PPARGC1A sequence variation and cardiovascular risk-factor levels: a study of the main genetic effects and gene x environment interactions in children from the European Youth Heart Study.

AIMS/HYPOTHESIS: The PPARGC1A gene coactivates multiple nuclear transcription factors involved in cellular energy metabolism and vascular stasis. In the present study, we genotyped 35 tagging polymorphisms to capture all common PPARGC1A nucleotide sequence variations and tested for association with metabolic and cardiovascular traits in 2,101 Danish and Estonian boys and girls from the European Youth Heart Study, a multicentre school-based cross-sectional cohort study. METHODS: Fasting plasma glucose concentrations, anthropometric variables and blood pressure were measured. Habitual physical activity and aerobic fitness were objectively assessed using uniaxial accelerometry and a maximal aerobic exercise stress test on a bicycle ergometer, respectively. RESULTS: In adjusted models, nominally significant associations were observed for BMI (rs10018239, p = 0.039), waist circumference (rs7656250, p = 0.012; rs8192678 [Gly482Ser], p = 0.015; rs3755863, p = 0.02; rs10018239, beta = -0.01 cm per minor allele copy, p = 0.043), systolic blood pressure (rs2970869, p = 0.018) and fasting glucose concentrations (rs11724368, p = 0.045). Stronger associations were observed for aerobic fitness (rs7656250, p = 0.005; rs13117172, p = 0.008) and fasting glucose concentrations (rs7657071, p = 0.002). None remained significant after correcting for the number of statistical comparisons. We proceeded by testing for gene x physical activity interactions for the polymorphisms that showed nominal evidence of association in the main effect models. None of these tests was statistically significant. CONCLUSIONS/INTERPRETATION: Variants at PPARGC1A may influence several metabolic traits in this European paediatric cohort. However, variation at PPARGC1A is unlikely to have a major impact on cardiovascular or metabolic health in these children.

Genetic, structural, and molecular insights into the function of ras of complex proteins domains

Ras of complex proteins (ROC) domains were identified in 2003 as GTP binding modules in large multidomain proteins from Dictyostelium discoideum. Research into the function of these domains exploded with their identification in a number of proteins linked to human disease, including leucine-rich repeat kinase 2 (LRRK2) and death-associated protein kinase 1 (DAPK1) in Parkinson’s disease and cancer, respectively. This surge in research has resulted in a growing body of data revealing the role that ROC domains play in regulating protein function and signaling pathways. In this review, recent advances in the structural informa- tion available for proteins containing ROC domains, along with insights into enzymatic function and the integration of ROC domains as molecular switches in a cellular and organismal context, are explored.

Genetic variant rs17225178 in the ARNT2 gene is associated with Asperger Syndrome

Background Autism Spectrum Conditions (ASC) are neurodevelopmental conditions characterized by difficulties in communication and social interaction, alongside unusually repetitive behaviours and narrow interests. Asperger Syndrome (AS) is one subgroup of ASC and differs from classic autism in that in AS there is no language or general cognitive delay. Genetic, epigenetic and environmental factors are implicated in ASC and genes involved in neural connectivity and neurodevelopment are good candidates for studying the susceptibility to ASC. The aryl-hydrocarbon receptor nuclear translocator 2 (ARNT2) gene encodes a transcription factor involved in neurodevelopmental processes, neuronal connectivity and cellular responses to hypoxia. A mutation in this gene has been identified in individuals with ASC and single nucleotide polymorphisms (SNPs) have been nominally associated with AS and autistic traits in previous studies. Methods In this study, we tested 34 SNPs in ARNT2 for association with AS in 118 cases and 412 controls of Caucasian origin. P values were adjusted for multiple comparisons, and linkage disequilibrium (LD) among the SNPs analysed was calculated in our sample. Finally, SNP annotation allowed functional and structural analyses of the genetic variants in ARNT2. We tested the replicability of our result using the genome-wide association studies (GWAS) database of the Psychiatric Genomics Consortium (PGC). Results We report statistically significant association of rs17225178 with AS. This SNP modifies transcription factor binding sites and regions that regulate the chromatin state in neural cell lines. It is also included in a LD block in our sample, alongside other genetic variants that alter chromatin regulatory regions in neural cells. Conclusions These findings demonstrate that rs17225178 in the ARNT2 gene is associated with AS and support previous studies that pointed out an involvement of this gene in the predisposition to ASC.

FAST, PARALLEL AND SECURE CRYPTOGRAPHY ALGORITHM USING LORENZ`S ATTRACTOR

A novel cryptography method based on the Lorenz`s attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher.

Pharmacogenetics of Warfarin: Development of a Dosing Algorithm for Brazilian Patients

A dosing algorithm including genetic (VKORC1 and CYP2C9 genotypes) and nongenetic factors (age, weight, therapeutic indication, and cotreatment with amiodarone or simvastatin) explained 51% of the variance in stable weekly warfarin doses in 390 patients attending an anticoagulant clinic in a Brazilian public hospital. The VKORC1 3673G>A genotype was the most important predictor of warfarin dose, with a partial R(2) value of 23.9%. Replacing the VKORC1 3673G>A genotype with VKORC1 diplotype did not increase the algorithm`s predictive power. We suggest that three other single-nucleotide polymorphisms (SNPs) (5808T>G, 6853G>C, and 9041G>A) that are in strong linkage disequilibrium (LD) with 3673G>A would be equally good predictors of the warfarin dose requirement. The algorithm`s predictive power was similar across the self-identified ""race/color"" subsets. ""Race/color"" was not associated with stable warfarin dose in the multiple regression model, although the required warfarin dose was significantly lower (P = 0.006) in white (29 +/- 13 mg/week, n = 196) than in black patients (35 +/- 15 mg/week, n = 76).

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Genome-wide association mapping in a wild avian population identifies a link between genetic and phenotypic variation in a life-history trait

Understanding the genetic basis of traits involved in adaptation is a major challenge in evolutionary biology but remains poorly understood. Here, we use genome-wide association mapping using a custom 50 k single nucleotide polymorphism (SNP) array in a natural population of collared flycatchers to examine the genetic basis of clutch size, an important life-history trait in many animal species. We found evidence for an association on chromosome 18 where one SNP significant at the genome-wide level explained 3.9% of the phenotypic variance. We also detected two suggestive quantitative trait loci (QTLs) on chromosomes 9 and 26. Fitness differences among genotypes were generally weak and not significant, although there was some indication of a sex-by-genotype interaction for lifetime reproductive success at the suggestive QTL on chromosome 26. This implies that sexual antagonism may play a role in maintaining genetic variation at this QTL. Our findings provide candidate regions for a classic avian life-history trait that will be useful for future studies examining the molecular and cellular function of, as well as evolutionary mechanisms operating at, these loci.

A Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network

Assigning cells to switches in a cellular mobile network is known as an NP-hard optimization problem. This means that the alternative for the solution of this type of problem is the use of heuristic methods, because they allow the discovery of a good solution in a very satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach and provide good solutions for large scale problems.

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Use of genetic algorithms and solvation potential to study peptides structure

In this work, genetic algorithms concepts along with a rotamer library for proteins side chains and implicit solvation potential are used to optimize the tertiary structure of peptides. We starting from the known PDB structure of its backbone which is kept fixed while the side chains allowed adopting the conformations present in the rotamer library. It was used rotamer library independent of backbone and a implicit solvation potential. The structure of Mastoporan-X was predicted using several force fields with a growing complexity; we started it with a field where the only present interaction was Lennard-Jones. We added the Coulombian term and we considered the solvation effects through a term proportional to the solvent accessible area. This paper present good and interesting results obtained using the potential with solvation term and rotamer library. Hence, the algorithm (called YODA) presented here can be a good tool to the prediction problem. (c) 2007 Elsevier B.V. All rights reserved.

Genetic parameters of milk, fat, and protein yields in the first three lactations, using an animal model and restricted maximum likelihood

Milk, fat, and protein yields of Holstein cows from the States of New York and California in the United States were used to estimate (co)variances among yields in the first three lactations, using an animal model and a derivative-free restricted maximum likelihood (REML) algorithm, and to verify if yields in different lactations are the same trait. The data were split in 20 samples, 10 from each state, with means of 5463 and 5543 cows per sample from California and New York. Mean heritability estimates for milk, fat, and protein yields for California data were, respectively, 0.34, 0.35, and 0.40 for first; 0.31, 0.33, and 0.39 for second; and 0.28, 0.31, and 0.37 for third lactations. For New York data, estimates were 0.35, 0.40, and 0.34 for first; 0.34, 0.44, and 0.38 for second; and 0.32, 0.43, and 0.38 for third lactations. Means of estimates of genetic correlations between first and second, first and third, and second and third lactations for California data were 0.86, 0.77, and 0.96 for milk; 0.89, 0.84, and 0.97 for fat; and 0.90, 0.84, and 0.97 for protein yields. Mean estimates for New York data were 0.87, 0.81, and 0.97 for milk; 0.91, 0.86, and 0.98 for fat; and 0.88, 0.82, and 0.98 for protein yields. Environmental correlations varied from 0.30 to 0.50 and were larger between second and third lactations. Phenotypic correlations were similar for both states and varied from 0.52 to 0.66 for milk, fat and protein yields. These estimates are consistent with previous estimates obtained with animal models. Yields in different lactations are not statistically the same trait but for selection programs such yields can be modelled as the same trait because of the high genetic correlations.

Tabu search algorithm for network synthesis

Large scale combinatorial problems such as the network expansion problem present an amazingly high number of alternative configurations with practically the same investment, but with substantially different structures (configurations obtained with different sets of circuit/transformer additions). The proposed parallel tabu search algorithm has shown to be effective in exploring this type of optimization landscape. The algorithm is a third generation tabu search procedure with several advanced features. This is the most comprehensive combinatorial optimization technique available for treating difficult problems such as the transmission expansion planning. The method includes features of a variety of other approaches such as heuristic search, simulated annealing and genetic algorithms. In all test cases studied there are new generation, load sites which can be connected to an existing main network: such connections may require more than one line, transformer addition, which makes the problem harder in the sense that more combinations have to be considered.

Genetic variability of Brazilian strains of the Microcystis aeruginosa complex (Cyanobacteria/Cyanophyceae) using the phycocyanin intergenic spacer and flanking regions (cpcBA)

The genetic and morphological variability among 15 Brazilian strains of Microcystis aeruginosa (Kütz.) Kütz. collected from four locations was examined and compared with several reference strains of M. aeruginosa, M. viridis (A. Br.) Lemm. and M. wesenbergii (Kom.) Kom. in Kondr. Brazilian strains were classified by morphological features and by comparison of the nucleotide sequences of the cpcBA intergenic spacer and flanking regions. Our results indicate that Brazilian strains classified as M. aeruginosa are phylogenetically diverse compared with reference strains of M. aeruginosa and that the current taxonomy underestimates genetic diversity within M. aeruginosa. The data also demonstrate that morphological criteria alone are inadequate to characterize Microcystis species. Although colonial characters were shown to vary considerably in culture, some genetic lineages demonstrated consistent cellular diameter ranges, indicating that cell size has value as a taxonomic character. The detection of six M. aeruginosa genotypes in a single water body indicates that morphological approaches can also seriously underestimate the diversity of Microcystis bloom populations.