Quantitative Trait Loci Mapping and Molecular Breeding for Developing Stress Resilient Maize for Sub-Saharan Africa
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The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private institutions, is working to develop and deploy improved maize (Zea mays L.) germplasm that is drought tolerant, nitrogen use efficient (NUE), and disease resistant for sub-Saharan Africa (SSA), using conventional pedigree selection and molecular breeding. Here, we provide an overview of the progress made on (i) quantitative trait loci (QTL) analysis for drought, NUE, and maize lethal necrosis (MLN); (ii) development of production markers for maize streak virus (MSV) and MLN resistance; and (iii) marker-assisted recurrent selection (MARS) and genomic selection (GS) for developing drought tolerant maize germplasm. We identified several small to moderate effect QTL associated with grain yield and anthesis-silking interval under low N, managed drought, and optimum environments, but only a few small to moderate effect QTL were detected in multiple genetic backgrounds. Thus, CIMMYT is conducting the largest public MARS and GS projects in SSA. A recent comparative study of pedigree selection, MARS and GS undertaken across 8 to 10 bi-parental populations, demonstrated the superior performance of both MARS and GS schemes over pedigree selection. More than 90% of current commercial maize germplasm was found to be highly susceptible to MLN disease. Preliminary mapping studies identified three major QTL for MLN disease resistance, and two are being used for pilot line conversion using marker-assisted backcrossing (MABC). For MSV, pilot marker-assisted selection is ongoing, using three single nucleotide polymorphic (SNP) markers to select a major QTL on chromosome 1. Development of drought tolerant and NUE maize varieties must include MLN resistance in eastern Africa, and we strongly suggest to maize breeders to introgress promising genomic regions for MLN resistance using either conventional or molecular methods.
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