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

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

doi:10.3389/fpls.2020.572027

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 and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that 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 QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10–15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10–6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6–8% 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 revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20–50% 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 in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.

Highlights

Maize is the most important cereal crop in sub-Saharan Africa (SSA), where more than 80% of the population rely on it as a source of food, income, and livelihood (Prasanna et al, 2020)
We used one association panel comprising 410tropical maize inbred lines for Genome-wide association studies (GWAS) and Genomic prediction (GP) to understand the genetic basis of resistance to Gray leaf spot (GLS)
We studied five biparental populations using linkage mapping and joint linkage association mapping (JLAM) to understand the underlying architecture of the trait

Summary

Introduction

Maize is the most important cereal crop in sub-Saharan Africa (SSA), where more than 80% of the population rely on it as a source of food, income, and livelihood (Prasanna et al, 2020). Gray leaf spot (GLS) is a one of the major foliar diseases of maize caused by the polycyclic pathogens Cercospora zeae-maydis and Cercospora zeina (Crous et al, 2006; He et al, 2018). In eastern Africa, C. zeae-maydis is more prevalent. GLS poses a serious problem to maize production with estimated yield losses of more than 70% (Liu et al, 2016) under favorable conditions. The disease caused severe economic losses in SSA (Ward, 1996; Vivek et al, 2010; Kibata et al, 2011; Bekeko et al, 2018; Yigrem and Yohannes, 2019). Maize breeding programs in SSA typically incorporate GLS resistance in product pipelines

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