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Published on: August 6, 2018
Content characteristics and genetic architecture of macronutrient signatures in cottonseed
Jintao Li1,2, Chengxiang Song3, Yangai Liu2
1Zhengzhou Research Base, State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agriculture and Biomanufacturing, Zhengzhou University, Zhengzhou, 450001, China.
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. 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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