AUTHOR=Kibe Maguta , Nair Sudha K. , Das Biswanath , Bright Jumbo M. , Makumbi Dan , Kinyua Johnson , Suresh L. M. , Beyene Yoseph , Olsen Michael S. , Prasanna Boddupalli M. , Gowda Manje 

TITLE=Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.572027

DOI=10.3389/fpls.2020.572027

ISSN=1664-462X

ABSTRACT=Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome wide association study (GWAS) and  genomic prediction (GP). We used both bi-parental populations and an association panel of 410 diverse tropical/subtropical inbred lines which were genotyped using genotype-by-sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A major effect QTL on chromosome 7(qGLS7-105) explained 28.2% of phenotypic variance. Together, all the detected QTL explained 10.50, 49.70, 23.67, 18.05 and 28.71% of phenotypic variance in DH populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs which individually explained 0.10 to 15.7% of phenotypic variance. GWAS revealed 10 significantly (p< 9.5x10-6) associated SNPs distributed on chromosomes 1, 2, 6, 7 and 8 which individually explained 6 to 8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs, with roles in plant defense against pathogens were identified. GP within populations revealed moderate accuracies with a range of 0.28 to 0.56 and was increased substantially to 0.84 for prediction across DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, revealed 20 to 50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance, while GP further consolidates the development of high GLS resistant lines by incorporating most of the major and minor effect genes.