ArticleBMC plant biology2026
Quantitative trait loci mapping of heterosis for leaf morphological traits and candidate gene identification in maize.
Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Novel loci for flag leaf thickness with breeding potential: genetic dissection and candidate genes prediction.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundLeaf morphology traits determine photosynthetic efficiency, high-density planting tolerance, and grain yield in maize (Zea mays L.). Understanding the genetic basis of these traits is of great practical significance for optimizing plant architecture and enhancing photosynthetic capacity to improve grain yield.
resultsIn this study, six maternal and paternal testcrossing (TC/M and TC/P) populations were developed from recombinant inbred line (RIL) populations using three inbred lines—Y46 (tropical), Ye107 (temperate), and MON2 (subtropical)—in pairwise crosses. The inbreds represented the most representative lines from the three major heterotic groups: Suwan, Reid, and Non-Reid. Each RIL population was backcrossed to maternal and paternal parents, to generate TC/M and TC/P populations, respectively. All individuals from these testcross populations (TCM/Ps) were genotyped using genotyping-by-sequencing technology. Phenotyping for mid-parent heterosis of leaf morphology traits—leaf length (LL), leaf width (LW), and leaf angle (LA)—in these populations were evaluated over two years and across two field locations in China. The inclusive composite interval mapping (ICIM) approach was used to identify quantitative trait loci (QTLs) and candidate genes associated with heterosis for leaf morphological traits. The results showed that more QTLs for LL were identified in the Suwan × Non-Reid and Reid × Non-Reid heterotic groups than in Suwan × Reid heterotic groups. However, the largest QTL effects—with a phenotypic variance explained (PVE) of 23.31% for LL—were observed in the Reid × Non-Reid heterotic group. For LW and LA, more QTLs were detected in the Reid × Non-Reid and Suwan × Reid heterotic groups than in the Suwan x Non-Reid heterotic groups. The largest QTL effects were observed in the Reid × Non-Reid heterotic group, with a PVE of 32.32% for LW and 23.13% for LA. In total, seven stable QTLs and 14 candidate genes were identified. Through gene expression analysis, six candidate pleiotropic genes—the ones expressed in maize leaves—were identified, among which Zm00001d022618 and Zm00001d008625 play crucial roles in regulating heterosis formation of leaf morphological traits.
conclusionThese findings provide insights into the complexity of heterosis determination in leaf morphological traits and may support precision breeding to optimize plant architecture and harness heterosis in maize.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.