ArticleBMC genetics2016
Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers.
Article in BMC genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 59 citations in OpenAlex.
- An integrated QTL-GWAS approach fine-maps a major locus (qMWS3 - 2) for stable resistance to maize white spot in tropical germplasm.BMC plant biology · 2026Article
- Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.The plant genome · 2023Article
- Genetic variation among elite inbred lines suggests potential to breed for BNI-capacity in maize.Scientific reports · 2023Article
- Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.Genes · 2023Review
- The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement.Current genomics · 2023Review
- Plant-Fungi Interactions: Where It Goes?Biology · 2023Review
- Restructuring plant types for developing tailor-made crops.Plant biotechnology journal · 2023Review
- Genome-wide association study presents insights into the genetic architecture of drought tolerance in maize seedlings under field water-deficit conditions.Frontiers in plant science · 2023Article
- Genetic resources and breeding of maize forFrontiers in plant science · 2023Review
- Back to the wild: mining maize (Zea mays L.) disease resistance using advanced breeding tools.Molecular biology reports · 2022Article
- Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities.Frontiers in genetics · 2022Review
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
- Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea (Frontiers in plant science · 2022Review
- Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Discovery of thermophilic Bacillales using reduced-representation genotyping for identification.BMC microbiology · 2020Article
- Breeding Maize Maternal Haploid Inducers.Plants (Basel, Switzerland) · 2020Review
- Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.G3 (Bethesda, Md.) · 2020Article
- Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers.Frontiers in plant science · 2020Article
- Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
- Genomic Selection with Allele Dosage inG3 (Bethesda, Md.) · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results.
resultsThrough a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes.
conclusionOur work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.