53 research outputs found
A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations
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A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations
Most standard QTL mapping procedures apply to populations derived from the cross of two parents. QTL detected from such biparental populations are rarely relevant to breeding programs because of the narrow genetic basis: only two alleles are involved per locus. To improve the generality and applicability of mapping results, QTL should be detected using populations initiated from multiple parents, such as the multiparent advanced generation intercross (MAGIC) populations. The greatest challenges of QTL mapping in MAGIC populations come from multiple founder alleles and control of the genetic background information. We developed a random-model methodology by treating the founder effects of each locus as random effects following a normal distribution with a locus-specific variance. We also fit a polygenic effect to the model to control the genetic background. To improve the statistical power for a scanned marker, we release the marker effect absorbed by the polygene back to the model. In contrast to the fixed-model approach, we estimate and test the variance of each locus and scan the entire genome one locus at a time using likelihood-ratio test statistics. Simulation studies showed that this method can increase statistical power and reduce type I error compared with composite interval mapping (CIM) and multiparent whole-genome average interval mapping (MPWGAIM). We demonstrated the method using a public Arabidopsis thaliana MAGIC population and a mouse MAGIC population
Wear Model of Silicon Nitride Ceramic Balls in Three-Body Coupling Grinding Mode
Silicon nitride ceramic balls are the key basic components of bearings in major equipment. Their key performance indices are accuracy and batch consistency. A grinding method with the most appropriate comprehensive performance is the basis and guarantee for optimizing these performance indices. In this study, an accurate wear model was established to predict the material removal rates (MRRs) of grinding methods and improve the dynamic grinding control ability of machinery during grinding, thus enabling the mass production of high-grade silicon nitride ceramic balls. A comprehensive analysis of various grinding and polishing methods revealed that the factors affecting sphericity were mainly manifested by the increase in ball sliding and the improvement in MRRs. More over, the three-body coupling grinding mode was considered as the grinding mode that was most applicable to silicon nitride ceramic balls. The upper disk served as an external nonlinear load, and the combination of the rotating speeds of the inner and outer disks of the lower grinding disk could actively control the ball’s angle of rotation. This three-body wear mode can fully envelop the grinding trajectory and ensure uniform grinding. The traditional two-body wear model was unsuitable for three-body coupling grinding. A wear model based on three-body wear was established to predict MRRs and understand the principle of material removal in the grinding of precision spheres. Theoretical analysis and experimental verification revealed that the MRRs of silicon nitride ceramic balls during wear are not only related to the process parameters of external load and speed but also to the physical properties and geometric parameters of balls, abrasives, and processing machinery. The wear model results of silicon nitride ceramic balls in the three-body coupling grinding mode can be obtained stably on the basis of the established wear model by removing adverse effects and adopting optimized processing parameters, thus verifying the correctness of the theoretical and simulation analyses
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Genomic selection using principal component regression
Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.e., partial least squares regression (PLSR), provide a solution through dimensionality reduction. In this study, we show that PCR can perform better than PLSR in cross validation. PCR often requires extracting more components to achieve the maximum predictive ability than PLSR and thus may be associated with a higher computational cost. However, application of the HAT method (a strategy of describing the relationship between the fitted and observed response variables with a hat matrix) to PCR circumvents conventional cross validation in testing predictive ability, resulting in substantially improved computational efficiency over PLSR where cross validation is mandatory. Advantages of PCR over PLSR are illustrated with a simulated trait of a hypothetical population and four agronomical traits of a rice population. The benefit of using PCR in genomic selection is further demonstrated in an effort to predict 1000 metabolomic traits and 24,973 transcriptomic traits in the same rice population
Analysis and Prediction of Influencing Parameters on the Coal Classification Performance of a Novel Three Products Hydrocyclone Screen (TPHS) Based on Grey System Theory
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Metabolome-wide association studies for agronomic traits of rice
Identification of trait-associated metabolites will advance the knowledge and understanding of the biosynthetic and catabolic pathways that are relevant to the complex traits of interest. In the past, the association between metabolites (treated as quantitative traits) and genetic variants (e.g., SNPs) has been extensively studied using metabolomic quantitative trait locus (mQTL) mapping. Nevertheless, the research on the association between metabolites with agronomic traits has been inadequate. In practice, the regular approaches for QTL mapping analysis may be adopted for metabolites-phenotypes association analysis due to the similarity in data structure of these two types of researches. In the study, we compared four regular QTL mapping approaches, i.e., simple linear regression (LR), linear mixed model (LMM), Bayesian analysis with spike-slab priors (Bayes B) and least absolute shrinkage and selection operator (LASSO), by testing their performances on the analysis of metabolome-phenotype associations. Simulation studies showed that LASSO had the higher power and lower false positive rate than the other three methods. We investigated the associations of 839 metobolites with five agronomic traits in a collection of 533 rice varieties. The results implied that a total of 25 metabolites were significantly associated with five agronomic traits. Literature search and bioinformatics analysis indicated that the identified 25 metabolites are significantly involved in some growth and development processes potentially related to agronomic traits. We also explored the predictability of agronomic traits based on the 839 metabolites through cross-validation, which showed that metabolomic prediction was efficient and its application in plant breeding has been justified
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