ArticleGenes2024
Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Mining Candidate Genes for Maize Tassel Spindle Length Based on a Genome-Wide Association Analysis.Genes · 2024Article
- Leveraging multi-omics tools to comprehend responses and tolerance mechanisms of heavy metals in crop plants.Functional & integrative genomics · 2024Review
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably,
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Registered trials
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