ArticleThe plant genome2025
Combining genome-wide association and genomic prediction to unravel the genetic architecture of carotenoid accumulation in carrot.
Article in The plant genome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Combining genome-wide association and genomic prediction to unravel the genetic architecture of carotenoid accumulation in carrot.The plant genome · 2025Article
- Cotton under heat stress: a comprehensive review of molecular breeding, genomics, and multi-omics strategies.Frontiers in genetics · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
Carrots (Daucus carota L.) are a rich source of provitamin A, namely, α- and β-carotene. Breeding programs prioritize increasing β-carotene content for improved color and nutrition. Understanding the genetic basis of carotenoid accumulation is crucial for implementing genomic-assisted selection to develop high-carotenoid lines. While previous studies identified loci (Y2, Y, Or, and REC) associated with carrot color and carotenoid content, this study employed genome-wide association (GWA) in a diverse panel of 738 carrot accessions. We discovered a novel locus with a candidate gene encoding phytoene synthase, a key enzyme in carotenoid biosynthesis. The Y2, Y, Or, and REC loci are mostly fixed in orange varieties, yet considerable variation in carotenoid concentration persists. This suggests a multigenic trait influenced by the environment. GWA of carotenoid concentration identified a quantitative trait locus for total carotenoids and α-carotene. We explored the accuracy of genomic prediction (GP) models to predict carotenoid concentration. We determined the optimal number of plants and plots required for accurate carotenoid phenotyping, finding ≥5 plants per plot and three plots per site as the minimum effective sample per accession. GP models achieved accuracies ranging from 0.06 to 0.40 depending on the carotenoid measured and environment the carrots were assayed. Additional studies in breeding programs will clarify the potential of genomic-assisted selection for high-carotenoid carrots.
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Registered trials
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