Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm

Abstract

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10^-6). For disease severity, these significantly associated SNPs individually explained 3-5% of the total phenotypic variance, whereas for AUDPC they explained 3-12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers' specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.

Keywords: GP; GWAS; maize lethal necrosis; markers; resistance; validation.

Figure 1

Phenotypic distribution of disease severity (MLN-DS) on the scale of 1–9 and the area under disease progress curve (AUDPC) values for maize lethal necrosis (MLN).

Figure 2

Population structure and PCA plot of 914 maize inbred lines estimated from 5085 SNPs. (a) PCA plot for the entire population and colored by the group divisions (DTMA, IMAS, and WEMA). (b) Plot of Delta K was calculated for K = 2 to K = 9. (c) Population structure of the lines for K = 2 and K = 3.

Figure 3

Linkage disequilibrium (LD) plot representing the average genome-wide LD decay in the panels with genome-wide markers. The values on the y-axis represents the squared correlation coefficient r² and the x-axis represents the physical distance in (kb).

Figure 4

Manhattan and Q-Q plots for the GWAS of MLN for disease severity and the AUDPC value. The dashed horizontal line in Manhattan plots depicts the significance threshold (p = 1 × 10⁻⁷). The x-axis indicates the SNP location along the 10 chromosomes, separated by different colors. the red line in the Q-Q plots depicts the line of best fit whereby for both traits the plot of expected −log₁₀(p) against observed −log₁₀(p) falls above the line of best fir.

Figure 5

Box plots showing the phenotypic values of the different allele classes of eight SNPs identified in GWAS for MLN disease severity and AUDPC value. The SNP names and alleles are mentioned below. The black horizontal lines in the middle of the boxes are the median values for the MLN disease severity and AUDPC value in the respective allele classes.

Figure 6

Effect of the number of markers and number of individuals on the accuracy of genomic prediction when the number of markers and size of the training population varied for MLN-DS and AUDPC.