OBJECTIVETo introduce the application of Multifactor Dimensionality Reduction (MDR) method for detecting gene-gene interactions in genetic case-control studies.

METHODSA brief overview on basic steps involved in the implementation, theoretical details, available software as well as the use and features of the MDR method were discussed based on a practical research case.

RESULTSAdvantages of MDR were compared to the conventional statistical approaches, showing that MDR method was a novel, nonparametric, genetic model-free approach that was developed specifically for detecting gene-gene interactions. Theoretical and empirical studies suggested that MDR was having reasonable power for detecting gene-gene interactions. Applications of MDR method had found the evidence of gene-gene interactions in several diseases such as sporadic breast cancer, atrial fibrillation and essential hypertension.

CONCLUSIONMDR method could be used for detecting gene-gene interactions in genetic case-control studies as having great advantages versus the conventional statistical approaches.

Psychiatric disorder as dysfunctional behavioural syndrome is a paradoxical phenomenon that is difficult to explain evolutionarily because moderate prevalence rate, high heritability and relatively low fitness are shown. Several evolutionary genetic models have been proposed to address this paradox. In this paper, I explain each model by dividing it into selective neutrality, mutation-selection balance, and balancing selection hypothesis, and discuss the advantages and disadvantages of them. In addition, the feasibility of niche specialization and frequency dependent selection as the plausible explanation about the central paradox is briefly discussed.
Mental Disorders ; Models, Genetic ; Prevalence

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from (http://agbu. une.edu.au/~kmeyer/wombat.html).
Animals ; Likelihood Functions ; Mice ; Models, Genetic ; Software

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.
Algorithms ; Humans ; Models, Genetic ; Neural Networks (Computer)

Approaches to the study of the genetic basis of common complex diseases and their clinical applications are considered. Monogenic Mendelian inheritance in such conditions is infrequent but its elucidation may help to detect pathogenic mechanisms in the more common variety of complex diseases. Involvement by multiple genes in complex diseases usually occurs but the isolation and identification of specific genes so far has been exceptional. The role of common polymorphisms as indicators of disease risk in various studies is discussed.
Genetic Diseases, Inborn ; genetics ; Genetic Predisposition to Disease ; Genetic Techniques ; Humans ; Inheritance Patterns ; Linkage Disequilibrium ; Models, Genetic ; Polymorphism, Genetic ; Risk Factors

OBJECTIVETo evaluate the effect and profitability of using the quantitative trait loci (QTL)-linked direct marker (DR marker) in gene-assisted selection (GAS).

METHODSThree populations (100, 200, or 300 sows plus 10 boars within each group) with segregating QTL were simulated stochastically. Five economic traits were investigated, including number of born alive (NBA), average daily gain to 100 kg body weight (ADG), feed conversion ratio (FCR), back fat at 100 kg body weight (BF) and intramuscular fat (IMF). Selection was based on the estimated breeding value (EBV) of each trait. The starting frequencies of the QTL's favorable allele were 0.1, 0.3 and 0.5, respectively. The economic return was calculated by gene flow method.

RESULTSThe selection efficiency was higher than 100% when DR markers were used in GAS for 5 traits. The selection efficiency for NBA was the highest, and the lowest was for ADG whose QTL had the lowest variance. The mixed model applied DR markers and obtained higher extra genetic gain and extra economic returns. We also found that the lower the frequency of the favorable allele of the QTL, the higher the extra return obtained.

CONCLUSIONGAS is an effective selection scheme to increase the genetic gain and the economic returns in pig breeding.

One of the main topics in population genetics is identification of adaptive selection among populations. For this purpose, population history should be correctly inferred to evaluate the effect of random drift and exclude it in selection identification. With the rapid progress in genomics in the past decade, vast genome-scale variations are available for population genetic analysis, which however requires more sophisticated models to infer species' demographic history and robust methods to detect local adaptation. Here we aim to review what have been achieved in the fields of demographic modeling and selection detection. We summarize their rationales, implementations, and some classical applications. We also propose that some widely-used methods can be improved in both theoretical and practical aspects in near future.
Adaptation, Physiological ; genetics ; Demography ; Evolution, Molecular ; Genetics, Population ; Genome ; Models, Genetic ; Polymorphism, Genetic ; Selection, Genetic ; genetics

Data are presented which strongly indicate that the locus occupied by the mutant wz lies to the left of, or on the same locus of apricot (wa). The fact that wz shows a non-suppressor effect in combination with zeste as a typical phenomenon in mutants of sites 1, 2, and 3 of the white locus, also supports that wz is a mutant at or left of apricot (site 3). A "model" for the genetic fine structure of the mutants (wzm, wzl, and wz) is proposed and discussed to account for the mutability among those three mutants. Analysis of wz leads to the hypothesis that it is the result of an inversion (sites 3 and 4) at the white locus, by two mechanisms which are discussed briefly here.
Animals ; *Drosophila ; Female ; *Genes ; Male ; Models, Biological ; *Mutation ; *Recombination, Genetic

The purpose of this review is to confirm the reason resulted the gene silence and explore the countermeasure avoiding the gene silence in transgene plant. The method is to divide the gene silencing into transcriptional gene silencing(TGS) and posttranscriptional gene silencing(PTGS). Several models resulted PTGS were analyzed by RNA threshold model, ectopic pairing and aberrant RNA model and ds-RNA model. The results showed that it was important to decide the phenomena of restraining transgene silencing and the mechanism of PTGS. The strategies of identification of gene function and prevention of virus were presented by RNAi and gene silencing respectively, etc.
Animals ; Gene Silencing ; Models, Genetic ; RNA ; RNA Processing, Post-Transcriptional

BACKGROUND/AIMS: The p22phox C242T gene polymorphism (rs4673) may be linked to an increased susceptibility for overt diabetic nephropathy (ODN), but the study results are still inconclusive. METHODS: To explore the relationship between p22phox C242T gene polymorphism and ODN, the current meta-analysis of 707 ODN patients and 745 controls from five individual studies was conducted. The pooled odds ratio (OR) and its corresponding 95% confidence interval (CI) were evaluated by either a random or fixed effect model. RESULTS: In our meta-analysis, a significant relationship between the p22phox C242T gene polymorphism and ODN was found under allelic (OR, 2.760; 95% CI, 1.400 to 5.450; p = 0.004), recessive (OR, 5.080; 95% CI, 1.020 to 25.430; p = 0.05), dominant (OR, 1.700; 95% CI, 1.167 to 2.477; p = 0.006), homozygous (OR, 3.900; 95% CI, 1.022 to 14.889; p = 0.046), heterozygous (OR, 1.523; 95% CI, 1.167 to 1.986; p = 0.002), and additive genetic models (OR, 2.019; 95% CI, 1.232 to 3.309; p = 0.005). CONCLUSIONS: A positive correlation between p22phox C242T gene polymorphism and ODN risk was found. The T allele carriers of p22phox C242T gene polymorphism might be predisposed to ODN.
Alleles ; Diabetic Nephropathies* ; Humans ; Models, Genetic ; Odds Ratio