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 five micronutrient concentrations were identified 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 significant 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 efficiency of cultivated wheat by using molecular marker-assisted selection.
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.
