Apple (Malus spp.) is a widely cultivated economic crop. Leaf-related traits, such as leaf size, pigment content, and photosynthetic rate, serve as important indicators of light use efficiency and are associated with apple fruit quality and potential yields. Identifying quantitative trait loci (QTLs) and genes related to these traits is essential for plant breeding. Genomic selection (GS) presents a promising approach, particularly for improving leaf-related traits in apples. This study focused on identifying QTLs and associated genes, and evaluating the use of GS for leaf-related traits in apples. By genotyping an F1 population (‘Luli’ × ‘Red No. 1’) and constructing a high-density linkage map with 3759 bin markers, 69 QTLs related to leaf traits were identified, explaining 10.4–17.8 % of the phenotypic variance. Forty-six candidate genes were predicted from these QTLs, resulting in 14, 14, 13, and 5 genes for leaf size, pigment content, photosynthetic rate, and fast chlorophyll fluorescence parameters, respectively. The study also assessed the accuracy of GS prediction for leaf traits using genome-wide markers and QTL interval markers through a 10-fold cross-validation. Results indicated that ridge regression BLUP (RR-BLUP) and gradient boosting decision tree (GBDT) methods using QTL interval SNP markers demonstrated higher prediction accuracies compared to whole genome markers for most traits, suggesting the feasibility of employing QTL interval markers for GS prediction. Notably, all models provided valuable genome prediction results with 4 K markers or when the training population represented 80 % of the total population. These findings significantly contribute to gene discovery and genetic improvement efforts related to leaf traits while highlighting the potential utility of GS in accelerating apple breeding programs.

中文翻译：

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'鲁里'×'红1号'苹果杂交种群中叶片相关性状的数量性状位点定位和基因组选择


苹果 （Malus spp.） 是一种广泛种植的经济作物。叶子大小、色素含量和光合速率等与叶子相关的性状是光利用效率的重要指标，与苹果果实的品质和潜在产量有关。鉴定数量性状位点 （QTL） 和与这些性状相关的基因对于植物育种至关重要。基因组选择 （GS） 提供了一种很有前途的方法，特别是对于改善苹果的叶子相关性状。本研究的重点是鉴定 QTL 和相关基因，并评估 GS 在苹果叶片相关性状中的应用。通过对 F1 种群 （'Luli' × 'Red No. 1'） 进行基因分型，并构建具有 3759 个 bin 标记的高密度连锁图谱，鉴定出 69 个与叶片性状相关的 QTL，解释了 10.4-17.8% 的表型方差。从这些 QTL 中预测了 46 个候选基因，分别产生了 14 、 14 、 13 和 5 个叶片大小、色素含量、光合速率和快速叶绿素荧光参数的基因。该研究还通过 10 倍交叉验证评估了使用全基因组标记和 QTL 间隔标记对叶片性状的 GS 预测的准确性。结果表明，与全基因组标记相比，使用 QTL 区间 SNP 标记的岭回归 BLUP （RR-BLUP） 和梯度提升决策树 （GBDT） 方法对大多数性状的预测准确性更高，表明使用 QTL 区间标记进行 GS 预测的可行性。值得注意的是，所有模型都提供了有价值的基因组预测结果，其中包含 4 K 标记，或者当训练人群占总人群的 80% 时。 这些发现对与叶片性状相关的基因发现和遗传改良工作做出了重大贡献，同时强调了 GS 在加速苹果育种计划方面的潜在效用。