The trait at the heart of the study is effective panicle number, or EPN, the count of grain-bearing branches that each rice plant ultimately produces. EPN is one of the three canonical components of rice yield, alongside grain weight and grains per panicle, and it is acutely sensitive to nitrogen availability. When nitrogen is plentiful, rice plants produce abundant tillers, many of which develop into effective panicles; when nitrogen is limiting, tiller buds fail to develop and panicle numbers collapse. Because nitrogen fertilizer is typically applied heavily to maximize tillering, varieties that can sustain a high EPN under low nitrogen would allow farmers to cut inputs without sacrificing yield, which is precisely the definition of improved nitrogen use efficiency, or NUE.

Finding the genes behind such quantitative traits in a crop genome as large and repetitive as rice is a formidable challenge, and the team deployed a two-stage strategy that has become one of the most powerful workflows in modern plant genetics. The first stage relied on bulked segregant analysis by sequencing, abbreviated BSA-seq, a technique that combines classical genetics with cheap, high-throughput genome sequencing. The researchers started with a recombinant inbred line population of 1,128 individuals, an unusually large mapping population derived by repeated selfing from a cross between two parental rice lines that differ in their nitrogen response. In such a population, the genomes of individual plants are mosaics of the two parents, shuffled by recombination over many generations.

The logic of BSA-seq is elegantly simple. The researchers measured effective panicle number across the entire population under low-nitrogen conditions, then pooled the DNA of the extreme individuals, those with the highest and lowest EPN values, into two bulked samples. Because the trait differences must be caused by genetic differences, the chromosomal regions that actually harbor the causal variants will be fixed for the high-performing parent’s alleles in the high-EPN bulk and for the low-performing parent’s alleles in the low-EPN bulk. Everywhere else in the genome, the two bulks will contain roughly equal mixtures of both parents. By sequencing the bulks and scanning the genome for regions where the parental allele frequencies diverge sharply between the two pools, the team could localize the trait to a specific stretch of chromosome without genotyping every individual plant.

This first-pass analysis flagged a region on chromosome 6, which the researchers named qNUE6, spanning approximately 1.93 million base pairs. That interval, while far smaller than the whole genome, still contained dozens of genes, so a second stage of higher-resolution mapping was required. The team generated backcross-derived populations, in which plants carrying recombination breakpoints within the candidate region were identified and selfed to produce families that could be scored for EPN under low nitrogen. By correlating the recombination breakpoints with the trait values across these families, a process known as fine linkage mapping, the researchers progressively narrowed the interval until it contained just 61.8 kilobases, a roughly thirtyfold reduction in physical size that left only four annotated genes in play.

Among those four candidates, one gene stood out immediately: OsNRT1.2, which encodes a member of the NRT1 family of low-affinity nitrate transporters. Nitrate is the dominant form of nitrogen available to rice roots in most flooded and aerobic soils, and the NRT1 transporter family sits at the front line of nitrogen uptake, moving nitrate across root cell membranes and redistributing it within the plant. When the researchers compared gene expression between the two parental lines under low-nitrogen treatment, OsNRT1.2 showed the strongest response to nitrogen starvation of any gene in the interval, a pattern consistent with a direct role in sensing or acquiring nitrate when supplies run short.

Sequence comparison between the two parents added a further layer of intrigue. Within the OsNRT1.2 region, the team identified one insertion-deletion polymorphism and several single nucleotide polymorphisms, including variants located in the promoter, the regulatory DNA sequence upstream of the gene that controls when, where and how strongly it is transcribed. Promoter polymorphisms are a common source of natural variation in crop genes, because they can alter expression patterns without changing the protein product itself. The authors are careful to note that these promoter variants are candidates rather than proven causes; direct functional testing of the specific promoter differences will be needed to confirm that they drive the differential low-nitrogen response between the parental lines.

To test whether OsNRT1.2 genuinely controls nitrogen use efficiency rather than merely sitting near a causal gene, the researchers turned to reverse genetics, generating rice lines in which the gene was either overexpressed or knocked out using genome editing. The results were striking and internally consistent. Lines with elevated OsNRT1.2 expression took up more nitrogen, produced more effective panicles, yielded more grain and showed higher overall nitrogen use efficiency, while knockout lines showed the opposite phenotype. This pattern of gain and loss of function, in which artificially increasing the gene’s activity improves the trait and removing it degrades the trait, is the gold standard for establishing that a gene positively regulates a biological process, and it places OsNRT1.2 firmly in the category of nitrogen uptake enhancers rather than repressors.

The discovery slots into a rapidly expanding catalogue of nitrogen efficiency genes in rice, several of which have made headlines in recent years. The nitrate transporter NRT1.1B was shown to underlie the divergence in nitrogen use between the indica and japonica rice subspecies and to shape the composition of the root microbiome in field-grown plants. Other studies have implicated transcription factors such as NLP4, NLP6, NIN-LIKE PROTEIN 3 and the GATA family member OsGATA8, as well as sugar-signaling modules like NGR5, in coordinating nitrogen uptake with tillering and yield. What distinguishes the new work is the combination of an unusually large mapping population, a two-stage mapping strategy that converged on a tiny genomic interval, and functional validation through edited lines, all focused on a yield component, effective panicle number, that is directly tied to farmer-visible productivity under nitrogen stress.

The practical implications are considerable, though the authors themselves flag the caveats. Before qNUE6 can be deployed in breeding programs, the locus will need to be validated across diverse rice germplasm to confirm that its effect holds beyond the two parental lines, and the causal promoter variants must be directly tested, for example through base editing or promoter swaps, to establish the molecular mechanism. If those steps succeed, breeders could use DNA markers linked to the favorable OsNRT1.2 haplotype to introgress nitrogen-efficient alleles into elite varieties, a marker-assisted approach that is far faster than phenotyping plants under varying fertilizer regimes. In a world where rice paddies account for a substantial share of agricultural greenhouse gas emissions and fertilizer runoff fuels aquatic dead zones, a gene that lets rice harvest more of every kilogram of applied nitrogen is the kind of discovery that could ripple from the genome to the dinner plate.

Subject of Research: Identification of the qNUE6 locus and the OsNRT1.2 nitrate transporter gene controlling nitrogen use efficiency in rice

Article Title: BSA-seq and fine linkage mapping for the identification of a novel locus (qNUE6) for nitrogen use efficiency in rice

Article References: Zou, Y., Xie, S., Zhao, Y., Liu, Y., Zhang, W., Gao, J., & Xin, W. (2026). BSA-seq and fine linkage mapping for the identification of a novel locus (qNUE6) for nitrogen use efficiency in rice. Theoretical and Applied Genetics, 139(10), Article 291. https://doi.org/10.1007/s00122-026-05380-w

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05380-w

Keywords: rice, nitrogen use efficiency, qNUE6, OsNRT1.2, nitrate transporter, BSA-seq, fine linkage mapping, effective panicle number, quantitative trait locus, plant genetics, crop breeding, sustainable agriculture