text:
- Software
- Open Access
Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2
- Michele Pinelli†1, 2Email author,
- Giovanni Scala†1, 3,
- Roberto Amato1, 3, 4,
- Sergio Cocozza1, 2 and
- Gennaro Miele1, 3, 4
© Pinelli et al.; licensee BioMed Central Ltd. 2012
- Received: 20 December 2011
- Accepted: 10 May 2012
- Published: 14 June 2012
The analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods.
The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive, the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Python language and takes advantage of operators and modules provided by the simuPOP simulation environment. It can be used through a graphical or a command-line interface and is freely available from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0.
Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.
- Gene-environment interaction
- Computer simulation
- Complex disease
- Epistasis
- Genetic
- Genome-wide association study
- Genetics
- Population
- SimuPOP
- Linkage disequilibrium
- Genomics
Most of the common human diseases with high mortality rates (such as cancer, heart disease, obesity, diabetes, and several common psychiatric and neurological conditions) are classified as complex diseases [1, 2]. By definition, a complex disease is a multifactorial complex trait generally caused by multiple predisposing loci (possibly interacting) and by the exposure to particular environmental factors [3]. Although several genetic and environmental factors have been shown to affect susceptibility to particular complex diseases, the intricate sets of relationships between these factors and disease susceptibility are not yet exhaustively understood. For this reason, typically, the proportion of risk accountable to genetics and environment remains mostly unpredictable [4]. Explanations for their unpredictability might include the occurrence of still unidentified factors and/or the presence of non-linear interactions among already identified factors; for example, some combinations of genetic and environmental factors could have disease risks that are consistently higher than those predicted by a single component.
Gene-environment interactions (G×E) are expected to influence complex phenotypes, for example, disease risk. Hence individuals with predisposing genetics are more likely to develop a disease when exposed to a damaging environment than individuals, exposed to the same environment, without predisposing genetics [5, 6]. The role of G×E is so relevant that it is generally accepted that neglecting them can lead to an underestimation of disease risk, and may explain some of the inconsistencies in replications in different studies [7].
Complex phenotypes are regulated by pathways and biochemical mechanisms that involve many genetic products. Hence, in addition to interactions among genes and environment, interactions among different genetic loci (G×G) can also influence disease risk. In particular, G×G are defined as epistatic when the allelic variations of one gene alters the effect of variations of another gene [8]. Epistasis has been identified in human diseases [9, 10], and its role in public health has been highlighted [8].
Surprisingly, despite the general agreement on the relevance of G×E and G×G for correct disease risk estimations, only a few epidemiological studies have attempted to identify them. Indeed, studying the complex interactions among risk factors is a daunting task that requires large data sets and specific research designs. Furthermore, the best statistical method for the identification of G×G and G×E in case-control data sets [11, 12] is still widely debated. The performance of statistical methods that are used for the identification of G×G and G×E are typically influenced by many factors: sample size, number of involved factors, type of interaction, model of inheritance, allelic frequencies, distributions of the environmental factors, and relative strength of different factors affecting disease risk. Unfortunately, only a few of these features are generally assessable in real populations. A further limitation of the epidemiological studies that have been performed so far, is the limited knowledge about the impact of linkage disequilibrium (LD) on association statistics in the presence of G×G and G×E. It has been demonstrated that when G×G occur and the assayed SNPs are not the functional ones but SNPs that are in LD with them, common statistics like r2 are generally inappropriate and tend to lead to an over/underestimation of disease risk [13].
- 1.
a Multi-Logistic Model (MLM) that can model any type of G×G and G×E,
- 2.
a mathematical approach (Knowledge Aided Parameterization System, KAPS) that can translate biological and epidemiological information to MLM parameters, and
- 3.
GENS (Gene Environment iNteraction Simulator), a software that produces simulated data sets.
Using that approach interactions between one genetic and one environmental factor only could be simulated; therefore, it was not possible to account for epistatic G×G. Moreover, all simulated loci were considered to be independent and thus it was not possible to account for LD [16].
In the present paper, we describe an extension of the previous model that overcomes such limitations using a new strategy that simulates up to two-genes×one-environment interactions with the possible inclusion of epistasis. Importantly, the present algorithm can be easily extended to manage more than two genetic and one environmental factors. However, to simplify the design of biologically meaningful interactions, we limited the number of features (see the Discussion section for details). Furthermore, the inclusion of two genetic factors (with epistatic interaction) that in turn interact with a continuous environmental factor heavily increased of the complexity of the model. Indeed, statistical methods that can deal with even two genetic factors are still far from being functionally useful for real, large data sets [17]. To provide a realistic genetic background to the simulated populations, we implemented our extended model as a module which can be integrated with simuPOP, a forward-time populations simulator that reproduces realistic demographic and evolutionary features [18].
GENS2 workflow
The epidemiologic parameters that were used for the sample simulation
Required parameters
1) Starting data (Hap Map)
Chromosomes, or chromosome regions, or markers and marker distance.
The genomic regions containing the loci that will be simulated
Population (ethnicity)
The starting frequency and linkage data to be used in the sumulation
2) Simulation of sample's genetic data
DPLs (Disease Predisposing Loci)
Loci that will influence the disease risk.
Target allelic frequency
Final allelic frequencies at the end of simuPOP simulation
Final sample size
Number of individuals that compose the population by simuPOP
Starting sample
simuPOP generated sample
Sample data generated with simuPOP
Disease prevalence
The expected disease prevalence in the whole sample
Environmental factor distribution
Distribution of the environmental exposure in the whole sample
Environmental factor OR
Odds ratio associated with one-unit-increase of the environmental exposure
Noisy Environmental variables
As many as desired confounding environmental exposures not associated with the disease risk (gaussian, binomial or uniform distributed)
These are the same DPLs as selected in the simuPOP simulation
High risk alleles
The allele, for each DPL, associated with the highest disease risk
DPLs genotypic RR
The relative risk of the high risk homozygote versus low risk homozygote, for each DPL
The relationship of the risk associated with the heterozygote with that associated with the homozygotes (recessive, dominant, codominant)
Epistasis model (G×G)
Percent increase of the risk associated with each combined genotype
Gene Environment interaction
G×E model
One of the four predefined interaction models
Generation of the synthetic data set
The generation of the starting sample is carried out by a series of simuPOP scripts [19] that
download phased genomic data from the HapMap public database [20],
select a subset of SNPs or entire genomic regions, and
let the population evolve until it reaches the desired size and frequencies for some disease predisposing loci (DPLs).
To obtain a synthetic data set, simuPOP drives a forward-time simulation to obtain a population that is composed of the desired number of individuals and genotypic frequencies for all the markers. The use of this simulator helps to retain genetic realism in the final population, in particular with respect to the patterns of LD (for a detailed description of this process, please see [19]). When the genetic information for each individual has been obtained, GENS2 assigns environmental exposures following a user defined Gaussian distribution for the disease environmental variable, and several other user defined distributions (Gaussian, Uniform or Binomial) for a number of other environmental variables not related with the disease (environmental-confusing variables).
Definition of the penetrance model
The second branch of the simulation procedure (right side of Figure 1) is implemented in the Knowledge Aided Parameterization System 2 (KAPS2) subsystem which accepts the input of some standard epidemiological measures for the relevant features listed here:
the expected prevalence of the disease in the sample,
the id in the input data set of one or two DPLs,
the allelic frequencies of DPLs (calculated automatically from the input population),
the effect on disease risk of each DPL in terms of the relative risk of the high risk homozygote compared with the other homozygote,
the dominance relation of each DPL (W), expressed as a number in the interval [0−1], representing the dominance relation (W=0 dominant, W=1 recessive, 0<W<1 co-dominant), and
the distribution parameters and the effect of the environmental factor on disease risk, expressed as odds ratio (OR) of the risk related to one-unit increase in the exposure.
- i)
G×E models are chosen from a set of four predefined models, two models of interaction between DPLs and the environment, and two special models in which there is no gene-environment interaction but in which only one genetic or environmental factor contributes to the disease risk (see Table 2).
Predefined gene-environment interaction models in GENS2
Interaction model
Genetic Model(GEN)
Disease risk depends only on the genetics of an individual
Environmental Model (ENV)
Disease risk depends only on environmental exposure of an individual
Gene Environment interaction Model (GEM)
The genetics modifies the effect of the environment in modulating the disease risk
Additive Model(ADD)
The effects of environment and genetics are independent and sums in modulating the disease risk
- ii)
G×G models (epistasis) are accepted in the form of percentage variations on the risk associated with a maximum number of three (out of the possible nine) combined genotypes.
Consequently, for each combined genotype, KAPS2 computes the coefficients of a penetrance function of the environmental exposure that is associated with the combined genotype in the MLM. In this step G×E are also modeled; the Additive model (ADD) assumes that combined genotypes with higher penetrance have a higher basal disease risk, while the risk associated with the environmental factor is just added. On the other hand, in the modulative model (GEM), combined genotypes with higher penetrance have the same basal risk although they are more 'sensitive' to the effect of the environment (see the Methods section).
Disease risk of an individual
In the final step the two branches of the procedure (Figure 1) join. Once the genetics and the environmental exposure for each individual are given, its disease risk can be obtained by evaluating the penetrance function associated with its combined genotype. The risk is then used to assign a disease status using a random process.
To create simulated populations, we employed an existing tool, SimuPOP, and the implementation of the above described algorithm. Using SimuPOP it is possible to drive a forward-time simulation that results in a population composed by the desired number of individuals and having specified genotype frequencies for a set of markers. To be usable in GENS2, populations should be created in SimuPOP as described previously [19]. This procedure generates a data set that contains genotypic data as a set of bi-allelic loci, for each individual in the population.
GENS2 accepts as input a population produced by SimuPOP and the ids of the DPLs chosen from those present in the data set. For each DPL, the penetrance values are provided as relative risk (RR) and dominance model (see [14]). RR is calculated as the risk ratio of the high over the low risk homozygotes. For each combination of DPL genotypes, the percentage variation of the penetrance value if epistasis occurs can be assigned. GENS2 assigns environmental factor exposure on the basis of a user-defined Gaussian distribution. Any remaining non-implicated loci can be considered as background noise. Similarly, GENS2 can produce an arbitrary number of non-associated random environmental exposures, following a Gaussian, uniform or binomial distribution, that act as further background noise.
On the basis of the selected type of G×G and G×E, GENS2 calculates the coefficients of the MLM as described in the Method section.
For each individual, GENS2 assigns the disease status (affected or unaffected) on the basis of its disease risk by applying the MLM and using a random process.
The main output of the software can be either a single file or several files for a set of subpopulations of a given size produced by means of a subsampling procedure. Subsampling allows bootstrapping procedures to be executed on data sets produced with the same features. The output of GENS2 is in the form of a table in which each row represents an individual and the columns contain, from the left to the right, disease status, gender, environmental exposures and genotypes for each individual.
Two possible formats for the genetics output are available: phased haplotypes or genotypes. In both output formats the initial columns are identical to those described above; however, they differ in the way the genetic information for each individual is represented. In the phased haplotype format, there are two columns for each SNP that report the allele status (either A, C, T or G) on each chromosome. In the genotype format, each SNP is represented by one number (1, 2 or 3), where 2 represents the heterozygote and 3 represents, for DLPs, the high risk homozygote or, for all the other SNPs, the lower frequency homozygote.
In addition to the main output file, GENS2 outputs a log file that contains an extensive report of all the intermediate steps in the procedure and the values used to obtain the populations. Optionally, a file containing the ID and genomic position of the SNPs in the data set can be returned.
GENS2 is designed to be used either from the command line as a Python script, or through a graphical user interface, similar to a wizard, that prompts the user in the specification of all required parameters [see Additional file 1].
Overall, the computational time complexity of the simulation procedure depends by both simuPOP and GENS2. For GENS2, the procedure is dominated by the assignment of the disease status to all individuals in the population. Indeed, after the KAPS2 module has performed the translation of user provided parameters into MLM parameters in bounded constant time, the time complexity becomes linear in the number of individuals and the number of represented variables (genotypes and environmental exposures) for each individual in the simulated population. On the other hand, the amount of time required to perform a simulation with simuPOP depends on the size of the simulation and scales roughly linearly with the number of markers and individuals that are used [19]. GENS2 is written in Python and is completely open-source. The software is freely available from [21].
Results and discussion
Here we describe a method based on the MLM to simulate two genetic and one environmental factors interacting in the determination of a disease risk. The method is implemented in GENS2, a software that is freely available.
To test populations produced by GENS2, we performed a set of analyses on some representative populations. The aim was to emulate a case in which GENS2 was used to assess the performances of a feature selection method. In particular, all the analyses were performed using a logistic regression (glm function in R) with a different model for each test and considering the status as the dependent variable.
The first test was a single-marker analysis on a population of 1,000 cases and 1,000 controls with two DPLs in two distinct genomic regions, with no epistasis and an additive G×E model. The association of each marker with the status was tested using logistic regression analyses with model: disease risk = genetic factor + environmental factor. As expected, the most significant associations were those of DPLs [see Additional file 2]. The result showed that the environmental variable was also associated with the disease (p<10−6). Furthermore, non-causative markers in LD with the two DPLs also showed a significant association that was roughly proportional to the value of r2 with the DPLs.
The second test was similar to the first, except that 10,000 cases and 10,000 controls and a modulative G×E model for the DPLs were used. For this test, the logistic regression was used by considering both an additive model (disease risk = genetic factor + environmental factor) and a multiplicative model (disease risk = genetic factor * environmental factor). None of the markers, when tested by additive model, reached a Bonferroni corrected significance level [see Additional file 3, middle panel]. Conversely, DPLs were found to be significant when the multiplicative model was explicitly considered. Non-causative markers in LD with them were also found to be significant. Notably, this more complex model required a 10-fold increase in the sample size to achieve the same significance level as the previous test.
The model described here can handle, in principle, any number of DPLs and environmental variables. However, we chose to limit the implementation to a relatively small number of factors (two genetic and one environmental) so that setting up the model does not become too complicated for the user. In this way, we reached a balance between the complexity of the represented phenomena and simplicity in the definition of the model. Moreover, the best strategy to identify even simple interactions as single G×G and G×E with binary environmental variables it is still debated (for an example of the debate, see the report on the 2009 Genetic Analysis Workshop [11, 12, 17]). For this reason, we believe that a set of simulated populations in which all features are known provides an important tool for the identification of the best strategy to identify and study G×G and G×E.
Several methods simulating genetic data have been proposed, many of them also handle complex LD patterns and polygenic traits [22, 23]. For example, HAPGEN2 [22] simulates multiple polymorphic loci that are in LD and in the same chromosomal region. HAPGEN2 can simulate G×G, including epistasis, between two loci; however, the available models are limited to a predefined set. Moreover, this program cannot handle G×E and the number of cases and controls that are produced cannot be controlled. Another tool, gs[23], similarly simulates multiple loci with a realistic pattern of LD; it can modulate a quantitative trait (as disease risk) and can also manage G×G and G×E. In gs the user can define G×G between two loci in two ways: one, by providing a penetrance matrix for combined genotypes or, two, by selecting a penetrance matrix from a predefined set of G×G models. However, both these approaches have some limitations. Although the first allows great control over the penetrance of each genotype, it easily leads to a loosening of control on marginal effects, making the replication of real populations difficult. The second approach, on the contrary, is too restrictive and does not allow any possible interaction to be simulated. For simulation of G×G, gs allows the user to input a list of rules regarding specific combinations of genotypes and levels of environmental values, and the corresponding risk levels. Again, this approach makes it very difficult to control the overall characteristics of populations in terms of marginal effects of genetic and environmental factors. The approaches described above can simulate complex interactions by loosening control on overall population characteristics or, alternatively, can keep the overall population characteristics under control by limiting the freedom to specify the interactions. Another strategy to simulate G×G and G×E is to manually write functions or sets of rules that associate each combination of genes and environmental factors to a risk value. Although this strategy provides more freedom, it is very difficult to set up when the control of marginal effects of single factors is desired. We believe that GENS2 provides a better balance between the freedom to define possible interactions among factors and the control of the overall population characteristics.
The simulated populations produced with GENS2 can be thought of as a sampling of an ideal infinite population that has the characteristics specified by the user. From this point of view, it is easy to understand that fluctuations of observed values around the expected ones can occur. Among the elements that mostly affect these fluctuations, are sample size, allele frequencies, and penetrance values. In particular, small sample sizes increase the effect of sampling error and thus, as expected, these fluctuations tend to vanish as the sample size is increased [see Additional file 4]. This property can be used to test statistical methods because it allows the user to assess how well the methods perform as population and sample features change; conversely, it helps to assess the statistical power of methods in the presence of population features that can only be presumed.
Although the GENS2 part of the simulation process is reasonably fast, the procedure to simulate large populations using simuPOP takes time to complete. It would be difficult to simulate a large number of samples without a cluster system, unless multiple (small) samples are drawn from the same large population.
GENS2 allows the simulation of gene-gene and gene-environment interactions among two genetic and one environmental factor in relation to the risk to develop a complex disease. It is based on data with a realistic pattern of LD and it has no limitations either on the number of individuals that can be simulated or on the number of genetic and environmental factors within a simulated data set. Furthermore, a large amount of effort has been channeled into allowing the input of parameters as standard epidemiological quantities so that the software is immediately usable by the biomedical community.
GENS2 provides large biologically realistic data sets with known features that can be used to challenge, and eventually improve, the statistical tools that are designed to identify those interactions.
- 1.
the genetics can influence the disease risk either directly or by modifying the effect of the environment.
- 2.
the genetic loci can have independent effects (no epistasis) or can interact in an epistatic manner, and
- 3.
the DPLs are not in LD.
The Multi-Logistic Model
where and are free parameters determined by the genetic factors that determine the shape of the function.
To simulate a population, the coefficients and of the logistic functions that produce the desired population features have to be determined. This task is performed by the KAPS2 module that finds the corresponding MLM coefficients by considering all the desired population features within a set of biological constraints.
Determination of MLM parameters
where X is the domain of the environmental variable.
Because every logistic function in MLM is characterized by its own parameters, the 3×3 pairs of values ( that satisfy the constraints expressed by equation (2) need to be found.
Modeling G×E
In general, equation (2) admits infinite solutions. However, the G×E model imposes some constraints on the coefficients. Thus, by fixing the value of one of the coefficients , hereafter denoted as β AB , the number of degrees of freedom of the system can be reduced, drawing one solution from the equation system. By convention, we chose to associate β AB to the genotype with highest risk; it is easy to show that this value corresponds to the natural logarithm of the odds ratio of the risk which is related to the increase of one unit of the environmental exposure. Constraints imposed on the system by each one of the proposed gene environment interactions model are summarized below:
Genetic effect (GEN): and and .
Environmental effect (ENV): and .
Modulative effect (GEM): and .
Additive effect (ADD): and .
When the interaction model, the matrix containing total risk values for each combination of genotypes, namely , and the value for the coefficient β AB have been defined, a set of six transcendent equations can be written with the coefficients of the logistic functions (except β AB ) as the unknown variables; these equations admit exactly one solution [14].
Modeling G×G
Notice that the superscript "I" is a reminder that the independent polygenic model has been assumed.
Using an independent polygenic model and a deformation procedure, epistatic interactions among DPLs can be modeled to obtain a matrix (where superscript "E" stands for epistatic) that still complies with constraints (4) and (5). In this approach, epistasis is modeled as a departure from the independent polygenic model via a change (positive or negative) in one or more entries of .
Once the quantities , , and are given, the constraints (9) and (10) define a convex region in R3×3 in which the elements are assignments for the entries of matrix . The specification of an epistatic model is, therefore, made through the definition of an increments matrix that complies with the constraints (9) and (10) and that also satisfies the positivity condition for . It can be difficult for a user to specify such a matrix in a way that does not violate the above constraints. However, the number of entries of that the user has to provide (paying attention to avoiding extreme or off-range values) can be reduced by letting the system find the remaining entries.
More precisely (in the two variables case), given the constraints of Eq.s (9) and (10) from one up to three entries for can be provided following the rule that any pair must lie on the same row or in the same column. If the user correctly provides three values, the system admits only one assignment for unspecified values of ; however, if the user provides less than three values, there are an infinite number of ways to choose the remaining entries of . In such a case, instead of randomly choosing a solution, a solution that maximizes an "objective function" is chosen. The problem of fixing the remaining values of can be represented as a continuous mathematical programming problem with decision variables that are the non-user-provided entries of and whose admissible region can be determined by Eq.s (9), (10) and (6).
An objective function can be used to minimize the variance of the set of ratios computed ∀(g a ,g b ) corresponding to non-user-assigned . Such a function is suitable for use in all situations in which the relationships between existing variables for which the user does not provide increments are to be maintained as far as is possible.
Establishing the disease status
Once the coefficients of the MLM are fixed, the disease risk for each individual in a population can be established by substituting the coefficients associated with the carried genotype into Eq. (1) and then by evaluating the resulting logistic function forthe exposure level of the environmental disease factor. Finally, to assign the disease status to each individual, the disease risk is compared with a random number drawn from a uniform distribution.
Availability and requirements
Project name: Gene-Environment iNteraction Simulator 2
Project home page: http://sourceforge.net/projects/gensim/
Operating system(s): Platform independent
Programming language: Python
Other requirements: SimuPop, OpenOpt, wxPython (optional)
License: GNU GPLv3
RA is the recipient of a fellowship from the Doctorate of Computational Biology and Bioinformatics, University "Federico II", Naples, Italy. The funders had no role in the study design, data collection and analysis, decision to publish, or in preparation of the manuscript.
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A Canadian Deep Dive
Consumers' changing needs are defined by a combination of variables:
1. WHAT (their intention)
2. WHERE (their physical location)
3. NOW (immediacy of their demand)
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- Research article
- Open Access
Duplication and independent selection of cell-wall invertase genes GIF1 and OsCIN1 during rice evolution and domestication
© Wang et al; licensee BioMed Central Ltd. 2010
- Received: 9 September 2009
- Accepted: 23 April 2010
- Published: 23 April 2010
Various evolutionary models have been proposed to interpret the fate of paralogous duplicates, which provides substrates on which evolution selection could act. In particular, domestication, as a special selection, has played important role in crop cultivation with divergence of many genes controlling important agronomic traits. Recent studies have indicated that a pair of duplicate genes was often sub-functionalized from their ancestral functions held by the parental genes. We previously demonstrated that the rice cell-wall invertase (CWI) gene GIF1 that plays an important role in the grain-filling process was most likely subjected to domestication selection in the promoter region. Here, we report that GIF1 and another CWI gene OsCIN1 constitute a pair of duplicate genes with differentiated expression and function through independent selection.
Through synteny analysis, we show that GIF1 and another cell-wall invertase gene OsCIN1 were paralogues derived from a segmental duplication originated during genome duplication of grasses. Results based on analyses of population genetics and gene phylogenetic tree of 25 cultivars and 25 wild rice sequences demonstrated that OsCIN1 was also artificially selected during rice domestication with a fixed mutation in the coding region, in contrast to GIF1 that was selected in the promoter region. GIF1 and OsCIN1 have evolved into different expression patterns and probable different kinetics parameters of enzymatic activity with the latter displaying less enzymatic activity. Overexpression of GIF1 and OsCIN1 also resulted in different phenotypes, suggesting that OsCIN1 might regulate other unrecognized biological process.
How gene duplication and divergence contribute to genetic novelty and morphological adaptation has been an interesting issue to geneticists and biologists. Our discovery that the duplicated pair of GIF1 and OsCIN1 has experienced sub-functionalization implies that selection could act independently on each duplicate towards different functional specificity, which provides a vivid example for evolution of genetic novelties in a model crop. Our results also further support the established hypothesis that gene duplication with sub-functionalization could be one solution for genetic adaptive conflict.
- Wild Rice
- Segmental Duplication
- Invertase Activity
- Alpha Amylase
- Synteny Analysis
Gene duplication has long been recognized to be an important way to provide a substrate on which evolution acts. The classical models that predict the most possible fate of one of the duplicate genes is to degenerate to a pseudogene or get lost from the genome due to vagaries of chromosomal remodeling, locus deletion or point mutation [1–5]. A less frequent fate of the duplicate genes is to gain a new function (neo-functionalization) when the other copy still maintains its original function. However, recent studies have indicated that the newly duplicated genes are often sub-functionalized from their ancestral functions held by the parental genes [6–8]. The sub-functionalization model (also referred to as duplication-degeneration-complementation model) explains that the duplicate genes are maintained in the genome relying on complementary degenerative changes in a pair of duplicate genes, such that the duplicate genes together retain the original functions of their single ancestor [1–5, 9]. During this process, the expression domain shifting is the most common character of duplicate genes. As a consequence, the duplicates acquired sub-functionalization and then were less constrained by selection than the single ancestor, which had to maintain the capacity to fulfill all functions. Therefore, selection could act independently on each duplicate and increase the gene function specificity [10].
Sequence variation plays an essential role in functional renovation of genes, however, the relationship between DNA variation and functional consequence has been enigmatic for the vast majority of genes in plant and animal kingdoms, despite an increasing number of studies have been reported. Crop species and their wild relatives with available genome information are becoming fascinating subjects for study of correlation between cryptic genetic variation and functional evolution, because they have undergone rapid diversification under intense artificial selection [11–14]. Therefore, investigating crop domestication genes will shed meaningful light on genetic variation that drives cultivation adaptation [15]. Rice was used by human about 11,000 years ago [16, 17]. It has been indicated that the divergence of indica and japonica predated rice domestication, suggesting that at least two genetically distinct gene pools of O. rufipogon were cultivated and subsequently domesticated [18–20]. During the long-term cultivation and domestication, tremendous diversity in rice has been selected by human, adapting to various ecosystems and agricultural management, in addition to high yielding characteristics, such as grain number and weight [12, 17].
Various evolutionary models have been proposed to interpret the fate of paralogous duplicates, but little is known about the mechanisms of evolutionary change in duplicate genes leading to functional novelty. Rice has been recognized as a cereal model for such a study, and recent studies have discovered that some rice genes have undergone adaptive evolution under domestication selection [17, 21]. We previously reported that the rice grain-filling gene GIF1 (OsCIN2) encoding a cell wall invertase (CWI) was most likely subjected to domestication selection [22]. Here, we report that GIF1 and another CWI gene OsCIN1 constitute a pair of duplicate genes with differentiated expression and function. Population genetic analysis showed that the two genes have experienced strong domestication selection, and interestingly, the target of selection in the GIF1 gene is the promoter region and that in OsCIN1 is the coding region.
Evolution of GIF1 and OsCIN1by gene duplication
Summary of nonsynonymous (Ka) and synonymous (Ks) substitutions in OsCIN1 and GIF1
p-value (fister)
Sub-functionalization of GIF1 and OsCIN1by expression differentiation
Sub-functionalization of GIF1 and OsCIN1 enzymes
The difference in the kinetics parameters of enzymatic activity might result from the amino acid substitutions, in particular the GIF1 and OsCIN1 proteins contain Ala and Gly residues in the NDPNG domain (motif), respectively (Figure 3C). Phylogenetic reconstruction (Figure 1A and 3C) revealed that the Gly-26-Ala substitution occurred after the GIF1 and OsCIN1 duplication. The crystal structure of the Arabidopsis CWI indicated that the conserved NDPNG domain is critical for CWI activity [30–32]. However, GIF1 and the homologous maize Mn1 contain a NDPNA motif instead of the NDPNG motif that presents in OsCIN1 and other CWIs, suggesting that the segmental duplication predates maize and rice differentiation. Furthermore, the crystal structure showed that Asp-239 interacted with Lys-242 and both the two amino acids played a crucial role in the transfructosylation process and interacted via H-bonds with the bound substrate [32]. A Thr-241-Arg substitution in the Asp-239/Lys-242 region occurred in GIF1 as well as in Mn1. It is noteworthy that the mutation in the Mn1 gene also caused shrunk grains [33]. The synteny between the GIF1 and Mn1 genome regions suggested that they could be orthologues (Additional file 3). These structure differences might contribute to different enzymatic kinetics of the GIF1 and OsCIN1 proteins. Together, these results suggested that GIF1 and OsCIN1 were subjected to sub-functionalization, or that GIF1 (probable Mn1 too) might have neofunctionalized, albeit we do not know the ancestral function of the CWIs.
Different phenotypes of GIF1-OE and CIN1-OE plants support sub-functionalization of GIF1 and OsCIN1
Evidence of OsCIN1domestication-selection
Cultivars and wild rice germplasm used in this study
Sample name/IRGC no.
Variety name
Jhona 349
Haginomae Mochi
Gie 57
Bico Branco
Kitrana 508
Canella De Ferro
Maintmolotsy 1226
South Korea
Leung Pratew
Southern Asian indica
Southern Asian indica
Southern Asian indica
Dongxiang, China
O. rufipogon
Yunnan, China
O. rufipogon
Guangdong, China
O. rufipogon
Hainan, China
O. rufipogon
Guangxi, China
O. rufipogon
O. rufipogon
Papua New Guinea
O. rufipogon
Sri Lanka
O. rufipogon
O. rufipogon
O. rufipogon
O. rufipogon
O. rufipogon
O. rufipogon
O. rufipogon
O. rufipogon
O. nivara
O. nivara
O. nivara
O. nivara
O. nivara
O. nivara
O. nivara
Sri Lanka
O. nivara
O. nivara
O. nivara
HKA tests of the OsCIN1 and GIF1 locia
polymorphism site number
sequence length
p value
To narrow down the selection target in the OsCIN1 gene, we examined all the variations in the OsCIN1 genome regions, and found that an amino acid substitution (Arg-212-Leu) almost fixed in the rice cultivars, indicating that, unlike GIF1 which was selected in the promoter region, this site in the coding region could be the target of artificial selection in the OsCIN1 gene (Figure 5B). Further functional characterization of this site will provide more evidence to address how this site has contributed to OsCIN1 function in cultivated rice.
Gene duplication and adaptive conflict
Gene duplication plays a fundamental role in organism evolution by providing genetic materials from which novel functions can arise. Large numbers of duplicate genes were found in genomes, which contributed greatly to the genome structure and function evolution [1–5, 39]. In general, the duplicate genes have two fates: first, the duplicate gene lost its function due to chromosome remodeling, deletion, and point mutation (known as non-functionalization); second, the duplicate gene retained for the maintenance of ancestral functions [1–5]. According to adaptive conflict model, adaptive mutations could be prohibited in the case of multifunctional genes, or one mutation that can optimize one function, may compromise the other functions, this mutation will be prohibited [6, 40]. The adaptive conflict could be solved by sub-functionalization of duplicate genes. In this case, the duplicate genes would be less constrained and be able to evolve new functions under selection [41, 42]. With this scenario, it is common that one gene could have multifunction in nature [40, 43].
Independent selection of GIF1 and OsCIN1mutations
The sequenced genomes of Oryza sativa, Arabidopsis thaliana and Populus trichocarpa all contain a family of cell wall invertases [44–46], some members of these gene families were reported to be involved in growth and development, disease resistance, stress responses and cell death, suggesting that the CWI gene families might have undergone sub- or neo-functionalized in these species.
Through genomic synteny analysis, we showed that GIF1 and OsCIN1 derived from a segmental duplication from an ancestor, most likely during genome duplication in grass species. After duplication, GIF1 and OsCIN1 have evolved to gain divergent functions with different expression patterns and enzymatic kinetics parameters through accumulating mutations in cultivated rice. In contrast to GIF1 on which domestication selection mainly occurred in the cis-regulatory region (Figure 5A), the artificial selection occurred mainly in the coding region of OsCIN1 (Figure 5B). Therefore, both GIF1 and OsCIN1 were most likely subjected to domestication selection, resulting in a cultivated GIF1 locus for better harvest, although the biological importance of OsCIN1 in domestication remains enigmatic. With this scenario, GIF1 and OsCIN1 may provide a good genetic model to demonstrate how duplicate genes could evolve and be artificially selected independently during crop domestication with divergent functions derived from accumulation of mutations in the regulatory and coding regions respectively, adding to those systems reported [47, 48].
Differential biological functions of GIF1 and OsCIN1
GIF1 is mainly expressed in seed vascular tissues and controls sucrose unloading for starch synthesis at the early grain-filling stage [22]. Overexpression of the GIF1 gene produced plants with marked defects both in grain-filling and development, indicating that over-activity of the GIF1 enzyme disrupts sugar homeostasis, a process important to normal grain and plant development. In contrast, OsCIN1 has lower CWI activity compared to GIF1 in the transgenic plants (Figure 3). Consistent with this, no obvious phenotype was observed in CIN1-OE plants except pre-harvest sprouting (Figure 4). Interestingly, OsCIN1 might be involved in pathogen defense and stress response [23]. It has been reported that sugars interact with signaling pathways mediated by phytohormones such as GA and ABA during seed germination and seedling development [34, 49], which are also involved in stress responses. Preharvest sprouting of the CIN1-OE seeds may implicate a role for OsCIN1 in sugar-mediated alpha amylases activation [34]. However, detailed experiments are needed to dissect the OsCIN1 function.
Gene duplication and functional divergence contribute greatly to genetic novelty and adaptive evolution. However, molecular basis of selection and functionalization of duplicate genes remains largely unknown. Based on a set of data including population genetic analysis, fine sequencing of wild rice BACs, phenotyping of transgenic plants and analysis of gene expression and enzymatic activity, we provide a line of evidence that the two rice CWI genes GIF1 and OsCIN1 are a pair of duplicate genes and have been subjected to sub-functionalization during evolution or domestication selection. Therefore, duplicate genes could be independently selected towards different functional specificity, either on promoter for different expression pattern or on coding region for different protein function/activity. Our study provides a vivid example for evolution of genetic novelties in a model crop. The interesting phenotype of preharvest sprouting OsCIN1-OE plants suggests that OsCIN1 overaccumulation might disturb sugar balance during seed germination.
Duplication and synteny analysis
The 500-kb radiuses of the GIF1 and OsCIN1 regions were scanned for homologous pairs. A homolog pair was defined as a single nr-KOME cDNA and its blastn homolog. A total of 18 homologous genes in both sides of the GIF1 and OsCIN1 loci were compared to establish linearity.
Sequencing and evolution analysis
To investigate the selective forces acting on GIF1 and OsCIN1 on the molecular evolution scale, we estimated the statistic Ka/Ks using the re-sequencing data (see below) and the maize Mn1 and Incw1-1 as the outgroup sequence, where Ka was the number of nonsynonymous substitutions per nonsynonymous site and Ks was the number of synonymous substitutions per synonymous site [27]. Ka/Ks values significantly less than 1 were often taken as evidence of constraint. The mean Ks of nine pair homolog genes, including GIF1 and other eight genes (Additional file 2), in the GIF1 and OsCIN1 regions were used to estimate the duplication time. Two BAC clones of O. punctata (BB genome) from the OMAP project http://www.omap.org/ containing GIF1 and OsCIN1 respectively, were sequenced.
Analysis of OsCIN1 and GIF1 domestication
We deeply analyzed the OsCIN1 and GIF1 sequences from the re-sequenced genomes of 25 rice cultivars and 25 wild rice germplasm (Table 2), which has been done in Dr. Wen Wang's group, using Solexa technology (data not shown). Haplotype diversity was calculated for nucleotide diversity (π), and Tajima's D- statistics were calculated with DnaSP version 4.0. The gene tree was created using MEGA software [50]. The sequences then were aligned. The 2-kb up-/downstream genome sequences and the GIF1, OsCIN1 coding sequences were used for HKA test as described [36]. Sequences from wild rice O. punctata (BB genome) from the OMAP project http://www.omap.org/ were used as outgroups for the HKA test. The DNA phylogenetic tree was constructed by neighbor-joining method using MEGA. The known or predicted CWI genes with high sequence similarity to GIF1 from Oryza sativa, Lolium perenne, Hordeum vulgare, Dendrocalamopsis oldhamii and the recently released Zea mays and Sorghum bicolor genomes were used in this study.
Development and growth of OsCIN1-OEtransgenic plants
The full-length OsCIN1 coding sequence was PCR-amplified from ZH11 cDNA by using the primers 5'-TCTAGTACAAAACAATGGGGACTC-3' and 5'-CGGAAAACCTCTTTATTATCTGTA-3'. The amplified fragment was subsequently cloned into the vector 35S-C1301 and transformed into ZH11 to generate 25 independent ectopic expression lines as described [22]. All transgenic materials were assayed in the second (T1) or third (T2) generations with 10-24 sibling plants grown in the paddy field to ensure agronomic traits.
Invertase activity assay
The caryopses were ground in the extraction buffer, and the extraction was centrifuged at 12,000 g for 10 min. The pellet was washed twice then re-suspended in the extraction buffer. Insoluble invertase activity was assayed as described [22].
RNA preparation and analysis
Total RNA was prepared from rice tissues using TRIzol reagent according to the manufacture's protocol (GIBCO BRL). For RT-PCR, 1-5 ug total RNA was used for the first-strand cDNA synthesis with the SuperScript III System (Invitrogen). RT-PCR analysis of GIF1 and OsCIN1 was performed with the primers [22, 23].
Accession numbers
All sequences have been deposited in GenBank under accession numbers GU797900-GU798049.
We thank M. Long and W. Capoen for critical reading of the manuscript and helpful suggestions; R. A. Wing for O. punctata BAC clones. X. Zhang, L. Zeng and S. Ye for rice growth. This work was supported by grants from the Ministry of Science and Technology of China (2007AA02Z162, 2007AA10Z187), grants from the National Natural Science Foundation of China (30721061) to Z. He
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- Sherson SM, Alford HL, Forbes SM, Wallace EG, Smith SM: Roles of cell-wall invertases and monosaccharide transporters in the growth and development of Arabidopsis. J Exp Bot. 2003, 54: 525-31. 10.1093/jxb/erg055.View ArticlePubMedGoogle Scholar
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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About RBC > Corporate Profile > Awards and Recognition > RBC named as one of the Global 100 > Most Sustainable Corporations in the World
TORONTO, January 23, 2013 — For the ninth consecutive year, RBC was named one of the Global 100: Most Sustainable Corporations in the World. RBC was the only Canadian bank included on the list.
"We are immensely proud of our performance at RBC and today's recognition as a global leader in sustainability and I believe it's time for more corporations to raise the bar," said Gordon M. Nixon, president and CEO, RBC. "I am confident that companies that find balance between their economic, social and environmental responsibilities and opportunities will create substantial long-term value for all their stakeholders. This is the new bottom line."
The Global 100: Most Sustainable Corporations in the World is an annual project initiated by Corporate Knights. Launched in 2005, the Global 100 is announced each year at the World Economic Forum in Davos, Switzerland. The objective of the Global 100 list is to recognize successful global corporations that have been world leaders in environmental, social and governance issues. To see full methodology, click here.
At RBC, we focus on six priority areas where we have an impact as a financial services company: corporate integrity, economic impact, marketplace, workplace, environment and community. Our pledges and commitments in each of these priority areas comprise our overall corporate responsibility strategy.
To learn more about RBC's corporate responsibility programs and performance visit: rbc.com/community-sustainability.
About RBC Community and Sustainability
Royal Bank of Canada (RY on TSX and NYSE) and its subsidiaries operate under the master brand name RBC. We employ approximately 80,000 full- and part-time employees who serve more than 15 million personal, business, public sector and institutional clients through offices in Canada, the U.S. and 49 other countries. RBC is recognized among the worlds financial, social and environmental leaders and is listed on the 2012 - 2013 Dow Jones Sustainability World Index, the DJSI North American Index, the Jantzi Social Index and the FTSE4Good Index. RBC has been named one of Canadas Greenest Employers and one of Canadas 50 Most Socially Responsible Corporations.
RBC supports a broad range of community initiatives through donations, sponsorships and employee volunteer activities. In 2012, we contributed more than $95 million to causes worldwide (more than $64 million in donations, and $31 million in sponsorships ).
For Financial highlights, annual reports, quarterly information and other financial results, see our Investor Relations site.


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Thinking Pink
Friday, March 17, 2017
Sammy Turns Six
Suffice it to say that Sammy had a pretty great day on Monday when he turned six.
Loved this shot of Daddy and Sammy.
Not as in love with this picture, but at least Sammy is cute.
We took him over to the local dog park after work and he had the best time. Um, why don't own this shirt?!?! It really should be in my possession.
The birthday boy!
Coogan Family.
After his dinner of two In & Out patties
(spoiled puppy)
, he had a lovely puppicino
(basically a tiny cup of whipped cream).
He was the happiest dog in all the land. Happy birthday, Sammy!
Posted by
Pinky Lovejoy-Coogan
Friday, March 17, 2017
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Rampant Rossvale made club history on Saturday at the expense of Stonehaven FC as they stormed into the last 16 of the Scottish Junior Cup for the first time in the club's history.
The Bishopbriggs side cruised to a 6-1 victory over Hive to book their place in the fifth round draw.
Both teams were glad to get the ETHX Energy Scottish Cup fourth round game underway following last weekend's disappointing late call off due to snow.
The opening exchanges were somewhat frantic as each team attempted to gain dominance over the other with both creating scoring opportunities.
But Rossvale were beginning to take control of the game and made the breakthrough after 20 minutes.
Rossvales Jamie Hunter evaded two challenges before being scythed down inside the box and Alex Cassells gave Stark no chance from the spot.
The Rossvale advantage was doubled 10 minutes later when Joe Bradley's looping cross was met sweetly by Ryan Coll who bulleted his header into the gaping net.
Stonehaven began the second period slightly better but Rossvale weathered the storm, although they had to wait until the 73rd minute to increase their lead.
Rossvales Jay McKay got in behind the Stonehavens defence before cutting the ball back to Bradley and from his cross Cassells rocketed home a header.
Stonehaven gained themselves a lifeline four minutes later when Rossvale failed to clear their lines and the ball broke to Stephen Robertson who curled a right-foot shot home from just outside the box.
This seemed to sting Rossvale into upping their game once again and the three-goal advantage was restored in 84 minutes.
Hunter's cross for the home side eluded everyone except Chris McQuade who had made excellent ground to get to the back post and with the inside of his right foot put the ball high into the Stonehaven net.
Rossvale's fifth goal was almost a mirror image. This time Corey Irvine was the provider from the Rossvale right hand side with Lewis Kinnaird the executioner at the back post.
The scoring was finished when Stark saved an excellent solo effort by McQuade but the ball rebounded to Cassells who sidestepped a defender and calmly placed the ball home to complete his hat-trick.
Final Score Rossvale 6-1 Stonehaven


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We all know Alpharetta's weather can be unpredictable. High winds, heavy rain, and hail can all leave unsightly and harmful damage to your roof. That's why you call the professionals at Rackley Roofing and Supply Inc. to inspect and repair your roof after any major storm or roof damage. With top-of-the-line products and expert repair knowledge, our professional and friendly contractors will help to get your roof back to new in no time!
The best way to determine the extent of any natural wear or damage to a roof is to hire a roofing contractor who has the expertise to give you a honest inspection report. From loose or broken shingles and tiles, to cracks in pipes and damaged gutters; we look for it all. If you're in need for professional roof inspection, contact the professionals at Rackley Roofing and Supply Inc. to take care of you.
If you're looking for professional and reliable roofing service, look no further. Our roofing professionals inspect, repair, and replace roofs all across the greater Alpharetta area. We do shingle, tile, metal, flat, and custom roofing. Known for our professionalism and attention to detail, our full-service roofing company aims to improve the look and value of your home while saving you time and money!
Gutters and downspouts are vital to your home and your roof's longevity. Transporting excess water away from your foundation walls helps to keep your home dry and free from unnecessary water damage or flooding. For rainy days in Alpharetta, it pays to have an efficient and reliable drainage system. Using the finest and most durable gutter material, Rackley Roofing and Supply Inc. will fit your home with the appropriate gutter and downspout system that's not only effective but looks good too!
When you hire the team to waterproof your home, you're putting your trust in a team that will do it right, the first time. The pros at Rackley Roofing and Supply Inc. are THAT team. Our knowledge and professionalism will ensure that your property is protected against water damage for years to come. We know the importance of having a water-tight structure, and we ensure the most complete and thorough job -- without breaking the bank. When you're in need of expert service in Alpharetta area, give the pros a call.
As one of the most popular roofing types, shingle roofing comes in several different options, so you and your expert roofing contractor can decide the best roofing material for your home. Slate, asphalt, wood or shake -- depending on your location, your budget, and your home, these shingles can make wonderful additions to the condition and aesthetic of your home. Serving Alpharetta with expert knowledge and craftsmanship, contact Rackley Roofing and Supply Inc. for all your shingle repair and re-roofing needs.


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Add Music to Your Site
Websites are meant to entertain and one thing when I am looking on the internet that entertains me is music! Here are the steps on how to get background music onto your website!
First you need to choose a music file, you can choose an mp3 file but it may take longer to load. After you choose what song you would like to add you will add this code to your html on which ever page you would like it to play on:
<embed src="musicfile.wav" autostart="true" loop="false"
You will need to enter your music source where is say "musicfile.wav"!
The autostart and loop codes are also important. For autostart that means if you put "true" as soon as you page loads it will begin to play music, and if you select "false" the viewer will have to manually make the music play! Loop means that if you put "true" then the song will continue to play over and over again and if you select "false" it will play once and then stop!
This is what your end html code should look like:
<ember src="www.yoursite.com/music/musicfile.wav" autostart="true" loop="false" >
Hope this helps!
-Madison Knapp


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Following the pre-emptive victory at Antietam President Lincoln issues the Emancipation Proclaimation, freeing slaves in states or portions of states still in rebellion on January 1, 1863. Few people realize that Lincoln didn't free the slaves with this document. Rather, the slaves that still were in areas controlled by the South were free. Many looked at this as Lincoln opting out of taking a stand on slavery. Oh well, I am off again, stay tuned for more.
Saturday, September 22, 2007
The Emancipation Proclaimation was issued today
Labels: Abraham Lincoln, Emancipation Proclaimation, Lincoln
2 comments:
Lincoln new well that The Emancipation Proclaimation was a letter of principles, rather than absolutes. He new that the south wouldnt be able to recover (if they lost) if all slavery was abolished. It would be devastating on the moneyflow.
Most of he`s writing does make it clear that he is pretty sincere in he`s view on slavery, but that he would have to lower he`s targets, if there was going to be a good growth, and possibility for properity after a long war.
Its pretty fatalistic, but quite nessesary.
- September 23, 2007 at 5:38 PM
- Mike said...
I agree, slavery had to be destroyed.
- September 29, 2007 at 11:29 PM
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There are lots of choices were to play online bingo for real money and it is just a case of picking the right place for you. Of course bingo is bingo and will be predominately the same wherever you decide to play. That said, the trigger playing for real money profits is to make sure you sign up for the right online bingo website.
Soon as you sign up for your online bingo site you will see that you will have lots of different options for card purchasing. In most cases you will be able to purchase cards from a few cents all the way up to a few dollars per card. You can win substantial amounts of money playing online bingo providing ufologist a few simple guidelines. It is very common for people to be winning hundreds of dollars by buying even the cheapest of cards for a few cents a time. Enjoy the best explainer videos at ExplainerVideo.me.
Having the ability to win large amount of money playing online casino games is actually quite realistic. You do however need to make sure that you sign up to the correct online bingo provider in order to get the best possible deal. You will soon see that there are lots of different sites available but personally I take the recommendations of others before signing up for an online bingo website. Will also find that when you've chosen your online bingo Hall there are lots of different options available to you.
People are still wary about entering card details into websites. However in this day and age Security has gone a long way and you can rest assured that providing the website is secure then so will your credit card details be. If however you are still concerned about entering card details most of the websites will give you the opportunity to add money to your account using services such as Skrill or PayPal. By using PayPal you know that you're covered by their own guarantees.
There is no doubt that playing bingo online for real money is getting more popular every year. One such benefit would be your initial bonus. Once you've decided that you want to play for real money then you will get a bonus of real money in itself.
I personally prefer the online bingo bonuses that will match my initial deposit with the same again. The way this works is if you were to deposit $200 into your betting account then the online bingo website would match this with a further $200. There will of course be certain caveats that you need to play a certain amount of games before you were able to withdraw your bonus. This is purely to protect their own interests.
For you sign up from online bingo site you need to make sure that whichever one you pick you're going to get the best deal possible. In order to do this I use recommendations of others. I use a website called onlineblingo.com who have trialled the sites and rated them based on their experiences. I've been using these guys the years and I trust the recommendations to make sure that I can stand the best chance of winning.
Online bingo is extremely popular because not only can you win good money but it is also extremely good fun. You can play from just a few cents time all the way up to a few dollars per card. If you're playing just for fun then you could have a great evenings gaming for a couple of dollars. But, when you win you can win big even from a card that is only cost you a couple of cents.
I've tried to mention a few strategies here the will help you boost your income from playing online bingo. I've mentioned the hints and tips below because of been doing this for quite some time now and frequently walk away with considerable profits.
While playing online bingo is generally a game of luck do try and stick consistently to the same method. My recommendation is to pick up batch of numbers that mean something to you. For me it is certain dates of importance. The second thing you need to do is select your own cards based upon these numbers. Don't let the algorithm pick the cards for you.
In order to stand the best chance of winning it pays to do a little research before starting to play. You have two choices when you're playing you can either go with the numbers which are frequently picked or go with probability and pick the numbers which is not appeared for quite some time.
Try to play real bingo online games when there aren't too many other players. When you have a lot of players in a single game the chances of you winning are considerably reduced. There's no doubt about it the games are far more fun when there is more people playing but your chances of winning are considerably reduced.
One thing you need to be aware of is don't be drawn in to the attempt Asian of playing with as many cards as you possibly can. The more cards you're playing with then the harder it becomes to manage. The more cards you have the more likely you are to make a mistake or miss something. There is no doubt you need to be skilful to effectively manage lots of cards. My advice is to start with one or two cards and then work up as your skills increase.
When you're playing online bingo the most important piece of advice I can give you is to set a budget and stick to it like stone. Most important things and never ever bet more than you can afford. Just following the simple steps here he stand of far better chance of winning although never taken for granted that winning is guaranteed.


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Things go too far sometimes in NASCAR and other sports as well.
Take The Malice in the palace for example, where several NBA superstars got into a fight with each other and eventually members of the onlooking crowd. While things were eventually broken up and players sent to their respective locker rooms, it was a moment that exemplified not only the worst in the players, but the worst in the fans as well for throwing drinks at the players.
With that being said, its no secret that every sport has these types of moments throughout their history and unfortunately, NASCAR is no exception tp rule. Of course a lot of the things NASCAR fans have done will come no where the near the level of insanity as The Malice in the palace, but NASCAR has certainly had their fair share of these controversial moments.
Below are five such examples of times that fans went too far and made the sport look bad in the process. Let us know your thoughts in the comments below and tell us what you thought the worst moment was as well!
Five things you need to know about Kevin Harvick cheating at Las Vegas


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Click here to view handouts from 2010 - 2013 conferences. (Under agenda, then archives)
Click here to view past conference highlights.
Presented at the 18th Annual Research Conference
We're sorry, handouts are not available for this presentation.
Session Number: 1000 Room: Salon E & F
Presenting: Robert Lutton; Josephine Bejarano; Kim Foy
All Authors for this paper: Robert Lutton; Josephine Bejarano; Kim Foy
Presentation Type: poster presentation
Synopsis: Issues around raising a child with behavioral health involvement are easily understood by other parents making that same journey, but can be challenging for system professionals to understand from the perspective of the parent. This can create barriers in integrating parents into the case management model, and using parents as active designers of their own interventions, supports, and services. By teaming the case manager with a peer parent, a model is created that allows the peer to draw the parent into the system of care process through the nature of the peer relationship. The family is then able to more fully utilize the structure created by the case manager. If the family is not infused into the case management process outcomes are affected negatively, no matter how sound a structure the case management team may have built.
Session Time: 6:00 PM - 8:00 PM
Presentation Time: 6:00 PM - 8:00 PM


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Universal Pictures has just announced a special sneak peak showing of footage from Sasha Baron Cohen's highly anticipated new film "Bruno" at SXSW- Fantastic Fest on Sunday, March 15th at 11:00pm in Austin, Texas.
This will be the VERY FIRST audience anywhere to see footage from the film. No badge is needed and its free, but go early to guarantee admission.
Those interested should show up at the Alamo Drafthouse, 1120 S. Lamar Blvd this Sunday.


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(Yes, thank you, Mary.)
Anyways. I'm going to skip over the whole sorry-I've-neglected-this-blog-for-so-long thing, since I tend to go through that every time, and get down to business.
Apparently I am an Elegant Blogger, for I have been given this award by three people (Molly, Emma Jane, and Miss Jane Bennet), and if that isn't proof of one's elegance, what is? (Thank you very much, girls!)
- When you receive the award, link back to keepcalmandsparkle1099.blogspot.com and the blog that nominated you (For the Beauty of the Earth and Classic Ramblings--Molly's is private.)
- Display the award button in the post
- Answer all of the 12 questions given in this post (do not make your own questions)
- Nominate 8 bloggers
- Notify them that they have been awarded
1. What made you decide to start blogging?
Ah! Good question. I'm sure I've explained this somewhere or other, but I'll give a quick rundown here... after "meeting" Jane Austen, I obviously grew more and more fanatical about her. Well, I only had about one friend who even qualified as a Janeite, and I think she was probably getting a little tired of my incessant rambling and questions. (I was sort of her protege for a while, but we weren't very close or anything.) Then of course there was my family who had to hear all about it, and not only could I tell they were growing a little weary, *I* was growing weary of their Lack of Understanding in the matter. I actually started out making up a Regency/Jane Austen magazine and was thinking of giving it to a few people, but this idea morphed into starting a blog, which I knew very little of, but my oldest sister had one and when I finally got the permission of my mamma, Regency Delight came into being on New Year's Day in 2011. ;) Since then my thirst for Understanding and Interested People has been fulfilled far beyond my hopes.
Yeah, my rundowns never end up being very quick. *cough*
2. What is your fashion style?
Mmmm... fashion style. Well. How does one describe that. :P I like old-fashioned and vintage looking things... (if I could only do my shopping in the 1930s-1950s, it would be bye-bye modern wardrobe!) I tend not to like loud-looking things (bright colors, funky, etc.) and I'm very picky about modesty. ;) However, I like things to be attractive and not boring. Heehee. ;) Sometimes the Chic and Classy look appeals to me but I rarely attain it myself, haha. I don't like to look dorky, but if what I like is what somebody else thinks is dorky, they can just go jump in the Pemberley pond.
3. What is something none of your followers know about you?
None of my followers? Good gracious! You realize, of course, that that covers my twin, a close friend who has known me for over eight years, and even a sister who has known me all of my life. Well, well. Time to bend the question a little, I think. How about excluding anybody I've ever seen in real life. This works? Good. Okay. Let's see here... well, I attended a cousin's wedding last week so that brings to mind the fact that I've been in four weddings. Twice as a flower girl, once as a bridesmaid, and once as a lantern carrier--which was rather in place of a candle-lighter--a rather small and insignificant role especially considering there were five others (it was a very large wedding in which the bride, another cousin of mine, tried to include a lot of people :P), but I got to wear a cape and look old-fashioned, and the whole thing was very Christmassy (including the music!), so it's all good. :D
4. What are some of your blogging goals?
#1: To write more posts.
#2: To publish more posts.
#3: To post more. Besides for jumping on any tag that comes along as a good excuse.
And not to continue losing followers at the rate of about three in two weeks. *cough*
5. Where is your favorite place to shop?
Anywhere that doesn't have loud music and where we don't stay very long. :P (Yeah, I'm not really a shopping person...) As far as Just For Fun goes, antique stores and of course bookstores are always nice. :)
|I'm just sticking this picture here randomly because it is Elegant.
6. What would your ideal amount of blog followers be?
Uhhh... hmm. Well, how many does Amy have?
(For anybody who reads this post when it's outdated, her current count is 310. Hahaha.)
No, I'm not really that competitive, nor do I begrudge the dear girl her superfluity of followers. I am only being silly. :D
7. What are your talents?
Cough, wow. Well, I'm afraid they aren't worth telling. But if you let me tell you what I imagine my talents to be, you'd find it a lot more interesting. (Anne of Green Gables, anyone?)
8. Are you a leader or a follower?
Neither one, really... but in general I am very much against being a follower. ;)
9. What is one of your favorite quotes?
Just... one? Randomly like that? Uhhh... and of course, when I try to think of a totally random quote, my mind becomes suddenly blank. >.<
Ah... here. Here's one I tend to say a lot.
"If I am a wild Beast, I cannot help it. It is not my own fault." ~Jane Austen (in a letter to Cassandra)
Anybody else out there who would love to have Jane Austen as a pen pal? She's so funny! That sounds really silly and flippant, almost, to say about a Great Author--but hey, it's true.
10. Do you have a favorite book or book series?
Heh, heh... Pride and Prejudice by Jane Austen.
What can I say? That's my natural reaction. Forgive me if it's boring for me to keep having the same answer to this question. :P
11. Out of all the of the synonyms for elegant, which would you describe yourself with (smart - stylish - dressy - graceful - dainty - fine)?
All of them, dahhhling. *sniffs and tosses hair dramatically*
Um... just kidding.
Well, I asked the person who I think knows me best, and she says "graceful with a touch of old-fashioned." Will that do?
12. What is your favorite flower?
There are times, I confess, when it would be useful to be a TSP fan, because then I could just say a scarlet pimpernel is my favorite flower. ;) Anyway, I really should decide this at some point so I can have an answer for people when they ask. Something... delicate. And fragrant. Not too large. Preferably with petals in pastel shades. If you discover what this flower is, do let me know. ;)
Now, as far as awarding people... I'm going to cheat, and not actually tell people I awarded them. But if they read this post, they will know, won't they? :D Sooo...
Maria Elisabeth
Miss Elizabeth Bennet
Miss Laurie
And this has nothing to do with this post at all, but--


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Altenew Build-A-Flower Blog Hop Winner
Hello friends, I hope you're all doing well! I'm here to announce the randomly selected winner from the comments left on my post for the Altenew Build-a-Flower Blog Hop! List of giveaways and winners can always be found here (I try to update at least weekly).
Congratulations to Angie Hall!
Please contact Altenew at email@example.com by March 15, 2018 with the subject line "Altenew Build-A-Flower Blog Hop Winner from Rubeena's Blog" for your discount code. Thank you to everyone who left me wonderful comments. I appreciate the support!
1 Comment
raggiodiluce82March 3, 2018 at 5:20 pm
Congrats Angie!!!


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Aston University is a public research campus university situated at Gosta Green, in the city centre of Birmingham, England. Aston began as the Birmingham Municipal Technical School in 1895, evolving into the UK's first elite College of Advanced Technology in 1956. Aston University's Royal Charter from Queen Elizabeth II was granted on 22 April 1966.Aston was ranked by QS as the world's 47th best university under 50 years old and as the world's 51st best university based on employer reputation. Aston is a pioneer of integrated placement year concept, with more than 70% of Aston students take placement year, the highest percentage in the UK. Aston has performed well in the National Student Survey, with its highest ever score of 89% in 2013.
Source: dbpedia


text: > 3) Kernel RPMS are here: > http://fs12.vsb.cz/hrb33/el5/hrb/stable/i386/RPMS/repodata/ > 4) Well, maybe I could create CD install media deltas. > 5) And of course ICH9 patched CentOS is not supported by CentOS > team/community. > Regards, > David David, Do you think you can put up the x86_64 of the Kernel RPMS? btw - I also sent you an email since the md5.txt does not match the delta dvd and I am getting block size not matching the image length in patching. thx __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com


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Hiding the Contact Button
When you sign up to HelpDocs, you'll automatically be given a contact button (which triggers a contact form) in the upper-right hand side of the header. This way, it's easy for customers to get in touch if they can't find the answer they're looking for.
If you're using one of our Contact Form Integrations, like Drift or Intercom, we automatically make the Contact button trigger a new conversation in the live chat window.
If you no longer want the contact form button to display, simply leave the form under 'contact form email' blank. Once you hit save, it'll remove the button completely.


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New World Record Rooftop Solar Power Array to be Built at Milan Trade Fair
Entrance to Fiera di Milano, photo: ...FX... via flickr
Milan will soon be able to boast that it hosts the world's largest rooftop solar power array, taking the crown from Zaragoza, Spain and its 12 MW array on top of a General Motors factory. The new array will be built on top of the Milan Trade Fair, cover about 270,000 square meters (2.9 million square feet), and have a capacity of 18 MW, Reuters reports: No word on exactly when construction will begin, or even who the final developer will be (a short list of potentials is currently being vetted), but the whole thing is expected to cost in the range €70-80 million ($97-111 million) and be completed by the end of 2010.
The Milan Trade Fair will not initially own the system, rather receiving rent from the solar power array's developer in exchange for the right to sell the electricity the system produces. However, after 20 years ownership of the array will be transferred to the Trade Fair.
It hasn't been decided how much (if any) of the power produced by the array will actually be used by the Trade Fair itself.
Largest US Rooftop Solar Power Array
The largest rooftop solar array in the United States is currently a 2.37 MW array on the Atlantic City convention center. The Port of Los Angeles has plans to install some 10 MW of rooftop solar power, to be completed in the next five years.
via: Reuters
Solar Power
600,000 Square Foot Solar Power Rooftop Completed by Southern California Edison
Port of Los Angeles to Install 1.16 Million Square Feet of Rooftop Solar Panels (10 MW) Over Next Five Years
New York State Aims for 100 MW of Rooftop Solar Power by 2015