Linkage mapping and genomic prediction of grain quality traits in tropical maize (Zea mays L.)

Abstract

The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18–0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.

Genomic loci associated with grain yield under well-watered and water-stressed conditions in multiple bi-parental maize populations

“…The absence of QTLs associated with GY and related traits on certain chromosomes in our analysis, compared to previous studies, highlights the complex interplay of genes and environmental pressures that significantly shape QTL identification in tropical maize. The observed disparities can be attributed to distinct maize populations and growing/management conditions employed (Ndlovu et al, 2024). This further emphasizes the need to consider these prevailing interactions when investigating genetic influences on maize traits under WS conditions.…”

“…Genomic prediction offers an alternative and complementary tool to achieve high selection efficiency with optimum resources (Beyene et al, 2015(Beyene et al, , 2019(Beyene et al, , 2021Atanda et al, 2021). Several studies reported that genomic-prediction-based models are effective in identifying betterperforming genotypes for GY and other agronomic and disease resistance traits (Crossa et al, 2017;Sitonik et al, 2019;Ertiro et al, 2020;Kibe et al, 2020a;Gowda et al, 2021;Ndlovu et al, 2022;Kimutai et al, 2023;Ndlovu et al, 2024). The effectiveness of GS compared to traditional phenotypic selection plays a significant role in determining its likelihood of adoption in breeding programs (Beyene et al, 2019;Kibe et al, 2020b).…”

“…Using conventional breeding to improve traits associated with WS tolerance presents a range of challenges, including its laborious and slow nature (Nikolić et al, 2013). However, there is significant potential to overcome some WS-tolerance breeding challenges by incorporating molecular breeding [e.g., quantitative-trait loci (QTL) mapping (Zhao et al, 2019;Hu et al, 2021;Sarkar et al, 2023), genome-wide association studies (GWAS) (Khan et al, 2022;Anilkumar et al, 2023;Chen et al, 2023) and genomic selection (GS) (Beyene et al, 2015;Cerrudo et al, 2018;He et al, 2019;Ndlovu et al, 2022Ndlovu et al, , 2024Zhang et al, 2022)] and phenomicsassisted breeding [i.e., high-throughput phenotyping (Wu et al, 2021)] approaches. To unravel the genetic architecture of WS tolerance in tropical maize, molecular breeding approaches have become crucial for improving this complex trait.…”