Genome-Wide Association and Prediction of Hybrid Performance for Resistance to Maize Lethal Necrosis in Cimmyt Maize Germplasm in Nakuru County, Kenya
Christine, Nyaga M.
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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. Genomic prediction has also been applied successfully in the prediction of hybrid performance in various crops. The aim of this research was to perform genome wide analyses and predict hybrid performance under MLN infestation in CIMMYT tropical maize germplasm. A set of 915 diverse maize tropical inbred lines and 307 hybrids were evaluated for their response to MLN under artificial inoculation by measuring disease severity and area under disease progress curve (AUDPC). They were also genotyped using SNPs in the genotyping by sequencing (GBS) platform. The research was carried out at the CIMMYT-KALRO MLN Screening facility in Naivasha and CIMMYT Nairobi. The experimental design was Alpha lattice in three replicates and data was collected four times (MLN early and MLN late) on disease resistance where disease severity was scored in the ordinal scale of 1-9. The phenotypic variation was significant for all traits (P< 0.05) and the heritability was 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 assist 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. The correlation between line per se and GCA was moderate. The prediction accuracies based on GCA effects and GP were high for both traits. In conclusion, there is potential for prediction of hybrid performance for maize lethal necrosis.