Some host parasite genetic interaction models.
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Diploidy↗
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Results for “Models, Genetic”
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Alcohol dependence and withdrawal: a genetic animal model.
Using the techniques of selective breeding, mouse lines have been developed that express severe (Withdrawal Seizure Prone: WSP) or mild (Withdrawal Seizure Resistant: WSR) handling induced convulsions after cessation of chronic ethanol exposure. These lines differ at least ten-fold in severity of withdrawal after identical ethanol treatment. One feature of the genetic model is that other traits which distinguish these lines are presumably influenced by those genes determining ethanol withdrawal severity. WSP and WSR mice do not differ markedly in the metabolism of ethanol. In addition to handling induced convulsions, they also differ in other withdrawal signs: for example, WSP mice show more pronounced tremor. WSP and WSR mice do not differ in sensitivity to ethanol's hypothermic, anesthetic, or locomotor stimulant effects, nor in the magnitude of tolerance development to these responses. This suggests that sensitivity, tolerance and dependence are distinct genetic entities. WSP mice also display more severe withdrawal from diazepam, phenobarbital, and nitrous oxide than WSR mice, suggesting that some genes generally predispose mice to withdrawal from depressants. WSP mice display withdrawal handling induced convulsions after a single dose of ethanol, pentobarbital, or diazepam. The effective dose for producing drug seizures is not markedly different between WSP and WSR mice for a number of compounds with varied mechanisms of action. However, WSP mice are more sensitive than WSR mice to the effects of acute doses of convulsants to elevate handling induced convulsions. WSP mice have more binding sites in hippocampus for the N-methyl-D-aspartate antagonist MK 801. Binding of this ligand is increased during ethanol dependence in both mouse lines.(ABSTRACT TRUNCATED AT 250 WORDS)
Alcoholism↗
A clinical model of genetic influences in alcohol dependence.
This essay presents a clinical model of the role of genetic factors in alcohol dependence. Data supporting a strong impact of environmental influences along with genetic heterogeneity and the absence of simple Mendelian models combine to produce a complex situation. In light of these complications, this essay presents a model developed by the author's research group focusing on the decreased reaction to alcohol as an important risk factor. The biological contributors might be polygenic or dominant with incomplete penetrance and must interact with environment. The application of this approach to other risk factors is set forth.
Alcohol Drinking↗
Genetic mouse models of Huntington's and Parkinson's diseases: illuminating but imperfect.
Genetic mouse models based on identification of genes that cause Huntington's and Parkinson's diseases have revolutionized understanding of the mechanistic pathophysiological progression of these disorders. These models allow the earliest manifestations of the diseases to be identified, and they display behavioral, neuropathological and electrophysiological deficits that can be followed over time in mechanistic and drug studies. An intriguing feature is that they do not reproduce the relatively selective and massive cell loss characterizing the human diseases. There is more information on Huntington's disease models because the disorder involves a single gene that was identified over ten years ago; genetic mutations causing Parkinson's disease are rare and were discovered more recently, and models of the disease have been generated only within the past few years.
Animals↗
The 5-HT1A agonist 8-OH-DPAT increases the number of spike-wave discharges in a genetic rat model of absence epilepsy.
The effects of the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)-tetralin (8-OH-DPAT) on the epileptiform activity has been investigated in adult WAG/RIJ rats. Either intraperitoneal (0.1-0.5 mg/kg) or intracerebroventricular (2-20 microg/rat) administration of 8-OH-DPAT caused marked, dose-dependent increases in the number and mean cumulative duration of spike-wave discharges. These effects were attenuated by NAN-190, a 5-HT1A receptor antagonist. These data indicate that serotonergic system regulates the epileptiform activity in this genetic model of human absence epilepsy.
8-Hydroxy-2-(di-n-propylamino)tetralin↗
Automatic prostate boundary recognition in sonographic images using feature model and genetic algorithm.
This paper describes the development of a model-based boundary recognition system for transrectal prostate ultrasonographic images. It consists of two techniques: boundary modeling and boundary searching with model constraints. To achieve higher specificity of the model, a method called feature modeling is derived from the existing point distribution modeling method. To improve the robustness of the searching technique, the genetic algorithm is used. Incremental genetic algorithm with crowding replacement and binary string chromosome type was found experimentally to give good search results. It was shown that the system could recognize the boundary with considerable accuracy and consistency within a few minutes in transrectal ultrasonographic images taken from approximate middle position of the prostate.
Algorithms↗
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. Selecting the optimal graph, which gives the best representation of the system among genes, is still a problem to be solved. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.
Bayes Theorem↗
Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.
Artificial Intelligence↗
Treatment of MRL/lpr mice, a genetic autoimmune model, with the Ras inhibitor, farnesylthiosalicylate (FTS).
Activation and proliferation of lymphocytes requires the active signal transducer Ras. Activation of lymphocytes, associated with autoimmunity, may therefore be modified by S-farnesylthiosalicylic acid (FTS), a synthetic substance that detaches Ras from the inner cell membrane and induces its rapid degradation. The MRL/lpr mouse is a genetic model of a generalized autoimmune disease sharing many features and organ pathology with systemic lupus erythematosus (SLE) and the primary antiphospholipid syndrome (APS). The objective of the present study was to examine the effect of FTS on laboratory and clinical pathology in the MRL/lpr mouse. Female MRL/lpr (n = 50) and MRL/++ control (n = 35) mice were treated intraperitoneally with either FTS (5 mg/kg/day) or saline between 6 and 18 weeks of age. The mice were weighed, tested for proteinuria and lymphadenopathy, lymphocyte proliferation, antibodies, grip strength and behaviour in an open field. FTS treatment resulted in a 50% decrease in splenocyte proliferation to ConA, LPS and a disease specific antigen, beta(2)-glycoprotein-I, and in a significant decrease in serum antibody levels against cardiolipin and dsDNA. Proteinuria and grip strength were normalized and lymphadenopathy and postmortem lymph node and spleen weights were significantly reduced in FTS treated MRL/lpr mice. These findings indicate that modulation of Ras activation has a significant impact on the MRL/lpr model and may represent a new therapeutic approach for the treatment of systemic autoimmune diseases such as SLE and APS.
Animals↗
Effects of topiramate in two models of genetically determined generalized epilepsy, the GAERS and the Audiogenic Wistar AS.
PURPOSE: The antiepileptic effects of topiramate (TPM) were assessed in two models of genetically determined generalized epilepsy. The model of nonconvulsive epilepsy used was a model of absence seizures, the GAERS (Genetic Absence Epilepsy Rat from Strasbourg); and the model of convulsive seizures was an audiogenic rat model, the Wistar Audiogenic Sensitive (AS) rat. METHODS: GAERS were equipped with four cortical electrodes over the frontoparietal cortex, and the duration of spike-and-wave discharges (SWDs) on the EEG was recorded for periods of 20 to 120 or 300 min. In Wistar AS, the occurrence of, latency to, and duration of one or two wild running episodes and tonic seizures were recorded. RESULTS: In the 16 GAERS studied, TPM (10, 30, and 60 mg/kg) dose-dependently reduced the expression of SWD that almost totally disappeared at the two highest doses between 40 and 120 min. SWD duration returned to control levels by 180 and 280 min after the injection of 30 and 60 mg/kg TPM, respectively. In Wistar AS, 10 mg/kg TPM induced the occurrence of a second running episode not present in control rats, indicative of a decrease in sensitivity of the rats to the stimulus and increased by 330% the latency to the tonic seizure that still occurred in the eight rats studied. At 30 and 60 mg/kg, the latency to wild running increased by 140%; the second running episode was suppressed in six and seven rats, respectively, whereas the tonic seizure occurred only in one of the eight rats studied at these two doses. CONCLUSIONS: These results support the broad spectrum of antiepileptic activity of TPM, confirming its efficacy in primary generalized seizures of both tonic-clonic and of the absence type.
Animals↗
Systemic lupus erythematosus: a genetic epidemiology study of 695 patients from China.
Our purpose was to explore potential genetic models for systemic lupus erythematosus (SLE) and analyze genetic epidemiologic characteristics of SLE in a Chinese population. Data for 695 patients with SLE were obtained by using a uniform questionnaire. Patients, clinical characteristics and their family history were analyzed using software. A complex segregation analysis was conducted to propose potential genetic models for SLE. The mean +/- SD age of onset were 30.2 +/- 10.5 years and mean time to progression to SLE was 32.5 +/- 44.4 months. The most frequent initial manifestations were malar rash (61.3%). During the evolution of the disease, the main clinical features were arthritis in 73.6% of our patients, followed by malar rash (68.1%), and renal involvement (56.7%). As the first symptom, the late-onset group (onset of disease beyond the age of 50 years) less often showed malar rash (45% vs. 63.4% in the early-onset group; p = 0.001). There were no significant differences in the other cumulative clinical symptoms between late-onset and early-onset group, except for a lower prevalence of malar rash, photosensitivity and alopecia and a higher prevalence of mucosal ulcers in the late-onset group. A positive family history of SLE was obtained in 50 patients (7.2%). There were no statistical differences in clinical characteristics between familial SLE and sporadic SLE patients. The heritability of SLE was 43.6%, the genetic model of SLE could be polygenetic model and major gene mode is the best fitted one. SLE could be a multifactorial disease with polygenetic model.
Adolescent↗
Maximum-Likelihood-Binomial method for genetic model-free linkage analysis of quantitative traits in sibships.
Sib-pair linkage studies are widely used to investigate the genetic factors implicated in complex quantitative traits. To analyze these data, we propose a Maximum-Likelihood-Binomial (MLB) approach, which considers the sibship as a whole and relies on the idea of binomial distributions of parental alleles among offsprings. The method is based on the introduction of a latent binary variable capturing the linkage information between the observed quantitative trait and the marker, and the final likelihood can be expressed assuming a parametric distribution for the studied trait but also without any assumption on this distribution. The test for linkage is a simple likelihood ratio test involving a single parameter. The performances of the MLB method are assessed by a simulation study in different kinds of family samples. In the case of families with various sibship sizes, both MLB approaches (assuming or not a parametric distribution for the quantitative trait) provide very consistent results in terms of type I errors and yield power levels generally higher than those of the classical Haseman-Elston method. In the case of extremely discordant sib pairs, we analytically show that, for a common asymptotic type I error, the distribution-free MLB statistic is expected to be more powerful than the test proposed by Risch and Zhang [(1995) Science 268:1584-1589]. In samples including both extremely concordant and discordant sib-pairs, simulation studies show that the MLB approach is at least as powerful as the EDAC method [Gu et al. (1996) Genet Epidemiol 13:513-533]. This MLB method, which can be easily extended to perform multipoint analysis and to account for genetic heterogeneity, appears to be quite an interesting alternative for mapping quantitative trait loci in humans.
Alleles↗
Cost-effective sib-pair designs in the mapping of quantitative-trait loci.
The extreme discordant-sib-pair design has been found to be the most powerful, across most genetic models. In this paper, we address two of the most frequently asked questions related to this design. First, under the extreme discordant-sib-pair design, a large number of people have to be screened for the phenotype of interest, before the desired number of discordant sibs can be collected for genotyping and linkage analysis. When the phenotyping cost is not negligible compared with the genotyping cost, such methods might not be cost effective. The second question is how sensitive the cost is to the genetic model and allele frequency. In this paper, we compare the cost under different sampling strategies, different genetic models, and different phenotyping:genotyping cost ratios. Because our knowledge of the underlying genetic model for a trait is limited, the discordant-sib-pair design proves to be the most robust. When the cost for screening probands is not included, the design that genotypes sibs with one sib in the top 10% and the other sib in the bottom 30% of the population with respect to the trait of interest is, across most models studied, the optimum among the designs considered in this paper. The cost under this design, across different genetic models, appears to be relatively robust to allele frequency and model type, whether additive or dominant. If probands initially must be screened as well, then 25% appears to be the optimal portions of the upper and lower distributions to be studied.
Chromosome Mapping↗
Genetic progression model for head and neck cancer: implications for field cancerization.
A genetic progression model of head and neck squamous cell carcinoma has not yet been elucidated, and the genetic basis for "field cancerization" of the aerodigestive tract has also remained obscure. Eighty-seven lesions of the head and neck, including preinvasive lesions and benign lesions associated with carcinogen exposure, were tested using microsatellite analysis for allelic loss at 10 major chromosomal loci which have been defined previously. The spectrum of chromosomal loss progressively increased at each histopathological step from benign hyperplasia to dysplasia to carcinoma in situ to invasive cancer. Adjacent areas of tissue with different histopathological appearance shared common genetic changes, but the more histopathologically advanced areas exhibited additional genetic alterations. Abnormal mucosal cells surrounding preinvasive and microinvasive lesions shared common genetic alterations with those lesions and thus appear to arise from a single progenitor clone. Based on these findings, the local clinical phenomenon of field cancerization seems to involve the expansion and migration of clonally related preneoplastic cells.
Carcinoma, Squamous Cell↗
Methodological aspects of the genetic dissection of gene expression.
MOTIVATION: Dissection of the genetics underlying gene expression utilizes techniques from microarray analyses as well as quantitative trait loci (QTL) mapping. Available QLT mapping methods are not tailored for the highly automated analyses required to deal with the thousand of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression. RESULTS: The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as 'high confidence' QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. After extensive analyses of real data and additional simulated data (altogether > 300,000 genome scans) we make the following recommendations for detection of QTL for gene expression: (1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on repeatability of the trait and the number of replicates. (2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. (3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. (4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localization of gene-transcripts and QTL. (5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the Type I error. (6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarized in a possible strategy for mapping of QTL in a least squares framework. AVAILABILITY: The software used for this study is available on request from the authors.
Algorithms↗
Models of genetic recombination.
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Bacteria↗
Model choice in gene mapping: what and why.
The choice of an appropriate genetic model describing the genetic architecture underlying a character of interest is an inherent part of the gene mapping studies of human and other living organisms. The genetic model specifies the statistical parameters for the number of genes, their positions, and the types and magnitudes of their contributions to the phenotype. There are many considerations involved in model formulation (choice) ranging from the assumptions concerning the data, the role of environment, and the number of oligogenes (or quantitative trait loci) influencing the trait behavior. There are several model selection procedures and criteria under specific sampling designs in the genetic literature. These approaches often have their origin in computer science or in general statistical theory. Our aim here is to give an overview of the most popular statistical criteria and to present principles behind them. Bayesian model averaging is suggested as a robust alternative for such methods.
Animals↗
Monthly model for genetic evaluation of laying hens 1. Fixed regression.
1. This paper addresses the possibility of using a monthly model for the genetic evaluation of laying hens, based on the definition of a test day model with fixed regression as used in dairy cattle, in which monthly records were treated as repeated measurements of the same trait. 2. Production records of 6450 hens, daughters of 180 sires and 1335 dams were analysed using an animal model with restricted maximum likelihood (REML). The traits considered were individual monthly egg production and cumulative egg production in 11 months. Four different models were fitted to various combinations of monthly and cumulative records. The covariates were derived from the regression of Ali and Schaeffer (1987). 3. Spearman rank correlations were computed to compare breeding values from different models. Two types of correlations were computed: between individual breeding values and between sire breeding values based on subsets of full-sib records. 4. The results indicated that a monthly model with nested covariates produced higher heritability and permanent environmental variance than the models with non-nested or without covariates. The estimates of heritability obtained from monthly model were lower than the estimates from the cumulative model. The monthly model resulted in higher correlations of sire breeding values between two subsets of full-sib records than those from cumulative models. 5. In conclusion, the monthly model with nested covariates appears to be better than the model with non-nested covariates or without covariate. Although the heritability estimates obtained from the monthly model were lower, the monthly model with nested covariates could be better than the cumulative model for genetic evaluation of laying hens in the 1st cycle of laying period when using either full or part records. The use of information from odd months of production could be of interest for the evaluation of full records.
Analysis of Variance↗