-based Java applet at http://tito.ucsc.edu/dinamo     

Multiple testing in candidate gene situations: a comparison of classical, discrete, and resampling-based procedures

In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SNP data. We analyze FDR and FWER controlling procedures, special procedures for discrete situations, and the minP-resampling-based procedure. Within the simulation study, we examine a broad range of different gene data scenarios. We show that the main difference in the varying performance of the procedures is due to sample size. In small sample size scenarios,the minP-resampling procedure though controlling the stricter FWER even had more power than the classical FDR controlling procedures. In contrast, FDR controlling procedures led to more rejections in higher sample size scenarios.     

Bacteriology testing of cardiovascular tissues: comparison of transport solution versus tissue testing

Bacteriology testing is mandatory for quality control of recovered cardiovascular allografts (CVA). In this paper, two different bacteriology examinations (A tests) performed before tissue antibiotic decontamination were compared: transport solution filtration analysis (A1) and tissue fragment direct incubation (A2). For this purpose, 521 CVA (326 heart and 195 artery tissues) from 280 donors were collected and analyzed by the European Homograft Bank (EHB). Transport solution (A1) tested positive in 43.25 % of hearts and in 48.21 % of arteries, whereas the tissue samples (A2) tested positive in 38.34 % of hearts and 33.85 % of arteries. The main species identified in both A1 and A2 were Staphylococcus spp. in 55 and 26 % of cases, and Propionibacterium spp. in 8 and 19 %, respectively. Mismatches in bacteriology results between both initial tests A1 and A2 were found. 18.40 % of the heart valves were identified as positive by A1 whilst 13.50 % were considered positive by A2. For arteries, 20.51 % of cases were positive in A1 and negative in A2, and just 6.15 % of artery allografts presented contamination in the A2 test but were considered negative for the A1 test. Comparison between each A test with the B and C tests after antibiotic treatment of the allograft was also performed. A total decontamination rate of 70.8 % of initial positive A tests was obtained. Due to the described mismatches and different bacteria identification percentage, utilization of both A tests should be implemented in tissue banks in order to avoid false negatives.     

Neogene biochronology of Antarctic diatoms: A comparison between two quantitative approaches,CONOP and UAgraph

A quantitative biochronological study by Cody et al. (2008) integrates comprehensive diatom biostratigraphy, magnetostratigraphy, and tephrostratigraphy from 32 Neogene sections around the Southern Ocean and Antarctic continental margin. A recent method, known as Constrained Optimization (CONOP), which can be viewed as a multidimensional version of graphic correlation, is applied to that very interesting database.The goal of the present paper is to discuss some theoretical aspects of quantitative biochronology and to compare the constrained optimization with the deterministic method called Unitary Associations (UAM), a graph theoretical model. We illustrate the fact that the UAM is an extremely powerful and unique theory allowing an in-depth analysis of the internal conflicting inter-taxon stratigraphic relationships, inherent to any complex biostratigraphical database.     

Estimation and testing of parent-of-origin effects for quantitative traits

Recent progress in developing family-based association methods has extended their use to the analysis of quantitative traits in the offspring and to the estimation, for dichotomous traits, of the relative contribution of genetic and environmental mechanisms for parent-of-origin effects. However, many traits of interest are not naturally measured on a binary scale yet are suspected or known to be influenced by imprinted genes, and there is consequent interest in seeking evidence for parent-of-origin effects at these loci. Here we show how simple linear models can be used to estimate these parent-of-origin effects for a broad class of phenotypes; in particular, normally distributed quantitative traits are easily dealt with.     

The useful plants of Tambopata,Peru: II. Additional hypothesis testing in quantitative ethnobotany

We present results of applying a simple technique to statistically test several hypotheses in ethnobotany, using plant use data from non-indigenous people in southeast Peru. Hypotheses tested concern: (1) the power of eight different variables as predictors of a plant’s use value; (2) comparisons of ethnobotanical knowledge among informants; and (3) the relationship between informant age and knowledge of plant uses. Each class of hypothesis is evaluated with respect to all uses, and classes (1) and (3) are evaluated for each of the following subsidiary use categories: construction, edible, commerce, medicine, and technology. We found that the family to which a plant belongs explains a large part of the variance in species’ use values. Each of the other factors analyzed (growth-form, density, frequency, mean and maximum diameter, mean and maximum growth rate) is also significantly predictive of use values. Age significantly predicts informant knowledge of(l) all uses, and (2) of medicinal uses. Plant medicinal lore is particularly vulnerable to acculturation.     

Knowledge-based model building of proteins: concepts and examples.

We describe how to build protein models from structural templates. Methods to identify structural similarities between proteins in cases of significant, moderate to low, or virtually absent sequence similarity are discussed. The detection and evaluation of structural relationships is emphasized as a central aspect of protein modeling, distinct from the more technical aspects of model building. Computational techniques to generate and complement comparative protein models are also reviewed. Two examples, P-selectin and gp39, are presented to illustrate the derivation of protein model structures and their use in experimental studies.