ArticlePloS one2019
SNP-based mixed model association of growth- and yield-related traits in popcorn.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Comprehensive analysis of housekeeping genes, tissue-specific genes, and dynamic regulation across developmental stages in pearl millet.BMC genomics · 2024Article
- Applications and Prospects of CRISPR/Cas9-Mediated Base Editing in Plant Breeding.Current issues in molecular biology · 2023Review
- Regional Heritability Mapping of Quantitative Trait Loci Controlling Traits Related to Growth and Productivity in Popcorn (Plants (Basel, Switzerland) · 2021Article
- Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model SpeciesPlants (Basel, Switzerland) · 2020Article
- A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize.Frontiers in genetics · 2020Article
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Authors and funding
16 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of the genes responsible for complex traits is highly promising to accelerate crop breeding, but such information is still limited for popcorn. Thus, in the present study, a mixed linear model-based association analysis (MLMA) was applied for six important popcorn traits: plant and ear height, 100-grain weight, popping expansion, grain yield and expanded popcorn volume per hectare. To this end, 196 plants of the open-pollinated popcorn population UENF-14 were sampled, selfed (S1), and then genotyped with a panel of 10,507 single nucleotide polymorphisms (SNPs) markers distributed throughout the genome. The six traits were studied under two environments [Campos dos Goytacazes-RJ (ENV1) and Itaocara-RJ (ENV2)] in an incomplete block design. Based on the phenotypic data of the S1 progenies and on the genetic characteristics of the parents, the MLMA was performed. Thereafter, genes annotated in the MaizeGDB platform were screened for potential linkage disequilibrium with the SNPs associated to the six evaluated traits. Overall, seven and eight genes were identified as associated with the traits in ENV1 and ENV2, respectively, and proteins encoded by these genes were evaluated for their function. The results obtained here contribute to increase knowledge on the genetic architecture of the six evaluated traits and might be used for marker-assisted selection in breeding programs.
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