Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]

Jointly multi-user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE-GAMuD scheme can be regarded as a promising alternative for implementing third-generation (3G) and fourth-generation (4G) wireless systems in the near future. Copyright (C) 2010 John Wiley & Sons, Ltd.

Hormonal modulation of photomorphogenesis-controlled anthocyanin accumulation in tomato (Solanum lycopersicum L. cv Micro-Tom) hypocotyls: Physiological and genetic studies

Hormones are likely to be important factors modulating the light-dependent anthocyanin accumulation. Here we analyzed anthocyanin contents in hypocotyls of near isogenic Micro-Tom (MT) tomato lines carrying hormone and phytochrome mutations, as single and double-mutant combinations. In order to recapitulate mutant phenotype, exogenous hormone applications were also performed Anthocyanin accumulation was promoted by exogenous abscisic acid (ABA) and inhibited by gibberellin (GA), in accordance to the reduced anthocyanin contents measured in ABA-deficient (notabills) and GA-constitutive response (procera) mutants. Exogenous cytokinin also enhanced anthocyanin levels in MT hypocotyls. Although auxin-insensitive chageotropica mutant exhibited higher anthocyanin contents, pharmacological approaches employing exogenous auxin and a transport inhibitor did not support a direct role of the hormone in anthocyanin accumulation Analysis of mutants exhibiting increased ethylene production (epwastic) or reduced sensitivity (Never ripe), together with pharmacological data obtained from plants treated with the hormone, indicated a limited role for ethylene in anthocyanin contents. Phytochrome-deficiency (aurea) and hormone double-mutant combinations exhibited phenotypes suggesting additive or synergistic interactions, but not fully espistatic ones, in the control of anthocyanin levels in tomato hypocotyls. Our results indicate that phytochrome-mediated anthocyanin accumulation in tomato hypocotyls is modulated by distinct hormone classes via both shared and independent pathways. (C) 2010 Elsevier Ireland Ltd. All rights reserved

Spatial genetic structure of Hymenaea stigonocarpa Mart. ex Hayne assessed with chloroplast microsatellite markers

The use of chloroplast DNA markers (cpDNA) helps to elucidate questions related to ecology, evolution and genetic structure. The knowledge of inter-and intra-population genetic structure allows to design effective conservation and management strategies for tropical tree species. With the aim to help the conservation of Hymenaea stigonocarpa of the Cerrado (Brazilian savanna) in Sao Paulo State, an analysis of the spatial genetic structure (SGS) was conducted in two populations using five universal chloroplast microsatellite loci (cpSSR). The population of 68 trees of H. stigonocarpa in the Ecological Station of Itirapina (ESI) had a single haplotype, indicating a strong founder effect. In turn, the population of 47 trees of H. stigonocarpa in a contiguous area that includes the Ecological Station of Assis and the Assis State Forest (ESA), showed six haplotypes ((n) over cap (h) = 6) with a moderate haplotype diversity ((h) over cap = 0667 + 0094), revealing that it was founded by a small number of maternal lineages. The SGS analysis for the population ESA/ASF, using Moran`s I index, indicated limited seed dispersal. Considering SGS, for ex situ conservation strategies in the population ESA/ASF, seed harvesting should require a minimum distance of 750 m among seed-trees.

A frailty modeling approach for parental effects in animal breeding

Survival models involving frailties are commonly applied in studies where correlated event time data arise due to natural or artificial clustering. In this paper we present an application of such models in the animal breeding field. Specifically, a mixed survival model with a multivariate correlated frailty term is proposed for the analysis of data from over 3611 Brazilian Nellore cattle. The primary aim is to evaluate parental genetic effects on the trait length in days that their progeny need to gain a commercially specified standard weight gain. This trait is not measured directly but can be estimated from growth data. Results point to the importance of genetic effects and suggest that these models constitute a valuable data analysis tool for beef cattle breeding.

Permanent Genetic Resources added to Molecular Ecology Resources Database 1 August 2010-30 September 2010

This article documents the addition of 229 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Acacia auriculiformis x Acacia mangium hybrid, Alabama argillacea, Anoplopoma fimbria, Aplochiton zebra, Brevicoryne brassicae, Bruguiera gymnorhiza, Bucorvus leadbeateri, Delphacodes detecta, Tumidagena minuta, Dictyostelium giganteum, Echinogammarus berilloni, Epimedium sagittatum, Fraxinus excelsior, Labeo chrysophekadion, Oncorhynchus clarki lewisi, Paratrechina longicornis, Phaeocystis antarctica, Pinus roxburghii and Potamilus capax. These loci were cross-tested on the following species: Acacia peregrinalis, Acacia crassicarpa, Bruguiera cylindrica, Delphacodes detecta, Tumidagena minuta, Dictyostelium macrocephalum, Dictyostelium discoideum, Dictyostelium purpureum, Dictyostelium mucoroides, Dictyostelium rosarium, Polysphondylium pallidum, Epimedium brevicornum, Epimedium koreanum, Epimedium pubescens, Epimedium wushanese and Fraxinus angustifolia.

Genetic structure and biology of Xylella fastidiosa strains causing disease in citrus and coffee in Brazil

Xylella fastidiosa is a vector-borne, plant-pathogenic bacterium that causes disease in citrus (citrus variegated chlorosis [CVC]) and coffee (coffee leaf scorch [CLS]) plants in Brazil. CVC and CLS occur sympatrically and share leafhopper vectors; thus, determining whether X. fastidiosa isolates can be dispersed from one crop to another and cause disease is of epidemiological importance. We sought to clarify the genetic and biological relationships between CVC- and CLS-causing X. fastidiosa isolates. We used cross-inoculation bioassays and microsatellite and multilocus sequence typing (MLST) approaches to determine the host range and genetic structure of 26 CVC and 20 CLS isolates collected from different regions in Brazil. Our results show that citrus and coffee X. fastidiosa isolates are biologically distinct. Cross-inoculation tests showed that isolates causing CVC and CLS in the field were able to colonize citrus and coffee plants, respectively, but not the other host, indicating biological isolation between the strains. The microsatellite analysis separated most X. fastidiosa populations tested on the basis of the host plant from which they were isolated. However, recombination among isolates was detected and a lack of congruency among phylogenetic trees was observed for the loci used in the MLST scheme. Altogether, our study indicates that CVC and CLS are caused by two biologically distinct strains of X. fastidiosa that have diverged but are genetically homogenized by frequent recombination.

Genetic divergence is not the same as phenotypic divergence

Far too often, phenotypic divergence has been misinterpreted as genetic divergence, and based on phenotypic divergence, genetic divergence has been indicated. We have attempted to disprove this statement and call for the differentiation of phenotypic and genotypic variation.

Genetic potential of a maize population from Southern Brazil for the modified convergent-divergent selection scheme

Maize (Zea mays L.) is a very important cereal to world-wide economy which is also true for Brazil, particularly in the South region. Grain yield and plant height have been chosen as important criteria by breeders and farmers from Santa Catarina State (SC), Brazil. The objective of this work was to estimate genetic-statistic parameters associated with genetic gain for grain yield and plant height, in the first cycle of convergent-divergent half-sib selection in a maize population (MPA1) cultivated by farmers within the municipality of Anchieta (SC). Three experiments were carried out in different small farms at Anchieta using low external agronomic inputs; each experiment represented independent samples of half-sib families, which were evaluated in randomized complete blocks with three replications per location. Significant differences among half-sib families were observed for both variables in all experiments. The expected responses to truncated selection of the 25% better families in each experiment were 5.1, 5.8 and 5.2% for reducing plant height and 3.9, 5.7 and 5.0% for increasing grain yield, respectively. The magnitudes of genetic-statistic parameters estimated evidenced that the composite population MPA1 exhibits enough genetic variability to be used in cyclical process of recurrent selection. There were evidences that the genetic structure of the base population MPA1, as indicated by its genetic variability, may lead to expressive changes in the traits under selection, even under low selection pressure.