Quantitative trait loci for resistance to spotted and African maize stem borers (Chilo partellus and Busseola fusca) in a tropical maize (Zea mays L.) population

Abstract

Maize (Zea mays L.) is the staple food in Kenya, and mapping the qualitative trait loci (QTL) associated with resistance to maize stem borer pest is important towards marker assisted breeding for this quantitative trait. The objective of this study was to identify any QTL associated with resistance to Chilo partellus and Busseola fusca, the two important stem borer pests in maize production in Kenya. A total of 203 F 2:3 individuals from a cross between CML442, a stem borer susceptible maize inbred line and CKSBL10026, a stem borer resistant maize inbred line; and 152 SNPs were used for mapping the QTL. Data were collected on leaf damage, stem borer exit holes and stem tunneling length as putative stem borer damage traits. A likelihood odds ratio (LOD) scores of 3.0 and maximum recombination frequency of 0.50 were used to declare linkage. LOD scores between 2.5 and 2.9 were considered strong indications of a QTL. Resistance QTL for the three putative traits were detected on chromosomes 1-7 and 9 for both individual locations and stem borer species analysis. In B. fusca sites, one QTL for reduced stem tunnelling was revealed on chromosome 4 while in the C. partellus sites, one QTL for reduced stem tunnelling was identified on chromosome 4 and another for reduced stem borer exit holes was identified on chromosome 5. Phenotypic variances explained ranged from 6 to 10%, suggesting a need to validate these QTL using a larger population and in different environments.

Maize Combined Insect Resistance Genomic Regions and Their Co-localization With Cell Wall Constituents Revealed by Tissue-Specific QTL Meta-Analyses

“…In this meta-analysis, no QTL experiment conducted in Africa could be included, yet maize resistance to local insects, especially stem borer species could be having a different genetic basis due to the co-evolutionary and environment-dependent nature of plant-insect interactions (War et al, 2012 ; Kliebenstein, 2014 ). Therefore, more QTL discovery studies for resistance to local stem borers such as Busseola fusca and Chilo partellus need to be conducted in addition to the already available ones (Munyiri and Mugo, 2017 ) to allow more comprehensive comparative mappings to be carried out. These recommendations also hold for other parts of the world such as Central and South-America, and Asia maize germplasms and stem borer and storage pest species.…”

“…However, due to non-availability of maps both from the online databases and the publications, we could not include some of the QTL experiments (Groh et al, 1998 ; Brooks et al, 2005 , 2007 ). We also did not consider experiments for which the maps were built using single nucleotide polymorphism (SNP) markers (Orsini et al, 2012 ; Mwololo, 2013 ; Samayoa et al, 2015a ; Munyiri and Mugo, 2017 ), because of a lack of shared markers with the other maps. Also, due to lack of similar markers with the consensus map, from the experiment by Méchin et al ( 2001 ), we could only project chromosome 7 containing one QTL.…”

“…Therefore, toward the application of MAS in maize breeding, several studies investigated the genomic regions controlling maize resistance to insect pests using family-based quantitative trait loci (QTL) analyses. These studies concerned SP species such as MW (García-lara et al, 2009 ; Mwololo, 2013 ; Castro-Álvarez et al, 2015 ) and LGB (Mwololo, 2013 ) and SB species such as the European corn borer (ECB) (Schön et al, 1993 ; Bohn et al, 2000 ; Cardinal et al, 2001 , 2006 ; Jampatong et al, 2002 ; Krakowsky et al, 2002 ; Papst et al, 2004 ), the sugarcane borer (SCB) (Bohn et al, 1996 , 1997 ; Groh et al, 1998 ), the Southwestern corn borer (SWCB) (Bohn et al, 1997 ; Groh et al, 1998 ; Khairallah et al, 1998 ; Brooks et al, 2005 , 2007 ), the Mediterranean corn borer (MCB) (Ordas et al, 2009 , 2010 ; Samayoa et al, 2014 , 2015a ; Jiménez-Galindo et al, 2017 ), and SSB and AMSB (Munyiri and Mugo, 2017 ). However, due to the polygenic nature of insect resistance in maize, these studies resulted in the discovery of a plethora of QTL with mainly low phenotypic effects.…”

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

“…Application of traditional markerassisted selection (MAS) is hampered by the necessity to first discover resistance-associated genomic regions through genetic linkage and genome-wide association mapping methods, both with several shortcomings, especially for complex traits (21)(22)(23). 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).…”

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

“…However, the complex nature of insect resistance traits makes PS slow and expensive, and thus, difficult to implement, especially for resource-constrained breeding programs (20). 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).…”