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PubMed2004

Genetic assimilation can occur in the absence of selection for the assimilating phenotype, suggesting a role for the canalization heuristic.

Genetic assimilation occurs when an acquired trait loses dependency on its environmental trigger and becomes an inherited trait. According to the standard quantitative genetic model for genetic assimilation, the trait is determined by the contributions of multiple genes. Trait expression occurs at a lower threshold with the trigger. Selection for the trait in the presence of the trigger increases the frequency of the trait-determining alleles. Eventually these alleles become frequent enough to breach the higher threshold for expression in the absence of the trigger. This loss of dependence on the trigger signifies genetic assimilation. Here I show that genetic assimilation can occur in the absence of selection for the trait in an evolutionary simulation of a gene network model. This contradicts the prediction of the standard quantitative genetic model, but is consistent with an explanation in terms of the canalization heuristic.

Biological Evolution↗
PubMed2001

A comparison of genetic network models.

With the completion of the sequencing of the human genome, the need for tools capable of unraveling the interaction and functionality of genes becomes extremely urgent. In answer to this quest, the advent of microarray technology provides the opportunity to perform large scale gene expression analyses. Recently, genetic networks were proposed as a possible methodology for modeling genetic interactions. Since then, a wide variety of different models have been introduced. However, it is, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: (1) inferential power; (2) predictive power; (3) robustness; (4) consistency; (5) stability and (6) computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.

Classification↗
PubMed1993

Genetic predisposition to hypertension and microvascular injury in the remnant kidney model.

Genetic factors have been implicated in the development and progression of glomerulosclerosis and nephron loss in both experimental animals and in humans. The influence of a differing genetic predisposition to hypertension was examined in the remnant kidney (RK) model of progressive glomerulosclerosis. Dahl salt-sensitive (S) and salt-resistant (R) rats fed a normal salt diet underwent either sham surgery or approximately 5/6 renal ablation and were studied 2 to 3 weeks later. Renal ablation resulted in significantly more severe hypertension in RK-S rats (205 +/- 6.3 mm Hg, mean +/- SEM) compared with RK-R rats (153 +/- 3.5 mm Hg; p < 0.01). Renal autoregulatory ability, a protective mechanism against renal transmission of systemic hypertension, was normal in both S and R rats with intact renal mass. Renal ablation resulted in similar impairments of renal autoregulatory ability in both strains. However, striking differences in the severity of renal microvascular and glomerular injury were observed between the remnant kidneys of S and R rats, paralleling the differences in the severity of hypertension. The RK-S rats exhibited acute fibrinoid necrosis and thrombosis of glomerular capillaries, arterioles, and small arteries, whereas only mild segmental glomerulosclerosis lesions were observed in a small percentage of glomeruli in the RK-R rats. The intact kidneys of both strains were essentially free of glomerular or vascular lesions. These data suggest that a genetic predisposition to hypertension is a major determinant of the severity of hypertension that follows severe reduction of renal mass and the severity of the resulting hypertension, in turn, critically influences the severity of glomerular injury in the RK model.

Animals↗
PubMed2000

Novel DNA binding domain and genetic regulation model of Bacillus subtilis transition state regulator abrB.

We have determined the high resolution NMR solution structure of the novel DNA binding domain of the Bacillus subtilis transition state regulator AbrB. Comparisons of the AbrB DNA binding domain with DNA binding proteins of known structure show that it is a member of a completely novel class of DNA recognition folds that employs a dimeric topology for cellular function. This new DNA binding conformation is referred to as the looped-hinge helix fold. Sequence homology investigations show that this DNA binding topology is found in other disparately related microbes. Structural analysis of the AbrB DNA binding domain together with bioanalytical and mutagenic data of full length AbrB allows us to construct a general model that describes the genetic regulation properties of AbrB.

Bacillus subtilis↗
PubMed1975

A general model for genetic recombination.

A general model is proposed for genetic recombination. Its essential new feature is the hypothesis that recombination is initiated by a single-strand (or asymmetric) transfer, which may, after isomerization, become a two-strand (or symmetric) exchange. The likelihood of this transition from asymmetric to symmetric strand exchange determines certain characteristic features of recombination in any particular organism.

Chromatids↗
PubMed2003

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

Animals↗
PubMed2006

Evolutionary theory for modifiers of epistasis using a general symmetric model.

Genetic interactions in fitness are studied by using modifier theory. The effects on fitness of two linked genes are perturbed by alleles at a third linked locus that controls the extent of epistasis in fitness between the first two. This epistasis is determined by a symmetric interaction matrix, and it is shown that a modifier allele that increases epistasis will invade when the linkage between the other two genes is sufficiently tight and these genes are in linkage disequilibrium. With linkage equilibrium among the major loci, increased or decreased epistasis may evolve depending on the allele frequencies at these loci.

Alleles↗
PubMed2006

From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms.

The calibration of storm water runoff models is a complex task. Early attempts focused on the choice of a performance criterion function that could capture all the facets of the problem into a single-objective framework. Subsequently, the awareness that a good calibration must necessarily take into account conflicting objectives led to the adoption of more sophisticated multi-objective approaches. Only recently, the focus has shifted towards effective ways of exploiting the mounting information provided by the availability of many sets of concurrent rainfall and flow measurements. This paper revisits through a case study the transition just elucidated: the calibration of a SWMM model applied to a catchment in Singapore is tackled through a single-objective, a multi-objective and a multi-objective multiple-event (MOME) paradigm respectively. A new approach to support the latter is presented herein. It consists in formulating the problem of model calibration as a multi-objective problem with m x r objective functions, where m and r are the number of performance criteria and rainfall events respectively, that must be optimized simultaneously. Results suggest that the new MOME framework performs significantly better than the others tested on the case study presented.

Algorithms↗
PubMed2001

Utilizing stochastic genetic epidemiological models to quantify the impact of selection for resistance to infectious diseases in domestic livestock.

This paper demonstrates the use of stochastic genetic epidemiological models for quantifying the consequences of selecting animals for resistance to a microparasitic infectious disease. The model is relevant for many classes of infectious diseases where sporadic epidemics occur, and it is a powerful tool for investigating the costs, benefits, and risks associated with breeding for resistance to specific diseases. The model is parameterized for transmissible gastroenteritis, a viral disease affecting pigs, and selection for resistance to this disease on a structured pig farm is simulated. Two genetic models are used, both of which involve selection of sires. The first involves selection with the assumption of continuous genetic variation (the continuous selection model). The second involves selection with the assumption of introgression of a major recessive gene that confers resistance (the gene introgression model). In the base population, the basic reproductive ratio, R0 (i.e., the expected number of secondary cases after the introduction of a single infected animal) was 2.24, in agreement with previous studies. The probabilities of no epidemic, a minor epidemic (one that dies out without intervention), and a major epidemic were 0.55, 0.20, and 0.25, respectively. Selection for resistance, under both genetic models, resulted in a nonlinear decline in the probability of a major epidemic and a decrease in the severity of the epidemic, should it occur, until R0 was less than 1.0, at which point the probability of a major epidemic was zero. For minor epidemics, the probability and severity of the epidemic increased until R0 reached 1.0, at which point the probabilities also fell to zero. The epidemic probabilities were critically dependent on the location on the farm where infected animals were situated, and the relative risks of different groups of animals changed with selection. The main difference between the two genetic models was in the time scale; the introgression results simply depended on how quickly the resistance allele could be introgressed into the population. For the introgression model, the probability of a major epidemic declined to zero when 0.6 of the animals were homozygous for the resistance allele.

Animal Husbandry↗
PubMed1996

The 1996 RSA Distinguished Research Award Lecture. A genetic animal model of alcohol withdrawal.

Work with genetic animal models has a long history in alcohol research. No attempt has been made here to acknowledge the contributions of the many other long-standing projects in the area. Rather, I have tried to give a capsule view of one such project, and to indicate some areas of future direction. It seems clear that the expression of molecular biological tools will greatly expand our capabilities in the immediate future. The field is well-situated to take advantage of this situation, as it has achieved much progress during the "dark ages," where the study of genetic influence was inferential: that is, we were limited to studying genes we could not identify. The future will surely lead us to the identification and study of specific genes of importance.

Alcohol Withdrawal Delirium↗
PubMed1999

Evolutionary consequences of niche construction and their implications for ecology.

Organisms regularly modify local resource distributions, influencing both their ecosystems and the evolution of traits whose fitness depends on such alterable sources of natural selection in environments. We call these processes niche construction. We explore the evolutionary consequences of niche construction using a two-locus population genetic model, which extends earlier analyses by allowing resource distributions to be influenced both by niche construction and by independent processes of renewal and depletion. The analysis confirms that niche construction can be a potent evolutionary agent by generating selection that leads to the fixation of otherwise deleterious alleles, supporting stable polymorphisms where none are expected, eliminating what would otherwise be stable polymorphisms, and generating unusual evolutionary dynamics. Even small amounts of niche construction, or niche construction that only weakly affects resource dynamics, can significantly alter both ecological and evolutionary patterns.

Alleles↗
PubMed2005

WinPop 2.5: software for representing population genetics phenomena.

The curriculum for genetics courses is shifting from a classical to a more molecular genetics focus, increasing the importance of subjects such as population genetics. Population genetics is a computational and statistical field that requires a good understanding of the nature of stochastic events. It is a difficult field for biology students with a limited mathematical background and there is a need for visualisation tools to facilitate understanding by the use of practical examples. WinPop provides students and researchers with a visual tool to allow the simulation and representation of population genetics phenomena. WinPop is a user-friendly software meant for use in population genetics courses and basic research. WinPop 2.5 contains six different modules that represent and simulate population genetics models. Genotype and allele frequencies are calculated under the different models: panmixia, genetic drift, assortative matings, selection, gene flow and mutation. The program's interface presents information in Cartesian graphics and isosceles triangular coordinate systems, allowing the user to save graphical and textual data output from the simulations. WinPop is developed in Visual Basic 6.0 and uses Windows 95 and higher. WinPop 2.5 can be downloaded from http://www.genedrift.org/winpop.php.

Algorithms↗
PubMed2006

Discrete time piecewise affine models of genetic regulatory networks.

We introduce simple models of genetic regulatory networks and we proceed to the mathematical analysis of their dynamics. The models are discrete time dynamical systems generated by piecewise affine contracting mappings whose variables represent gene expression levels. These models reduce to boolean networks in one limiting case of a parameter, and their asymptotic dynamics approaches that of a differential equation in another limiting case of this parameter. For intermediate values, the model present an original phenomenology which is argued to be due to delay effects. This phenomenology is not limited to piecewise affine model but extends to smooth nonlinear discrete time models of regulatory networks. In a first step, our analysis concerns general properties of networks on arbitrary graphs (characterisation of the attractor, symbolic dynamics, Lyapunov stability, structural stability, symmetries, etc). In a second step, focus is made on simple circuits for which the attractor and its changes with parameters are described. In the negative circuit of 2 genes, a thorough study is presented which concern stable (quasi-)periodic oscillations governed by rotations on the unit circle - with a rotation number depending continuously and monotonically on threshold parameters. These regular oscillations exist in negative circuits with arbitrary number of genes where they are most likely to be observed in genetic systems with non-negligible delay effects.

Algorithms↗
PubMed2006

Genetic and environmental interrelations between measurements of thyroid function in a healthy Danish twin population.

Serum thyrotropin (TSH), free thyroxine (T4), and free triiodothyronine (T3) levels illustrate the thyroid function set point, but the interrelations between these have never been characterized in detail. The aim of this study was to examine the associations between TSH and thyroid hormone levels in healthy euthyroid twins and to determine the extent to which the same genes influence more than one of these biochemical traits; 1,380 healthy euthyroid Danish twins (284 monozygotic, 286 dizygotic, 120 opposite-sex twin pairs) were recruited. Genetic and environmental associations between thyroid function measurements were examined using quantitative genetic modeling. In bivariate genetic models, the phenotypic relation between two measurements was divided into genetic and environmental correlations. Free T4 and free T3 levels were positively correlated (r=0.32, P<0.0001). The genetic correlation between serum free T4 and free T3 levels was rg=0.25 (95% CI 0.14-0.35), suggesting that a set of common genes affect both phenotypes (pleiotropy). The correlation between the environmental effects was re=0.41 (0.32-0.50). From this we calculated that the proportion of the correlation between free T4 and free T3 levels mediated by common genetic factors was 48%. Only 7% of the genetic component of serum free T3 levels is shared with serum free T4. Serum TSH and thyroid hormone levels did not share any genetic influences. In conclusion, thyroid hormone levels are partly genetically correlated genes that affect free T4 levels and exert pleiotropic effects on free T3 levels, although most of the genetic variance for these measurements is trait specific.

Adult↗
PubMed2000

ANP in regulation of arterial pressure and fluid-electrolyte balance: lessons from genetic mouse models.

The recent development of genetic mouse models presenting life-long alterations in expression of the genes for atrial natriuretic peptide (ANP) or its receptors (NPR-A, NPR-C) has uncovered a physiological role of this hormone in chronic blood pressure homeostasis. Transgenic mice overexpressing a transthyretin-ANP fusion gene are hypotensive relative to the nontransgenic littermates, whereas mice harboring functional disruptions of the ANP or NPR-A genes are hypertensive compared with their respective wild-type counterparts. The chronic hypotensive action of ANP is determined by vasodilation of the resistance vasculature, which is probably mediated by attenuation of vascular sympathetic tone at one or several prejunctional sites. Under conditions of normal dietary salt consumption, the hypotensive action of ANP is dissociated from the natriuretic activity of the hormone. However, during elevated dietary salt intake, ANP-mediated antagonism of the renin-angiotensin system is essential for maintenance of blood pressure constancy, inasmuch as the ANP gene "knockout" mice (ANP -/-) develop a salt-sensitive component of hypertension in association with failure to adequately downregulate plasma renin activity. These findings imply that genetic deficiencies in ANP or natriuretic receptor activity may be underlying causative factors in the etiology of salt-sensitive variants of hypertensive disease and other sodium-retaining disorders, such as congestive heart failure and cirrhosis.

Animals↗
PubMed2001

Directional selection and the site-frequency spectrum.

In this article we explore statistical properties of the maximum-likelihood estimates (MLEs) of the selection and mutation parameters in a Poisson random field population genetics model of directional selection at DNA sites. We derive the asymptotic variances and covariance of the MLEs and explore the power of the likelihood ratio tests (LRT) of neutrality for varying levels of mutation and selection as well as the robustness of the LRT to deviations from the assumption of free recombination among sites. We also discuss the coverage of confidence intervals on the basis of two standard-likelihood methods. We find that the LRT has high power to detect deviations from neutrality and that the maximum-likelihood estimation performs very well when the ancestral states of all mutations in the sample are known. When the ancestral states are not known, the test has high power to detect deviations from neutrality for negative selection but not for positive selection. We also find that the LRT is not robust to deviations from the assumption of independence among sites.

Alleles↗
PubMed2006

Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling.

A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood-brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMS(V) and RMS(CV) equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data.

Blood-Brain Barrier↗