ArticleFrontiers in plant science2023
Identification of QTN-by-environment interactions for yield related traits in maize under multiple abiotic stresses.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- A genome-wide association study to elucidate the genetic basis of the cottonseed protein content in Upland cotton (Breeding science · 2026Article
- Genome-Wide Association Study of Yield-Related Traits and Photoperiod Response in Rice.Plants (Basel, Switzerland) · 2026Article
- Detection of QTLs and QTL-environment interactions for fruit branch number in Upland cotton (Gossypium hirsutum L.) using a restricted two-stage multi-locus genome-wide association study.BMC plant biology · 2026Article
- Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize.BMC plant biology · 2025Article
- Integrating GWAS with a gene co-expression network better prioritizes candidate genes associated with root metaxylem phenes in maize.The plant genome · 2024Article
- Multi-locus genome-wide association studies reveal the dynamic genetic architecture of flowering time in chrysanthemum.Plant cell reports · 2024Article
- Pilot-scale genome-wide association mapping in diverse sorghum germplasms identified novel genetic loci linked to major agronomic, root and stomatal traits.Scientific reports · 2023Article
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7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) play an increasingly essential role in the genetic dissection of complex traits in crops as global climate change accelerates. The abiotic stresses, such as drought and heat, are the major constraints on maize yields. Multi-environment joint analysis can improve statistical power in QTN and QEI detection, and further help us to understand the genetic basis and provide implications for maize improvement. Methods: In this study, 3VmrMLM was applied to identify QTNs and QEIs for three yield-related traits (grain yield, anthesis date, and anthesis-silking interval) of 300 tropical and subtropical maize inbred lines with 332,641 SNPs under well-watered and drought and heat stresses. Results: Among the total 321 genes around 76 QTNs and 73 QEIs identified in this study, 34 known genes were reported in previous maize studies to be truly associated with these traits, such as ereb53 (GRMZM2G141638) and thx12 (GRMZM2G016649) associated with drought stress tolerance, and hsftf27 (GRMZM2G025685) and myb60 (GRMZM2G312419) associated with heat stress. In addition, among 127 homologs in Arabidopsis out of 287 unreported genes, 46 and 47 were found to be significantly and differentially expressed under drought vs well-watered treatments, and high vs. normal temperature treatments, respectively. Using functional enrichment analysis, 37 of these differentially expressed genes were involved in various biological processes. Tissue-specific expression and haplotype difference analysis further revealed 24 candidate genes with significantly phenotypic differences across gene haplotypes under different environments, of which the candidate genes GRMZM2G064159, GRMZM2G146192, and GRMZM2G114789 around QEIs may have gene-by-environment interactions for maize yield. Discussion: All these findings may provide new insights for breeding in maize for yield-related traits adapted to abiotic stresses.
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