数字农科院2.0

Content characteristics and genetic architecture of macronutrient signatures in cottonseed

文献类型： 外文期刊

作者： Jintao Li;Chengxiang Song;Yangai Liu;Xiaoyu Pei;Zhiqiang Zhang;Zhongying Ren;Kunlun He;Fei Zhang;Jinfeng Guo;Jianhui Ma;Daigang Yang;Wei Li

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期刊名称： Theoretical and Applied Genetics

ISSN： 0040-5752

年卷期： 2026 年 139 卷 1 期

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收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Key message: This study reveals genetic factors influencing macronutrient content in cottonseeds, identifying key loci and candidate genes for breeding strategies to improve seed nutrition. Abstract: Macronutrients such as potassium (K), calcium (Ca), and magnesium (Mg) are essential for crop growth, seed quality, and nutrition and health of humans and animals. Insufficient levels of these macronutrients in cottonseeds can lead to malnutrition in animals consuming cottonseed meal-based products. However, the variation and genetic basis of macronutrient content in cottonseeds remain unclear. Here, we investigated the content of K, Ca, and Mg in cottonseeds from 276 cotton accessions grown across diverse ecological regions in China. All three macronutrients exhibited continuous and considerable large variation in the population, with broad-sense heritability values of 70.14% for K, 65.11% for Ca, and 74.42% for Mg. Correlation and variance analysis showed significant positive correlations among the macronutrients and a strong genetic component underlying their variation. Using genome-wide association analysis with 10,660 high-quality single-nucleotide polymorphisms (SNPs) and multi-environment phenotype data, we identified 313 significant marker–trait associations (MTAs) and 159 quantitative trait loci (QTLs) related to these macronutrients. Notably, we detected a key candidate gene, Gh_D02G2194, encoding a subunit of the V-type H+-ATPase, in which a non-synonymous SNP was significantly associated with both K and Mg contents, highlighting its role as a genetic determinant. Linear regression models demonstrated significant positive correlations between the number of superior allelic variants and elemental content. Finally, genomic prediction analysis revealed that the identified MTAs significantly improve trait quality and prediction accuracy. These findings enhance our understanding of the content variation and genetic architecture of cottonseed macronutrients and provide a breeding strategy for improving macronutrient content in cottonseeds.

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