ArticleFrontiers in plant science2015
Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy.
Article in Frontiers in plant science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Genetic mapping identifies QTL and candidate genes including Mendel's A gene controlling high levels of partial resistance to Fusarium avenaceum in Pea.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- White Lupin Genomic Selection for Adaptation to Drought or Moderately Calcareous Soil: A Proof-of-Concept Study.International journal of molecular sciences · 2026Article
- Genetic and molecular breeding perspectives on developing abiotic stress tolerant pea (Frontiers in plant science · 2026Review
- Harnessing historical genebank data to accelerate pea breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genetic and molecular approaches for Fusarium wilt resistance in garden pea: advances and future outlook.Plant molecular biology · 2025Review
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Genomic Selection for Pea Grain Yield and Protein Content in Italian Environments for Target and Non-Target Genetic Bases.International journal of molecular sciences · 2025Article
- Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.Plants (Basel, Switzerland) · 2024Review
- Effective population size in field pea.BMC genomics · 2024Article
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 2024Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Genomic prediction for rust resistance in pea.Frontiers in plant science · 2024Article
- Construction of a strawberry breeding core collection to capture and exploit genetic variation.BMC genomics · 2023Article
- A comparison of methods for training population optimization in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic selection and genome-wide association studies in tetraploid chipping potatoes.The plant genome · 2023Article
- Genomic prediction and allele mining of agronomic and morphological traits in pea (Frontiers in plant science · 2023Article
- Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits.Frontiers in plant science · 2023Article
- Accurate prediction of quantitative traits with failed SNP calls in canola and maize.Frontiers in plant science · 2023Article
- On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops.Frontiers in plant science · 2023Article
- The Seed Coat's Impact on Crop Performance in Pea (Plants (Basel, Switzerland) · 2022Review
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11 authors.
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Abstract
Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q (2) of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea.
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