Noel Ndlovu, Charles Spillane, Peter C. McKeown, Jill E. Cairns, Biswanath Das & Manje Gowda

Theoretical and Applied Genetics December 2022; vol. 135: 4351–4370

Key message

Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits.

Abstract

Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.

See https://link.springer.com/article/10.1007/s00122-022-04224-7

Figure 2: Manhattan and quantile–quantile plots generated using a mixed linear model for grain yield (A), Protein content (B), starch content (C) and Oil content (D) under optimum management. The significance level (P = 2 × 10^–5 at 0.1 False Discovery Rate (FDR)) is represented by the dashed horizontal line. The X-axis shows the position of SNPs along the 10 maize chromosomes, with various colours indicating distinct chromosomes. The Y-axis shows the − log10(P observed) in each analysis.