Chelang’at Sitonik, L. M. Suresh, Yoseph Beyene, Michael S. Olsen, Dan Makumbi, Kiplagat Oliver, Biswanath Das, Jumbo M. Bright, Stephen Mugo, Jose Crossa, Amsal Tarekegne, Boddupalli M. Prasanna, Manje Gowda

Theoretical and Applied Genetics; August 2019, Volume 132, Issue 8, pp 2381–2399

Key message

Analysis of the genetic architecture of MCMV and MLN resistance in maize doubled-haploid populations revealed QTLs with major effects on chromosomes 3 and 6 that were consistent across genetic backgrounds and environments. Two major-effect QTLs, qMCMV3-108/qMLN3-108 and qMCMV6-17/qMLN6-17, were identified as conferring resistance to both MCMV and MLN.

Abstract

Maize lethal necrosis (MLN) is a serious threat to the food security of maize-growing smallholders in sub-Saharan Africa. The ability of the maize chlorotic mottle virus (MCMV) to interact with other members of the Potyviridae causes severe yield losses in the form of MLN. The objective of the present study was to gain insights and validate the genetic architecture of resistance to MCMV and MLN in maize. We applied linkage mapping to three doubled-haploid populations and a genome-wide association study (GWAS) on 380 diverse maize lines. For all the populations, phenotypic variation for MCMV and MLN was significant, and heritability was moderate to high. Linkage mapping revealed 13 quantitative trait loci (QTLs) for MCMV resistance and 12 QTLs conferring MLN resistance. One major-effect QTL, qMCMV3-108/qMLN3-108, was consistent across populations for both MCMV and MLN resistance. Joint linkage association mapping (JLAM) revealed 18 and 21 main-effect QTLs for MCMV and MLN resistance, respectively. Another major-effect QTL, qMCMV6-17/qMLN6-17, was detected for both MCMV and MLN resistance. The GWAS revealed a total of 54 SNPs (MCMV-13 and MLN-41) significantly associated (P ≤ 5.60 × 10⁻⁰⁵) with MCMV and MLN resistance. Most of the GWAS-identified SNPs were within or adjacent to the QTLs detected through linkage mapping. The prediction accuracy for within populations as well as the combined populations is promising; however, the accuracy was low across populations. Overall, MCMV resistance is controlled by a few major and many minor-effect loci and seems more complex than the genetic architecture for MLN resistance.

See https://link.springer.com/article/10.1007/s00122-019-03360-x

Figure 3: Manhattan plot and Q–Q plots for the GWAS of MCMV and MLN for disease severity and the AUDPC value in the IMAS association mapping panel. The dashed horizontal line in Manhattan plots depicts the signifcance threshold (P=5.8×10−5). The X-axis indicates the SNP location along the 10 chromosomes, separated by diferent colors; Y-axis is the −log10 (P observed) for each analysis. Q–Q plots depicts infation of observed versus expected −log10 (P values) plots for each trait.