Malakondaiah Animireddy China, Kumar Sudhir, Krishna Hari, Singh Biswabiplab, Taria Sukumar, Dalal Monika, Dhandapani R, Sathee Lekshmy, Pandey Renu, Kumar Ranjeet Ranjan, Chinnusamy Viswanathan
Division of Plant Physiology, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India.
Division of Genetics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India.
Front Plant Sci. 2025 May 12;16:1553525. doi: 10.3389/fpls.2025.1553525. eCollection 2025.
Micronutrient deficiencies, particularly zinc (Zn) and iron (Fe), are prevalent global health issues, especially among children, that lead to hidden hunger. Wheat is a primary food source for billions of people, but it contains low essential minerals. According to recent studies, the optimum application of nitrogen (N) fertilizers can significantly enhance the micronutrient uptake and accumulation in wheat grains.
The aims of this study were to identify superior wheat recombinant inbred lines (RILs) of RAJ3765 × HD2329 with high nutrients in grain using the multi-trait genotype-ideotype distance index (MGIDI) and to identify quantitative trait loci (QTLs)/genes associated with grain nutrient content using a single-nucleotide polymorphism (SNP)-based genetic linkage map. The parents and their RIL population were grown under control and nitrogen-deficient (NT) conditions, and nutrient content was determined using inductively coupled plasma optical emission spectroscopy (ICP-OES).
Analysis of variance and descriptive statistics showed a significant difference among all the nutrients. The highest mean values of grain iron concentration (GFeC) and grain zinc concentration (GZnC) were 52.729 and 35.137 mg/kg, respectively, under the control condition, while the lowest mean values were 41.016 and 33.117 mg/kg, respectively, recorded under NT; a similar trend was observed in all the elements. Genotyping was carried out using the 35K Axiom Wheat Breeder's Array. A genetic linkage map was constructed using 2,499 polymorphic markers identified for parents across 21 wheat chromosomes. Genetic linkage mapping identified a total of 26 QTLs on 17 different chromosomes. A total of 18 QTLs under the control condition and eight QTLs under the nitrogen stress condition were identified. QTLs for each nutrient were selected based on the high percentage of phenotypic variation explained (PVE%) and logarithm of odds (LOD) score value of more than 3. The LOD scores for studied nutrients varied from 3.04 to 13.42, explaining approximately 1.1% to 27.83% of PVE. One QTL was mapped for grain calcium concentration (GCaC), whereas two QTLs each for grain potassium concentration (GKC), GFeC, grain copper concentration (GCuC), and grain nickel concentration (GNiC) were mapped on different chromosomes. Four QTLs were mapped each for GZnC, grain manganese concentration (GMnC), and grain molybdenum concentration (GMoC), while the highest five were linked to grain barium concentration (GBaC). analysis of these chromosomal regions identified putative candidate genes that code for 30 different types of proteins, which play roles in many important biochemical or physiological processes. Putative candidate gene magnesium transporter MRS2-G linked to GFeC and probable histone-arginine methyltransferase CARM1 and ABC transporter C family were found to be linked to GZnC. These QTLs can be utilized to generate cultivars adapted to climate change by marker-assisted gene/QTL transfer.
微量营养素缺乏,尤其是锌(Zn)和铁(Fe)缺乏,是全球普遍存在的健康问题,在儿童中尤为突出,会导致隐性饥饿。小麦是数十亿人的主要食物来源,但它所含的必需矿物质含量较低。根据最近的研究,氮肥的最佳施用可以显著提高小麦籽粒中微量营养素的吸收和积累。
本研究的目的是使用多性状基因型-理想型距离指数(MGIDI)鉴定RAJ3765×HD2329具有高籽粒营养的优良小麦重组自交系(RIL),并使用基于单核苷酸多态性(SNP)的遗传连锁图谱鉴定与籽粒营养含量相关的数量性状位点(QTL)/基因。亲本及其RIL群体在对照和缺氮(NT)条件下种植,并使用电感耦合等离子体发射光谱法(ICP-OES)测定营养含量。
方差分析和描述性统计表明,所有营养素之间存在显著差异。在对照条件下,籽粒铁浓度(GFeC)和籽粒锌浓度(GZnC)的最高平均值分别为52.729和35.137mg/kg,而在NT条件下记录的最低平均值分别为41.016和33.117mg/kg;所有元素均观察到类似趋势。使用35K Axiom小麦育种家芯片进行基因分型。利用为亲本在21条小麦染色体上鉴定出的2499个多态性标记构建了遗传连锁图谱。遗传连锁图谱在17条不同染色体上共鉴定出26个QTL。在对照条件下共鉴定出18个QTL,在氮胁迫条件下鉴定出8个QTL。根据表型变异解释率(PVE%)和大于3的优势对数(LOD)得分值,为每种营养素选择QTL。所研究营养素的LOD得分在3.04至13.42之间,解释了约1.1%至27.83%的PVE。为籽粒钙浓度(GCaC)定位了1个QTL,而在不同染色体上分别为籽粒钾浓度(GKC)、GFeC、籽粒铜浓度(GCuC)和籽粒镍浓度(GNiC)定位了2个QTL。为GZnC、籽粒锰浓度(GMnC)和籽粒钼浓度(GMoC)各定位了4个QTL,而最高的5个与籽粒钡浓度(GBaC)相关。对这些染色体区域的分析确定了编码30种不同类型蛋白质的推定候选基因,这些基因在许多重要的生化或生理过程中发挥作用。发现与GFeC相关的推定候选基因镁转运蛋白MRS2-G以及可能与GZnC相关的组蛋白精氨酸甲基转移酶CARM1和ABC转运蛋白C家族。这些QTL可用于通过标记辅助基因/QTL转移培育适应气候变化的品种。