Micronutrient malnutrition affects over three billion people worldwide, especially women and children in developing countries. Increasing the bioavailable concentrations of essential elements in the edible portions of crops is an effective resolution to address this issue. To determine the genetic factors controlling micronutrient concentration in wheat, the quantitative trait locus （QTL） analysis for iron, zinc, copper, manganese, and selenium concentrations in two recombinant inbred line populations was performed. In all, 39 QTLs for ifve micronutrient concentrations were identiifed in this study. Of these, 22 alleles from synthetic wheat SHW-L1 and seven alleles from the progeny line of the synthetic wheat Chuanmai 42 showed an increase in micronutrient concentrations. Five QTLs on chromosomes 2A, 3D, 4D, and 5B found in both the populations showed signiifcant phenotypic variation for 2-3 micronutrient concentrations. Our results might help understand the genetic control of micronutrient concentration and allow the utilization of genetic resources of synthetic hexaploid wheat for improving micronutrient efifciency of cultivated wheat by using molecular marker-assisted selection.
Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.
Since the combining ability was proposed in 1942, efforts to uncover the genetic basis underlying this phenomenon have been ongoing for nearly 70 yr, with little success. Some breeding strategies based on evaluation of combining ability have been produced, and are still extensively used in hybrid breeding. In this review, the genetic basis underlying these breeding strategies is discussed, and a potential genetic control of general combining ability （GCA） is postulated. We suggested that GCA and the yields of inbred lines might be genetically controlled by different sets of loci on the maize genome that are transmitted into offspring. Different inbred lines might possess different favorable alleles for GCA. In hybrids, loci involved in multiple pathways, which are directly or indirectly associated with yield performance, might be regulated by GCA loci. In addition, a case of GCA mapping using a set of testcross progeny from introgression lines is provided.
