ArticleFrontiers in plant science2022
Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Genome-Wide Association Study Identifies QTNs and Candidate Genes Conferring Resistance to Soybean Frogeye Leaf Spot Race 7.Plants (Basel, Switzerland) · 2026Article
- Genome-Wide Association Study of Yield-Related Traits and Photoperiod Response in Rice.Plants (Basel, Switzerland) · 2026Article
- Fine Mapping of QTLs/QTNs and Mining of Genes Associated with Race 7 of the SoybeanPlants (Basel, Switzerland) · 2025Article
- Integration of GWAS and transcriptome and haplotype analyses to identify QTNs and candidate genes controlling oil content in soybean seeds.Scientific reports · 2025Article
- Multi-locus genome-wide association studies reveal the dynamic genetic architecture of flowering time in chrysanthemum.Plant cell reports · 2024Article
- New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study.BMC plant biology · 2024Article
- Genome-wide association study of agronomic traits related to nitrogen use efficiency in Henan wheat.BMC genomics · 2024Article
- Identification of QTNs, QTN-by-environment interactions for plant height and ear height in maize multi-environment GWAS.Frontiers in plant science · 2023Article
- Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax.Frontiers in plant science · 2023Article
- Genome-wide association study of cooking-caused grain expansion in rice (Frontiers in plant science · 2023Article
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6 authors.
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Abstract
Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (
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