Polished rice is one of the commonly consumed staple foods across the world. However, it contains limited nutrients especially iron (Fe) and zinc (Zn). To identify promising recombinant inbred lines (RILs) for grain Zn and single plant yield, 190 RILs developed from PR116 and Ranbir Basmati were evaluated in two environments (E1 and E2). A subset of 44 contrasting RILs for grain Zn was screened in another two environments (E3 and E4). Phenotypic data was collected for 10 traits, viz., days to 50% flowering, plant height, panicle length, number of tillers, single plant yield (SPY), test weight, Fe and Zn in brown (IBR, ZBR), and polished rice (IPR, ZPR). Stepwise regression analysis of trait data in 190 RILs and a subset of 44 RILs revealed the interdependence of ZPR, ZBR, IPR, and IBR and the negative association of grain Zn with single plant yield. Based on the additive main effect and multiplicative interaction (AMMI) and genotype and genotype × environment interaction (GGE) analyses of the subset of 44 RILs across four environments (E1–E4), six promising RILs were identified for ZPR with >28 ppm. Mapping of 190 RILs with 102 simple sequence repeats (SSRs) resulted in 13 QTLs for best linear unbiased estimates (BLUEs) of traits including advantage over check (AOC). Using genotype-based sequencing (GBS), the subset of 44 RILs was mapped with 1035 single-nucleotide polymorphisms (SNPs) and 21 QTLs were identified. More than 100 epistatic interactions were observed. A major QTLqZPR.1.1(PV 37.84%) and another QTLqZPR.11.1(PV 15.47%) were identified for grain Zn in polished rice. A common major QTL (qZBR.2.1andqZPR.2.1) was also identified on chromosome 2 for grain Zn content across SSR and SNP maps. Two potential candidate genes related to transporters were identified based on network analyses in the genomic regions of QTL < 3 Mb. The RILs identified for grain Zn and SPY were nominated for national evaluation as under rice biofortification, and two QTLs identified based on BLUEs could be used in the rice biofortification breeding programs.

Globally, micronutrient (iron and zinc) enriched rice has been a sustainable and cost-effective solution to overcome malnutrition or hidden hunger. Understanding the genetic basis and identifying the genomic regions for grain zinc (Zn) across diverse genetic backgrounds is an important step to develop biofortified rice varieties. In this case, an RIL population (306 RILs) obtained from a cross between the high-yielding rice variety MTU1010 and the high-zinc rice variety Ranbir Basmati was utilized to pinpoint the genomic region(s) and QTL(s) responsible for grain zinc (Zn) content. A total of 2746 SNP markers spanning a genetic distance of 2445 cM were employed for quantitative trait loci (QTL) analysis, which resulted in the identification of 47 QTLs for mineral (Zn and Fe) and agronomic traits with 3.5–36.0% phenotypic variance explained (PVE) over the seasons. On Chr02, consistent QTLs for grain Zn polished (qZnPR.2.1) and Zn brown (qZnBR.2.2) were identified. On Chr09, two additional reliable QTLs for grain Zn brown (qZnBR.9.1 and qZnBR.9.2) were identified. The major-effect QTLs identified in this study were associated with few key genes related to Zn and Fe transporter activity. The genomic regions, candidate genes, and molecular markers associated with these major QTLs will be useful for genomic-assisted breeding for developing Zn-biofortified varieties.

Background: Key traits for determining rice grain yield include the total number of spikelets per panicle (STOT), the total number of grains per panicle (GTOT), and the percentage of grain filling per panicle (GFTOT). Molecular genetic basis of grain yield is complex and regulates several interconnected genes. Identification of candidate genes and their favourable haplotypes associated with the spikelets, grains and grain filling would accelerate rice grain yield enhancement projects. The interface between genetic and environmental factors often influences the grain filling, affecting overall grain yield. Therefore, we executed a Genome-Wide Association Study (GWAS) to investigate the candidate genes associated with grain-filling traits and to explore genotype × environment (G × E) interactions influencing grain filling traits in rice—STOT, GTOT and GFTOT in the subset of Rice Diversity Panel 1 (RDP1). Methods and results: A set of 188 RDP1 accessions were evaluated across two environments (wet seasons—2021 and 2022) following the standard package of practices to raise healthy crop. Phenotyping analysis identified promising genotypes for three traits of study—STOT and GTOT (Priano Guaira and Tainan-Iku No. 512) and GFTOT (IR8 and Biser-1). GWAS was carried out using 5.2 M SNP dataset in the Parallel Identification of QTL’s using EMMAX (PIQUE) pipeline. Five QTLs, one for STOT (qSTOT6.6), one for GTOT (qGTOT4.2) and three for GFTOT (qGFTOT3.4, qGFTOT3.5 and qGFTOT12.1), were detected. Further, superior haplotypes for eight candidate genes (LOC_Os6g38850 and LOC_OS6g39050 for STOT; LOC_OS4g11040 for GTOT; LOC_OS3g62720, LOC_OS3g62750, LOC_OS3g62820, LOC_OS12g03450 and LOC_OS12g03470 for GFTOT) were identified. Conclusions: Our results provide key genetic information for the enhancement of grain filling traits in rice. The identified candidate genes and superior haplotypes can possibly be utilized in marker-assisted selection to improve rice grain yield through gene-pyramid breeding/haplotype-assisted breeding.

The global application of nitrogen (N) fertilizers continues to rise in efforts to bolster agricultural productivity. However, this surge in usage has led to significant N losses, resulting in low nitrogen use efficiency (NUE) among genotypes and consequent water and air pollution. Although many studies advocate reducing N fertilizer usage, research on screening rice genotypes under graded N application is limited. This study aimed to screen rice genotypes to identify N use efficient cultivars under varying nitrogen levels: N0, N50, N100 and N150. The study also sought to identify key physiological traits linked to grain yield under reduced N conditions. Grain yield decreased by 45.2 % at N0 and 21.4 % at N50 while increasing by 22.3 % at N150 compared to N100. Under reduced N application (N0 and N50), MTU-1010, Vasumati, DRR Dhan-58, Varadhan, Brown Gora SB 92, Tulasi, BV-1692 and DRRH2 exhibited least reduction in grain yield, over N100. Notably, parameters such as ?PSII (actual quantum yield of PSII), ETR (electron transport rate) and qP (coefficient of photochemical quenching) displayed a robust positive association with grain yield under reduced N application compared to the recommended (N100) and high (N150) N application. This underscores the significance of PSII photochemistry in enhancing grain production under limited N. Consequently, leaf chlorophyll fluorescence traits emerge as promising indicators for screening rice genotypes with enhanced NUE under limited N scenarios. In summary, the study conclusively identifies Varadhan as a genotype demonstrating high efficiency in nitrogen utilization, both in terms of grain yield and GYEI, particularly under reduced N regimes.