Genetic Basis of Maize Resistance to Multiple-Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

Abstract

Several herbivores feed on maize in field and storage setups making the development of multiple-insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to FAW (fall armyworm) whilst bulked grains were subjected to MW (maize weevil) bioassay, genotyped with Diversity Array Technologies single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance level of 0.05 and 0.01, respectively, and located within or close to multiple-insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple-traits of which six were associated with resistance to both FAW and MW suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of five of the six combined-resistance QTNs, thus, reinforcing the pleiotropy hypothesis. In addition, through In-silico co-functional network inferences, an additional 107 Network-based CGs (NbCGs), biologically connected to the 64 GbCGs, differentially expressed under biotic or abiotic stress were revealed within MIRGRs. The provided multiple-insect resistance physical map should contribute to the development of combined-insect resistance in maize.

Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

“…Application of traditional marker-assisted selection (MAS) is hampered by the necessity to first discover resistanceassociated genomic regions through genetic linkage and GWAS methods, both with several shortcomings, especially for complex traits (21)(22)(23) and have seldom been conducted in African germplasm (8,24), further impeding the application of MAS in the development of insect resistance maize lines in Africa. Moreover, in a previous study, we discovered several quantitative trait nucleotides and genes putatively associated with FAW and MW resistance which confirmed the quantitative nature of these traits, and thus, the difficulty in trying to improve these traits through MAS (25). An alternative to both PS and MAS is genomic selection (GS) which uses whole-genome markers to perform genomic prediction (GP) of breeding values of unphenotyped genotypes from which to select superior candidate genotypes for crossing to produce hybrids or to advance to the next generation (26).…”

<strong>Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils</strong>

“…In addition, genetic linkage and genome-wide association mapping studies have seldom been explored in African germplasm (8,24), which further impedes the application of MAS in the development of insect resistance maize germplasm in Africa. In a previous study, we discovered several quantitative trait nucleotides and genes that are putatively associated with FAW and MW resistance confirming the quantitative nature of these traits, hence the difficulty in improving these traits through MAS (25). An alternative to both PS and MAS is genomic selection (GS) which uses whole-genome markers to perform genomic prediction (GP) of breeding values of unphenotyped genotypes from which to select superior candidate genotypes for crossing to produce hybrids or to advance to the next generation (26).…”

“…The differential performances of the different GP algorithms on the insect resistance traits evaluated in this study could be due to differences in the genetic structures (extent of additive vs. non-additive gene action) of the respective traits (23,38,47,49). Maize resistance to FAW, which was moderately heritable across environments (103), would be expected to be controlled by both additive and non-additive genetic factors including epistasis (104)(105)(106), whereas, MW resistance traits such as GWL, AP, and AK with heritability values above 90% (103) were most likely characterized by a prevalence of additive gene action (107,108) in the current panel.…”

“…Genotyping of the panel and SNPs quality were described in our previous study (25). In brief, maize leaves at the sixth-leaf stage of development were harvested from 341 of the 358 lines of the panel (5-10 plants per line) in 2017B and 2018A (for lines not captured in 2017B).…”

DArT-seq based SNP analysis of diversity, population structure and linkage disequilibrium among 274 cowpea (Vigna unguiculata L. Walp.) accessions

“…SNPs markers were pruned to identify markers which are in true linkage disequilibrium in window size of 500kb ( =0.2 threshold) using the R package SNPRelate [67]. The rate of LD decay was depicted as a graph of variation of R vs 2 along physical genetic distance (Mbp) between pairs of SNP markers; and the average distances at which LD decayed at R vs 2 = 0.1 and R vs 2 =0.2 were estimated in R [59,68]. All data generated or analysed during this study are included in this published article and its supplementary information les.…”