Mapping of resistance to corn borers in a MAGIC population of maize
FECHA:
2019-10-17
IDENTIFICADOR UNIVERSAL: http://hdl.handle.net/11093/4167
TIPO DE DOCUMENTO: article
RESUMEN
Background: Corn borers constitute an important pest of maize around the world; in particular Sesamia
nonagrioides Lefèbvre, named Mediterranean corn borer (MCB), causes important losses in Southern Europe.
Methods of selection can be combined with transgenic approaches to increase the efficiency and durability of the
resistance to corn borers. Previous studies of the genetic factors involved in resistance to MCB have been carried
out using bi-parental populations that have low resolution or using association inbred panels that have a low
power to detect rare alleles. We developed a Multi-parent Advanced Generation InterCrosses (MAGIC) population to
map with high resolution the genetic determinants of resistance to MCB.
Results: We detected multiple single nucleotide polymorphisms (SNPs) of low effect associated with resistance to
stalk tunneling by MCB. We dissected a wide region related to stalk tunneling in multiple studies into three smaller
regions (at ~ 150, ~ 155, and ~ 165 Mb in chromosome 6) that closely overlap with regions associated with cell wall
composition. We also detected regions associated with kernel resistance and agronomic traits, although the colocalization
of significant regions between traits was very low. This indicates that it is possible the concurrent
improvement of resistance and agronomic traits.
Conclusions: We developed a mapping population which allowed a finer dissection of the genetics of maize
resistance to corn borers and a solid nomination of candidate genes based on functional information. The
population, given its large variability, was also adequate to map multiple traits and study the relationship between
them.
Keywords: Maize, Mediterranean corn borer, Sesamia nonagrioides, Resistance, Mapping with multi-parent
advanced generation InterCrosses (MAGIC) populations, Quantitative trait loci (QTL), Genome wide association
analysis (GWAS)