ArticleThe plant genome2026
Development of an open-pollinated genetic mapping framework to facilitate the identification of QTL in apples.
Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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1 citing paper in PubMed.
- Development of an open-pollinated genetic mapping framework to facilitate the identification of QTL in apples.The plant genome · 2026Article
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3 authors.
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Abstract
Genetic mapping of traits in apples (Malus domestica), a temperate woody perennial, is challenging due to high heterozygosity, long juvenility, and extensive spatial maintenance requirements for the populations. These factors limit the effective use of traditional quantitative trait locus (QTL) mapping methods in identifying the genetic basis of traits critical for apple production, marketability, and sustainability. We explored the use of open-pollinated (OP) genetic mapping as an alternative to conventional biparental QTL mapping and genome-wide association studies. A QTL was simulated, and the performance of a mixed linear model (MLM) and a multi-locus mixed model (MLMM) in QTL mapping accuracy was compared using a genotyping-by-sequencing dataset of seven interspecific Royal Gala × Malus sieversii biparental F1 populations. The simulation results show that the MLMM outperformed the MLM by accurately identifying the simulated QTL. Analysis of power indicated that a population size of 183 individuals is required to reach a power = 0.8 for a simulated major effect QTL. Mapping resolution analysis showed that a population size of 470-600 individuals, depending on local recombination rates, is necessary to achieve high resolution within the OP population. Simulations demonstrate the potential of OP-based genetic mapping for identifying QTL in apples, reducing the logistical challenges associated with traditional QTL mapping methods. Our results show that OP-based genetic mapping could be used to speed up the identification of novel alleles directly from diverse germplasm collections in apples.
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