Sethi Mehak, Saini Dinesh Kumar, Devi Veena, Kaur Charanjeet, Singh Mohini Prabha, Singh Jasneet, Pruthi Gomsie, Kaur Amanpreet, Singh Alla, Chaudhary Dharam Paul
Division of Biochemistry, Indian Institute of Maize Research, Ludhiana, Punjab, India.
Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
Front Genet. 2023 Aug 7;14:1248697. doi: 10.3389/fgene.2023.1248697. eCollection 2023.
Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population's hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.
玉米是全球很大一部分人口的重要营养储备。然而,为了有效应对不断增长的世界人口的隐性饥饿问题,必须关注两个关键方面:玉米的生物强化以及通过先进育种技术提高其产量潜力。此外,在单一育种计划中协调多个目标构成了一项复杂的挑战。本研究汇总了过去十年进行的定位研究,确定了与玉米籽粒品质和产量相关性状相关的数量性状位点。对169个组成性状(与品质和产量相关性状有关)的2974个QTL进行的元QTL分析揭示了不同遗传背景和环境下的68个MQTL。这些MQTL中的大多数使用全基因组关联研究(GWAS)的数据进一步进行了验证。此外,筛选出了十个MQTL,称为育种友好型MQTL(BF-MQTL),其显著表型变异解释率超过10%,置信区间小于2 Mb。BF-MQTL进一步用于鉴定潜在的候选基因,包括59个编码参与基本代谢途径的重要蛋白质/产物的基因。还建议在未来的育种计划中利用五个与品质和产量性状相关的BF-MQTL。与小麦和水稻基因组的共线性分析揭示了基因组间的保守区域,表明这些热点区域是未来育种计划中培育生物强化、高产玉米品种的有效目标。经过验证后,鉴定出的候选基因还可用于通过标记辅助育种、基因工程和基因组编辑等各种方法有效地构建植物结构模型并增强理想的品质性状。