Heritability Estimate and Genetic Correlations of Reproductive Features in Nellore Bulls, Offspring of Super Precocious, Precocious and Normal Cows Under Extensive Farming Conditions

The present work aimed to estimate heritability and genetic correlations of reproductive features of Nellore bulls, offspring of mothers classified as superprecocious (M1), precocious (M2) and normal (M3). Twenty one thousand hundred and eighty-six animals with average age of 21.29 months were used, evaluated through the breeding soundness evaluation from 1999 to 2008. The breeding soundness features included physical semen evaluation (progressive sperm motility and sperm vigour), semen morphology (major, minor and total sperm defects), scrotal circumference (SC), testicular volume (TV) and SC at 18 months of age (SC18). The components of variance, heritability and genetic correlations for and between the features were estimated simultaneously by restricted maximum likelihood, with the use of the vce software system vs 6. The heritability estimates were high for SC18, SC and TV (0.43, 0.63 and 0.54; 0.45, 0.45 and 0.44; 0.42, 0.45 and 0.41, respectively for the categories of mothers M1, M2 and M3) and low for physical and morphological semen aspects. The genetic correlations between SC18 and SC were high, as well as between these variables with TV. High and positive genetic correlations were recorded among SC18, SC and TV with the physical aspects of the semen, although no favourable association was verified with the morphological aspects, for the three categories of mothers. It can be concluded that the mothers sexual precocity did not affect the heritability of their offspring reproduction features.

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

For many tree species, mating system analyses have indicated potential variations in the selfing rate and paternity correlation among fruits within individuals, among individuals within populations, among populations, and from one flowering event to another. In this study, we used eight microsatellite markers to investigate mating systems at two hierarchical levels (fruits within individuals and individuals within populations) for the insect pollinated Neotropical tree Tabebuia roseo-alba. We found that T. roseo-alba has a mixed mating system with predominantly outcrossed mating. The outcrossing rates at the population level were similar across two T. roseo-alba populations; however, the rates varied considerably among individuals within populations. The correlated paternity results at different hierarchical levels showed that there is a high probability of shared paternal parentage when comparing seeds within fruits and among fruits within plants and full-sibs occur in much higher proportion within fruits than among fruits. Significant levels of fixation index were found in both populations and biparental inbreeding is believed to be the main cause of the observed inbreeding. The number of pollen donors contributing to mating was low. Furthermore, open-pollinated seeds varied according to relatedness, including half-sibs, full-sibs, self-sibs and self- half-sibs. In both populations, the effective population size within a family (seed-tree and its offspring) was lower than expected for panmictic populations. Thus, seeds for ex situ conservation genetics, progeny tests and reforestation must be collected from a large number of seed-trees to guarantee an adequate effective population in the sample.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Comparison of microsatellites and isozymes in genetic diversity studies of Oryza glumaepatula (Poaceae) populations

The study of the genetic structure of wild plant populations is essential for their management and conservation. Several DNA markers have been used in such studies, as well as isozyme markers. In order to provide a better comprehension of the results obtained and a comparison between markers which will help choose tools for future studies in natural populations of Oryza glumaepatula, a predominantly autogamous species, this study used both isozymes and microsatellites to assess the genetic diversity and genetic structure of 13 populations, pointing to similarities and divergences of each marker, and evaluating the relative importance of the results for studies of population genetics and conservation. A bulk sample for each population was obtained, by sampling two to three seeds of each plant, up to a set of 50 seeds. Amplified products of eight SSR loci were electrophoresed on non-denaturing polyacrylamide gels, and the fragments were visualized using silver staining procedure. Isozyme analyses were conducted in polyacrylamide gels, under a discontinuous system, using six enzymatic loci. SSR loci showed higher mean levels of genetic diversity (A=2.83, p=0.71, A(P)=3.17, H-o=0.081, H-e=0.351) than isozyme loci (A=1.20, p=0.20, A(P)=1.38, H-o=0.006, H-e=0.056). Interpopulation genetic differentiation detected by SSR loci (R-ST=0.631, equivalent to F-ST=0.533) was lower than that obtained with isozymes (F-ST=0.772). However, both markers showed high deviation from Hardy-Weinberg expectations (F-IS=0.744 and 0.899, respectively for SSR and isozymes). The mean apparent outcrossing rate for SSR ((t) over bar (a)=0.14) was higher than that obtained using isozymes ((t) over bar (a)=0.043), although both markers detected lower levels of outcrossing in Amazonia compared to the Pantanal. The migrant number estimation was also higher for SSR (Nm=0.219) than isozymes (Nm=0.074), although a small number for both markers was expected due to the mode of reproduction of this species, defined as mixed with predominance of self fertilization. No correlation was obtained between genetic and geographic distances with SSR, but a positive correlation was found between genetic and geographic distances with isozymes. We conclude that these markers are divergent in detecting genetic diversity parameters in O. glumaepatula and that microsatellites are powerful for detecting information at the intra-population level, while isozymes are more powerful for inter-population diversity, since clustering of populations agreed with the expectations based on the geographic distribution of the populations using this marker. Rev. Biol. Trop. 60 (4): 1463-1478. Epub 2012 December 01.

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.

The genetic signature of Neolithic in Greece

The Neolithic is characterized by the transition from a subsistence economy, based on hunting and gathering, to one based on food producing. This important change was paralleled by one of the most significant demographic increase in the recent history of European populations. The earliest Neolithic sites in Europe are located in Greece. However, the debate regarding the colonization route followed by the Middle-eastern farmers is still open. Based on archaeological, archaeobotanical, craniometric and genetic data, two main hypotheses have been proposed. The first implies the maritime colonization of North-eastern Peloponnesus from Crete, whereas the second points to an island hopping route that finally brought migrants to Central Greece. To test these hypotheses using a genetic approach, 206 samples were collected from the two Greek regions proposed as the arrival point of the two routes (Korinthian district and Euboea). Expectations for each hypothesis were compared with empirical observations based on the analysis of 60 SNPs and 26 microsatellite loci of Y-chromosome and mitochondrial DNA hypervariable region I. The analysis of Y-chromosome haplogroups revealed a strong genetic affinity of Euboea with Anatolian and Middle-eastern populations. The inferences of the time since population expansion suggests an earlier usage of agriculture in Euboea. Moreover, the haplogroup J2a-M410, supposed to be associated with the Neolithic transition, was observed at higher frequency and variance in Euboea showing, for both these parameters, a decreasing gradient moving from this area. The time since expansion estimates for J2a-M410 was found to be compatible with the Neolithic and slightly older in Euboea. The analysis of mtDNA resulted less informative. However, a higher genetic affinity of Euboea with Anatolian and Middle-eastern populations was confirmed. These results taken as a whole suggests that the most probable route followed by Neolithic farmers during the colonization of Greece was the island hopping route.

Exercise associated factors affecting cardiovascular health and function: from myocyte signaling to genetic variations

The exact mechanisms of the exercise induced adaptations is not lucid, but recent studies have delineated two means of signaling by which the adaptations occur (1) substrate availability signaling (metabolic stress) (2) hormone-receptor signaling. We have decided to specifically investigate two metabolic signaling enzymes [AMP-activated kinase (AMPK) and Sirtuin 1(SIRT1)] and two hormones [Adiponectin and Adrenergic stimulation].Tis based on four papers with the following conclusions: (1)Increase in SIRT1 activity and expression in H9c2 cells treated with phenylephrine is an adaptive response to the hypertrophic stress, mediated by AMPK. (2)The lack of optimal nutritional conditions (energetic substrates) due to a prolonged activation of AMPK can contrast the establishment of hypertrophy, possibly also by means of the negative modulation of ODC activity. (3) Our findings offer a possibile hypothesis as to the fact the the G allele on site 45 could lead to the increasd risk of Type II diabetes through a decrease in lean body mass. (4) Our results suggest that there is an ADIPOQ gene effect in relation to bone parameters. Statistical analysis show that the presence of the T allele in position 45 favors an increase in lumbar spine bone mineral content (BMC) when compared to subjects with a G allele substitution, which can be do the the increase in lean body mass in this genotype group.

Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.

Progression of pulmonary hyperinflation and trapped gas associated with genetic and environmental factors in children with cystic fibrosis

BACKGROUND: Functional deterioration in cystic fibrosis (CF) may be reflected by increasing bronchial obstruction and, as recently shown, by ventilation inhomogeneities. This study investigated which physiological factors (airway obstruction, ventilation inhomogeneities, pulmonary hyperinflation, development of trapped gas) best express the decline in lung function, and what role specific CFTR genotypes and different types of bronchial infection may have upon this process. METHODS: Serial annual lung function tests, performed in 152 children (77 males; 75 females) with CF (age range: 6-18 y) provided data pertaining to functional residual capacity (FRCpleth, FRCMBNW), volume of trapped gas (VTG), effective specific airway resistance (sReff), lung clearance index (LCI), and forced expiratory indices (FVC, FEV1, FEF50). RESULTS: All lung function parameters showed progression with age. Pulmonary hyperinflation (FRCpleth > 2SDS) was already present in 39% of patients at age 6-8 yrs, increasing to 67% at age 18 yrs. The proportion of patients with VTG > 2SDS increased from 15% to 54% during this period. Children with severe pulmonary hyperinflation and trapped gas at age 6-8 yrs showed the most pronounced disease progression over time. Age related tracking of lung function parameters commences early in life, and is significantly influenced by specific CFTR genotypes. The group with chronic P. aeruginosa infection demonstrated most rapid progression in all lung function parameters, whilst those with chronic S. aureus infection had the slowest rate of progression. LCI, measured as an index of ventilation inhomogeneities was the most sensitive discriminator between the 3 types of infection examined (p < 0.0001). CONCLUSION: The relationships between lung function indices, CFTR genotypes and infective organisms observed in this study suggest that measurement of other lung function parameters, in addition to spirometry alone, may provide important information about disease progression in CF.

Fine-scale genetic structure and dispersal in the common vole (Microtus arvalis)

The genetic structure and demography of local populations is tightly linked to the rate and scale of dispersal. Dispersal parameters are notoriously difficult to determine in the field, and remain often completely unknown for smaller organisms. In this study, we investigate spatial and temporal genetic structure in relation to dispersal patterns among local populations of the probably most abundant European mammals, the common vole (Microtus arvalis). Voles were studied in six natural populations at distances of 0.4-2.5 km in three different seasons (fall, spring, summer) corresponding to different life-history stages. Field observations provided no direct evidence for movements of individuals between populations. The analysis of 10 microsatellite markers revealed a persistent overall genetic structure among populations of 2.9%, 2.5% and 3% FST in the respective season. Pairwise comparisons showed that even the closest populations were significantly differentiated from each other in each season, but there was no evidence for temporal differentiation within populations or isolation by distance among populations. Despite significant genetic structure, assignment analyses identified a relatively high proportion of individuals as being immigrants for the population where they were captured. The immigration rate was not significantly lower for females than for males. We suggest that a generally low and sex-dependent effective dispersal rate as the consequence of only few immigrants reproducing successfully in the new populations together with the social structure within populations may explain the maintenance of genetic differentiation among populations despite migration.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.

Spatial isolation and genetic differentiation in naturally fragmented plant populations of the Swiss Alps

Aims The effect Of anthropogenic landscape fragmentation on the genetic diversity and adaptive potential of plant populations is a major issue in conservation biology. However, little is known about the partitioning of genetic diversity in alpine species, which occur in naturally fragmented habitats. Here, we, investigate molecular patterns of three alpine plants (Epilobium fleischeri, Geum reptans and Campanula thyrsoides) across Switzerland and ask whether Spatial isolation has led to high levels of populations differentiation, increasing over distance, and a decrease of within-population variability. We further hypothesize that file contrasting potential for long-distance dispersal (LDD) of Seed in these Species will considerably influence and explain diversity partitioning. Methods For each study species, we Sampled 20-23 individuals from each of 20-32 populations across entire Switzerland. We applied Random Amplified Polymorphic Dimorphism markers to assess genetic diversity within (Nei's expected heterozygosity, H-e; percentage of polymorphic hands, P-P) and among (analysis of molecular variance, Phi(st)) populations and correlated population size and altitude with within-populalion diversity. Spatial patterns of genetic relatedness were investigated using Mantel tests and standardized major axis regression as well as unweighted pair group method with arithmetic mean cluster analyses and Monmonier's algorithm. To avoid known biases, We standardized the numbers of populations, individuals and markers using multiple random reductions. We modelled LDD with a high alpine wind data set using the terminal velocity and height of seed release as key parameters. Additionally, we assessed a number of important life-history traits and factors that potentially influence genetic diversity partitioning (e.g. breeding system, longevity and population size). Important findings For all three species, We found a significant isolation-by-distance relationship but only a moderately high differentiation among populations (Phi(st): 22.7, 48 and 16.8%, for E. fleischeri, G. reptans and C. thyrsoides, respectively). Within-population diversity (H-c: 0.19-0.21, P-p: 62-75%) was not reduced in comparison to known results from lowland species and even small populations with < 50 reproductive individuals contained high levels of genetic diversity. We further found no indication that a high long-distance seed dispersal potential enhances genetic connectivity among populations. Gene flow seems to have a strong stochastic component causing large dissimilarity between population pairs irrespective of the spatial distance. Our results suggest that other life-history traits, especially the breeding System, may play an important role in genetic diversity partitioning. We conclude that spatial isolation in the alpine environment has a strong influence on population relatedness but that a number of factors can considerably influence the strength of this relationship.

Genetic and phenotypic diversity in selected genotypes of tef [Eragrostis tef (Zucc.)] Trotter

Tef [Eragrostis tef (Zucc.) Trotter] is an important staple food crop, especially in Ethiopia where it is annually grown on 2.8 million hectares of land. It is important for food security in the region, in spite of having a low yield, mainly due to lodging. In this study, 15 representative landraces as well as three improved varieties have been selected for in-depth characterization of many parameters, especially those implicated in yield. The genotypes were clustered into six groups, mainly based on agronomic traits and about 80% of the diversity in the genotypes could be explained on the basis of four principal components. In general, all traits investigated showed substantial diversity among genotypes, offering high chances for improving tef through direct selection or intra-specific hybridization. Moreover, in view of climatic changes, breeding with early maturing landraces such as Red dabi or Karadebi would be advantageous to cope with moisture scarcity during the later stage of crop maturity.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Genetic analysis of white facial and leg markings in the Swiss Franches-Montagnes Horse Breed

White markings and spotting patterns in animal species are thought to be a result of the domestication process. They often serve for the identification of individuals but sometimes are accompanied by complex pathological syndromes. In the Swiss Franches-Montagnes horse population, white markings increased vastly in size and occurrence during the past 30 years, although the breeding goal demands a horse with as little depigmented areas as possible. In order to improve selection and avoid more excessive depigmentation on the population level, we estimated population parameters and breeding values for white head and anterior and posterior leg markings. Heritabilities and genetic correlations for the traits were high (h(2) > 0.5). A strong positive correlation was found between the chestnut allele at the melanocortin-1-receptor gene locus and the extent of white markings. Segregation analysis revealed that our data fit best to a model including a polygenic effect and a biallelic locus with a dominant-recessive mode of inheritance. The recessive allele was found to be the white trait-increasing allele. Multilocus linkage disequilibrium analysis allowed the mapping of the putative major locus to a chromosomal region on ECA3q harboring the KIT gene.