ArticleThe plant genome2020
Genomic selection for lentil breeding: Empirical evidence.
Article in The plant genome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 59 citations in OpenAlex.
- Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning.Plants (Basel, Switzerland) · 2026Article
- Comparative genomic analysis of QTL for resistance to Aphanomyces euteiches between pea, lentil, faba bean, and the model species Medicago truncatula.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Harnessing genomic prediction in Brassica napus through a nested association mapping population.The plant genome · 2025Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- EasyGeSe - a resource for benchmarking genomic prediction methods.BMC genomics · 2025Article
- Genomic prediction for heat and herbicide tolerance in faba bean.The plant genome · 2025Article
- Obscured-ensemble models for genomic prediction.PloS one · 2025Article
- Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing.Biological research · 2024Review
- Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.The plant genome · 2024Article
- Applying a non-GMO breeding approach with an identified natural variation to reduce food allergen Len c3 inFrontiers in plant science · 2024Article
- Stacked generalization as a computational method for the genomic selection.Frontiers in genetics · 2024Article
- Review
- Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials.The plant genome · 2023Article
- Wheat Omics: Advancements and Opportunities.Plants (Basel, Switzerland) · 2023Review
- Genomic prediction and selection response for grain yield in safflower.Frontiers in genetics · 2023Article
- Genomic selection for target traits in the Australian lentil breeding program.Frontiers in plant science · 2023Article
- UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.Plants (Basel, Switzerland) · 2022Article
- An evolutionary look into the history of lentil reveals unexpected diversity.Evolutionary applications · 2022Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection (GS) is a marker-based selection initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models are used to implement GS; however, none have been tested for use in lentil (Lens culinaris Medik.) breeding. This study was conducted to compare the accuracy of different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait (ST) models, two multiple-trait (MT) models, and a model that incorporates genotype × environment interaction (GEI) using populations from a lentil diversity panel and two recombinant inbred lines (RILs). The lines in all populations were phenotyped for five phenological traits and genotyped using a custom exome capture assay. Within-population, across-population, and across-environment genomic predictions were made. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, and traits. Single-trait models showed similar accuracy in the absence of large effect quantitative trait loci (QTL) but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and MT-GS models increased prediction accuracy for a low heritability trait by up to 66 and 14%, respectively. Moderate to high accuracies were obtained for within-population (range of .36-.85) and across-environment (range of .19-.89) predictions but across-population prediction accuracy was very low. Results suggest that GS can be implemented in lentil breeding to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.
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Registered trials
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.