Imagery Phenotyping and Mapping Quantitative Trait Loci for Northern Leaf Blight (Nlb) Resistance in Maize

Abstract

Northern leaf blight (NLB) is a major foliar disease caused by fungus Exserohilum turcicum that leads to limited production of cereals in the Sub-Saharan Africa. Maize is normally susceptible to NLB from the seedling stage to maturity making it expensive in the management and control. The disease lowers production of maize up to 80%, threatening food security in the region. However, to achieve increased food production, improved agricultural technologies should be adopted, whereby research institutions and breeders have continued assessing the breeding values and using advanced technologies for phenotyping diseases. Currently, new technologies have been incorporated where digital imagery tools are used for detecting foliar diseases in the field earlier enough before the severity is high. To curb this major problem of foliar diseases in maize quantitative trait loci (QTL) mapping is recommended and adopted to assist as an effective and efficient tool in breeding to generate resistant host plants. QTL mapping enhances in identification and evaluation of potential sources of resistance followed by introgression of favorable alleles into susceptible variety. This study was implemented to; i) compare the visual scoring method of phenotyping foliar diseases with the digital imagery methodology under a high disease pressure area. ii) Identifying the genomic regions associated with resistance to Northern leaf blight disease through quantitative trait loci (QTL) mapping. One hundred and ninety-two double haploid (DH) lines obtained from International maize and wheat improvement Center (CIMMYT) were test crossed to 2 single cross parents (CML539 x Laposta Seq F64) and (CML 312 x Laposta Seq F64). An alpha lattice design with two replications was used to evaluate the 192DH hybrids with three commercial local checks across two locations in Kenya under high disease pressure area condition during 2016-2017 growing seasons. Each plot measured 4m long spaced at 0.75m between rows and 0.25m

between hills. Data was collected on days to anthesis, grain yield, plant and ear aspect, number of ears, plant and ear height and northern corn leaf blight where the disease severity was scored using a CIMMYT scoring scale of 1-5 where 1-there are no infections, the plant is fully clean, 2- light infection with moderate number of lesions on the lower leaves, 3-moderate infection with abundant lesions on the lower leaves and a few lesions on the middle leaves, 4- heavy infection with lesion abundant on all leaves, 5- very heavy infection with lesions on all leaves. At flowering stage, image analysis was conducted using a Nikon camera where images of the maize plot were taken; scanners were also used where maize leaves from every plot were scanned to obtain a clear view of the damaged lesions. All data collected was analyzed using Meta-R software to obtain the analysis of variance. It was concluded from the studies that digital imagery analysis led to more efficient and effective breeding since it gives accurate and precise information on the field data and also it consumes less time. To identify genomic loci associated with NLB resistance, double haploid (DH) lines from two bi-parental mapping populations were genotyped and marker trait association analysis carried out. Genome-Wide Association Study (GWAS) revealed a major quantitative trait locus (QTL) on chromosome 5 and chromosome 7 that were significantly associated with NLB resistance. This study provides important insights into the genetic architecture underlying resistance to NLB, and identified a useful set of polymorphic single nucleotide polymorphism (SNPs) to be used in breeding for NLB resistance.