ArticleFrontiers in plant science2023
Genomic selection to improve husk tightness based on genomic molecular markers in maize.
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 5 papers.
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5 citing papers in PubMed, 5 citations in OpenAlex.
- Genetic dissection of maize grain moisture content and dehydration rate using high-density bin mapping in a recombinant inbred line population.BMC plant biology · 2025Article
- Genome-wide association analysis and genomic selection for leaf-related traits of maize.PloS one · 2025Article
- Genome-wide association studies and genomic selection for leaf-related traits in maize.Frontiers in plant science · 2025Article
- Article
- Genetic architecture of kernel-related traits in sweet and waxy maize revealed by genome-wide association analysis.Frontiers in genetics · 2024Article
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9 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. Methods: An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods. Results: The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness. Discussion: The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.
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