QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines （RIL） derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length （GL）, grain length to width ratio （LWR）, chalk grain rate （CGR）, chalkiness degree （CD）, gelatinization temperature （GT）, amylose content （AC） and gel consistency （GC） of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model （MCIM）, inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model （ICIM） and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis （MIMR）. Results showed that five QTLs with additive effect （A-QTLs） were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect （E-QTLs） detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.

An Introduction to Basic Statistical Models in Genetics

The use of the three genetic models viz. additive, dominant and recessive in Genome-wide association study (GWAS) is a common and powerful approach to study the association between genetic variants and a trait (disease). The selection of these models depends on the pattern of inheritance and the scope of the study. GWAS typically focuses on single-nucleotide polymorphism (SNPs) and common human diseases in a case-control setup. In order to study this type of association between the risk genotype and the phenotype for a given inheritance pattern, the use of these genetic models helps to identify the disease risk appropriately. This study provides an overview of the existing genetic models (additive, dominant and recessive) and a practical demonstration of these model tests for the contingency tables of SNP genotypes and the disease phenotypes in a case-control setting.

Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson's disease,with insights into Pink1 knockout models

Parkinson’s disease(PD)relates to defective mitochondrial quality control in the dopaminergic motor network.Genetic studies have revealed that PINK1 and Parkin mutations are indicative of a heightened propensity to PD onset,pinpointing mitophagy and inflammation as the culprit pathways involved in neuronal loss in the substantia nigra(SNpc).In a reciprocal manner,LRRK2 functions in the regulation of basal flux and inflammatory responses responsible for PINK1/Parkin-dependent mitophagy activation.Pharmacological intervention in these diseasemodifying pathways may facilitate the development of novel PD therapeutics,despite the current lack of an established drug evaluation model.As such,we reviewed the feasibility of employing the versatile global Pink1knockout(KO)rat model as a self-sufficient,spontaneous PD model for investigating both disease etiology and drug pharmacology.These rats retain clinical features encompassing basal mitophagic flux changes with PD progression.We demonstrate the versatility of this PD rat model based on the incorporation of additional experimental insults to recapitulate the proinflammatory responses observed in PD patients.

Genetic Rat Models of Schizophrenia-Relevant Symptoms

It is recognized that developing valid animal models is essential for the research on the neurobiological mechanisms of (and treatments for) psychiatric disorders, even when these are as complex as schizophrenia. To be considered a valid analogue of the disorder, a given model should present good face validity (i.e. similarity of symptoms), good predictive validity (i.e. similarity of treatment effects and potential for discovering novel treatments) and enough construct validity (i.e. the model should help discover neurobiological mechanisms underlying the disorder or some relevant symptoms). The complexity of symptoms (positive, negative and cognitive) of schizophrenia makes it a very difficult task for a model to mimic all the main features of the disorder, but some rodent (mouse and rat) models have behavioural and even neurobiological phenotype characteristics resembling positive-like symptoms, cognitive symptoms and some neurochemical features of schizophrenia. As several recent works have already reviewed the main behavioural and developmental models, as well as the most used drug-induced, lesion-induced and genetic mouse models, the present review focuses on describing the most relevant genetically-based rat models of schizophrenia-relevant symptoms. Thus, we discuss several selective breeding programs leading to rat lines/strains which present impaired prepulse inhibition (PPI) of the acoustic startle response and (in some cases) latent inhibition deficits (both of which may be considered as endophenotypes of schizophrenia related with pre-attentive processes and attention, respectively), as well as other schizophrenia-relevant symptoms (e.g. learning deficits). Evidence is presented for the effects of genetic background on PPI (and other symptoms/phenotypes), as well as for environmental influences on genetic predisposition to enhanced apomorphine (mixed dopamine receptor agonist) effects. Some of the described rat models appear to present face validity and, to a certain extent, construct validity. While efforts should be made to evaluate the predictive validity of these genetic rat models, we propose that they have the advantage (over mouse knockouts, for example) of better representing “normal” genetic, neurobiological and phenotype variation, thus allowing the study of associations among them by means of genetic mapping or gene expression studies.

Genetic Analysis of the First Female Flower Node and the First Male Flower Node in Bitter Gourd

Exploring genetic mechanism of the first female flower node and the first male flower node in bitter gourd has practical significance for formulating breeding strategy. In this article, a cross was made between CN19-1 and Thai4-6, and the F2segregation population was also constructed through F1selfing. The genetic characteristics of the first female flower node and the first male flower node were analyzed by adopting the major gene plus polygene mixed genetic model. The data analysis results showed that the first female flower node and the first male flower node were continuous distribution in the F2segregation population. E-2 model was the most suitable model for the genetic analysis of the first female flower node and the first male flower node. The additive effect values of the 2 pairs of major genes controlling the first female flower node were 2.722 and 1.862 8 respectively, the dominant effect values were-2.721 6 and-0.171 8, respectively. The additive effect value of polygene was-0.839 2, and the dominant effect value of polygene was 2.225 4. The heritability of major genes and polygene were 83.73% and 1.54%, respectively. The additive effect values of the 2 pairs of major genes controlling the first male flower node were 17.746 9 and 3.972, respectively, the dominant effect values were 5.191 6 and-3.972, respectively. The additive effect value of polygene was-20.530 5, and the dominant effect value was-4.141 4. The heritability of major genes and polygene was 92.34% and 4.7%, respectively. This study could provide a theoretical basis for bitter gourd breeding.

Genetic Regression Model for Dam Safety Monitoring

Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.

Animal models of amyotrophic lateral sclerosis： a comparison of model validity

Animal models are necessary to investigate the pathogenic features underlying motor neuron degeneration and for therapeutic development in amyotrophic lateral sclerosis（ALS）. Measures of model validity allow for a critical interpretation of results from each model and caution from over-interpretation of experimental models. Face and construct validity refer to the similarity in phenotype and the proposed causal factor to the human disease, respectively. More recently developed models are restricted by limited phenotype characterization, yet new models hold promise for novel disease insights, thus highlighting their importance. In this article, we evaluate the features of face and construct validity of our new zebrafish model of environmentally-induced motor neuron degeneration and discuss this in the context of current environmental and genetic ALS models, including C9 orf72, mutant Cu/Zn superoxide dismutase 1 and TAR DNA-binding protein 43 mouse and zebrafish models. In this mini-review, we discuss the pros and cons to validity criteria in each model. Our zebrafish model of environmentally-induced motor neuron degeneration displays convincing features of face validity with many hallmarks of ALS-like features, and weakness in construct validity. However, the value of this model may lie in its potential to be more representative of the pathogenic features underlying sporadic ALS cases, where environmental factors may be more likely to be involved in disease etiology than single dominant gene mutations. It may be necessary to compare findings between different strains and species modeling specific genes or environmental factors to confirm findings from ALS animal models and tease out arbitrary strain-and overexpression-specific effects.

Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits:Genetic Model for Diploid Plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction （NCI） as well as their genotype by environment （GE） interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction （AUP） method. Data on four quantitative traits （boll number, lint percentage, fiber length, and micronaire） in Upland cotton （Gossypium hirsutum L.） were analyzed as a worked example to show the effectiveness of the model.

Geochemical characteristics and genetic model of dolomite reservoirs in the eastern margin of the Pre-Caspian Basin

The widespread Carboniferous KT-I dolomite in the eastern margin of the Pre-Caspian Basin is an important hydrocarbon reservoir. The dolomite lithology is dominated by crystalline dolomite. The δ18O values range from -6.71‰ to 2.45‰, and average 0.063‰, obviously larger than -2.5‰, indicating low-temperature dolomite of evaporation origin. Stable strontium isotope ratios （87Sr/86Sr） range from 0.70829 to 0.70875 and average 0.708365, very consistent with 87Sr/86Sr ratios in Carboniferous seawater. Chemical analysis of Ca and Mg elements shows that the dolomite has 9.1 mole% excess Ca or even higher before stabilization. The degree of order of dolomite is medium–slightly poor, varying in a range of 0.336-0.504 and averaging 0.417. It suggests that the dolomite formed under near-surface conditions. There are two models for the origin of the Carboniferous KT-I dolomite reservoir. These are 1） the evaporation concentration – weathering crust model and 2） the shoal facies – seepage reflux model. The former is mainly developed in restricted platforms – evaporate platforms of restricted marine deposition environments with a representation of dolomite associated with gypsum and mudstone. The latter mainly formed in platform edge shoals and intra-platform shoals and is controlled by dolomitization due to high salinity sea water influx from adjacent restricted sea or evaporate platform.

Genetic Model for Fresh-keeping Character in Sweet Corn

This study was conducted to investigate the genetic regularity of indexes related to freshness keeping and its molecular basis by acquiring 6 generations （P1, P2, F1, B1, B2 and F2） of an inbred line T3 with long freshness period × an inbred line T15 with short freshness period in sweet corn. The genetic analysis of the indexes was performed by major gene＋polygene mixed genetic model combined with the genetic analysis combining six generations.The results showed that the decreasing rate of the postharvest sugar content in the T3 was controlled by two pairs of additive-dominante-epistatic major genes＋additive-dominant polygenes; each segregating generation was affected by its major genes, the heritability of major genes and polygene in the B1 generation was 74.63% and 17.67%, respectively; the heritability of major gene and potygene in the B2 was 91.98% and 0,00%, respectively; and the heritability of major gene and polygene inthe F2 was 82.67%, and 12.93%, respectively.

Genetic Analysis of Stripe Disease Resistance in Rice Restorer Line C224 Using Major Gene plus Polygene Mixed Effect Model

The inheritance of stripe disease resistance in a rice restorer line C224 was analyzed using the mixed effect model of major gene plus polygene for quantitative traits.In addition,the resistance was investigated in seven crosses of C224 with maintainer lines.The results showed that the stripe resistance of C224 was controlled by two major genes with additive-dominance-epistasis effects plus polygenes with additive-dominance effects （E-1 model）.These two genes had additive effects of-12.47% and-24.75%,respectively,showing negative dominance effects.There were significant epistasis and interaction effects between the two major genes.The heritability of the two major genes was 92.12%,while that of polygenes was 2.74%,indicating that the stripe resistance had dominant major gene effect.Of the seven crosses,five displayed high or medium resistance to the stripe disease.

Genetic Analysis of the Resistance to Aspergillus flavus Infection in Maize (Zea mays L.)

Maize （Zea mays L.）, one of main crops in the world, is easily susceptible to Aspergillusflavus （Link： fr） infection, resulting huge loses worldwide. Breeding for A. flavus resistance has been proved an efficient way to solve the problem of aflatoxin contamination. Genetic analysis of the sources of resistance to A.flavus in maize is necessary for this purpose. The complete diallel crosses of 6 inbred lines with different resistance to A.flavus infection were implemented. Inoculation categorical data of each cross were analyzed with the additive-dominant and additive-dominant-epitasis genetic models. Results indicated some crosses fitted the 2 major genes with additive-dominant-epitasis genetic model. Others fitted the major gene and polygene mixed model. Moreover, the additive, dominant, and epitasis effects varied in crosses. The A.flavus resistance was controlled by both major gene and polygene.

Cancer models in preclinical research:A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.

Genetic association study of P2x7 A1513C(rs 3751143) polymorphism and susceptibility to pulmonary tuberculosis: A meta-analysis based on the findings of 11 case-control studies

Objective:To summarize the precise association between pulmonary tuberculosis(PTB) and P2x7 A1513 C gene polymorphism.Methods:PubMed and Google Scholar web-databases were searched for the studies reporting the association of P2x7 A1513 C polymorphism and PTB risk.A meta-analysis was performed for the selected case-control studies and pooled odds ratios(ORs) and 95%confidence intervals(95%CIs) were calculated for all the genetic models.Results:Eleven studies comprising 2 678 controls and 2 113 PTB cases were included in this meta-analysis.We observed overall no significant risk in all the five genetic models.When stratified population by the ethnicity,Caucasian population failed to show any risk of PTB in all the genetics models.In Asian ethnicity,variant allele(C vs.A:P=0.001;QR=1.375,95%CI=1.159-1.632) and heterozygous genotype(AC vs.AA:P=0.001;OR=1.570,95%CI=1.269-1.944) demonstrated significant increased risk of PTB.Likewise,recessive genetic model(CC+AC vs.AA:P=0.001;OR=1.540,95%CI= 1.255-1.890) also demonstrated increased risk of PTB in Asians.Conclusions:Our meta-analysis did not suggest the association of P2x7 A1513 C polymorphism with PTB risk in overall or separately in Caucasian population.However,it plays a significant risk factor for predisposing PTB in Asians.Future larger sample and expression studies are needed to validate this association.

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Optimization of the catch bench design using a genetic algorithm

Rockfalls are one of the hazards that may be associated with open pit mining. The majority of rockfalls occur due to the existing conditions of slopes, such as back break, fractures and joints. Constructing a berm on the catch bench is a popular method for the mitigation of rockfall hazards in open pit mining.The width of the catch bench and the height of the berm play a major role in the open pit bench design.However, there is no systematic method currently available to optimize the size of these parameters. This study proposes a novel methodology which calculates the optimum catch bench width by integrating the rockfall simulation model and genetic algorithm into a Simulation-Optimization Model. The proposed methodology is useful when used to determine the minimum catch bench width, or the maximum overall slope angle, insuring that a sufficient factor of safety of the slope is included while maximizing the overall profitability of the open pit mine.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.