Selection of Parents for Breeding Edible Varieties of Sweetpotato with High Carotene Content

Sweetpotato varieties with high carotene content have high value for human health. This work aims to improve the breeding efficiency of special varieties with high carotene content by combining molecular markers and agronomic traits to evaluate and screen the parents. The cluster analysis was carried out to identify and screen promising parents by utilizing phenotypic results of the agronomic and characteristic traits, and RAPD and ISSR markers of 15 parents and their offspring by group crossing. Among different parental materials, greater variations were observed in two important traits, the carotene content and the storage root yield. Negative correlation was found between the carotene content and both fresh and dried root yields. The most significant positive correlation was between the carotene content of parents and that of their offspring, with the coefficient of correlation value of 0.7932＊＊. The relationship based on the agronomic characters of the trial materials was not in agreement with the known genealogy, while that based on the molecular marker data showed better result. Each primer amplified 13.8 bands oflSSR markers on average, in which the rate of polymorphic loci was 89.6%. 9.4 bands of RAPD markers were arnplified per primer, in which the rate of polymorphic bands was 74.46%. Large genetic variation of carotene content was found among the parents. For analyzing the genetic diversity of sweetpotato, the molecular marker methods were better than morphological traits; ISSR markers were more efficient than RAPD markers. The combination of molecular markers and agronomic trait analysis may provide valuable theoretic basis for selection of parents to breed new varieties with high carotene content.

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory,the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models(RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection(GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value(GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.

Parental Selection in Rice Cultivar Improvement

The evaluation of rice （Oryza safiva L.） cultivars assists breeders in identifying useful trait relationships and in selecting parents as donors of specific traits. This study was conducted to compare long-grain rice cultivars using genotype x trait （GT） biplot analysis and determine potential donors of traits related to grain yield and quality. Seventeen cultivars in the 2005 and 2006 Uniform Regional Rice Nursery in Beaumont, Texas, USA were analyzed for 20 traits using GT biplot analysis. The GT biplots showed the diversity among cultivars with regards to yield-related traits. Cultivars recommended as donor parents were： Trenasse, Spring, Presidio, and Cocodrie for high grain yield and head rice rate Trenasse and Presidio for semi-dwarfness; Banks for tall plant height; Wells for high flag leaf area, panicle weight, and number and mass of filled grains per panicle; Hidalgo for high tiller density; Francis for high number of spikelets per panicle; Spring and Trenasse for short flowering duration; Cheniere for more days to heading and maturity and Spring for less days; and Spring and Hidalgo for high grain weiqht. Breeders can use these cultivars with specific traits to increase qrain yield and quality.

Improving differential evolution with a new selection method of parents for mutation

In differential evolution （DE）, the salient feature lies in its mutation mechanism that distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms, the parents for mutation are randomly chosen from the current population. Hence, all vectors of population have the equal chance to be selected as parents without selective pressure at all. In this way, the information of population cannot be fully exploited to guide the search. To alleviate this drawback and improve the performance of DE, we present a new selection method of parents that attempts to choose individuals for mutation by utilizing the population information effectively. The proposed method is referred as fitnessand-position based selection （FPS）, which combines the fitness and position information of population simultaneously for selecting parents in mutation of DE. In order to evaluate the effectiveness of FPS, FPS is applied to the original DE algorithms, as well as several DE variants, for numerical optimization. Experimental results on a suite of benchmark functions indicate that FPS is able to enhance the performance of most DE algorithms studied. Compared with other selection methods, FPS is also shown to be more effective to utilize information of population for guiding the search of DE.