ArticleBMC genomics2023
Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 18 citations in OpenAlex.
- Comparative evaluation of GWAS-prioritized SNP, SSR, and insertion-type structural variant marker panels for genomic prediction in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.The plant genome · 2026Article
- Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.BMC plant biology · 2026Article
- Integrating GWAS-guided markers preselection with genomic selection enhances prediction of pulpwood-related traits in slash pine (Pinus elliottii Englem.).BMC plant biology · 2026Article
- From research to application: evaluation of literature-based and newly identified GWAS and GP-derived loci for anthracnose resistance in white lupin, across validation panels and environments.Molecular breeding : new strategies in plant improvement · 2026Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Banana breeding by genome design.Journal of integrative plant biology · 2025Review
- Genomic selection with GWAS-identified QTL markers enhances prediction accuracy for quantitative traits in poplar (Populus deltoides).Communications biology · 2025Article
- Variable selection strategies for genomic prediction of growth and carcass related traits in experimental Nellore cattle herds under different selection criteria.Scientific reports · 2025Article
- Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).The plant genome · 2025Article
- Development of a 101.6K liquid-phased probe for GWAS and genomic selection in pine wilt disease-resistance breeding in Masson pine.The plant genome · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Beyond the genome: the role of functional markers in contemporary plant breeding.Frontiers in plant science · 2025Review
- Increased genomic predictive ability in mango using GWAS-preselected variants and fixed-effect SNPs.Frontiers in plant science · 2025Article
- Genomic prediction of male fertility in Brown Swiss cattle.JDS communications · 2024Article
- Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.Journal of animal science and biotechnology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 - 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest p-values from GWAS showed the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000-4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.
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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.