Table 1_Genome-wide association study and genome prediction of tallness trait in spinach phenotyping.xlsx

Plant height is a critical agronomic trait in spinach (Spinacia oleracea L.), influencing both mechanical harvesting efficiency and overall yield. In this study, plant height variation was evaluated in 307 United States Department of Agriculture (USDA) germplasm accessions, which were phenotyped and genotyped using 15,058 single-nucleotide polymorphisms (SNPs) obtained from whole-genome resequencing. A genome-wide association study (GWAS) was conducted using the General Linear Model (GLM), Mixed Linear Model (MLM), Multiple Loci Mixed Model (MLMM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) models implemented in the Genomic Association and Prediction Integrated Tool version 3 (GAPIT3). Ten SNPs were significantly associated with plant height: (i) SOVchr1_10780051 (10,780,051 bp) on chromosome (chr) 1; (ii) SOVchr2_68062488 (68,062,488 bp) on chr 2; (iii) SOVchr4_38323167 (38,323,167 bp), SOVchr4_188084317 (188,084,317 bp), and SOVchr4_188084338 (188,084,338 bp) on chr 4; (iv) SOVchr5_70192260 (70,192,260 bp) and SOVchr5_105368320 (105,368,320 bp) on chr 5; and (v) SOVchr6_8139833 (8,139,833 bp), SOVchr6_90951127 (90,951,127 bp), and SOVchr6_91175684 (91,175,684 bp) on chr 6. Genomic prediction (GP) models were applied to estimate genomic estimated breeding values (GEBV) for plant height, achieving an r-value of 0.55 using GWAS-derived SNP markers in cross-population prediction. The integration of GWAS and GP provides insights into the genetic architecture of plant height in spinach and supports marker-assisted breeding strategies to enhance crop management and economic returns.

株高是菠菜（Spinacia oleracea L.）的关键农艺性状，同时影响机械采收效率与总产量。
本研究对307份美国农业部（United States Department of Agriculture, USDA）种质资源的株高变异展开评估，所有材料均基于全基因组重测序获得的15058个单核苷酸多态性（single-nucleotide polymorphisms, SNPs）完成表型与基因型鉴定。
本研究采用基因组关联与预测集成工具版本3（Genomic Association and Prediction Integrated Tool version 3, GAPIT3）中内置的一般线性模型（General Linear Model, GLM）、混合线性模型（Mixed Linear Model, MLM）、多位点混合模型（Multiple Loci Mixed Model, MLMM）、固定和随机模型循环概率统一（Fixed and Random Model Circulating Probability Unification, FarmCPU）以及贝叶斯信息与连锁不平衡迭代嵌套关键模型（Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway, BLINK），开展全基因组关联研究（genome-wide association study, GWAS）。
最终共鉴定到10个与株高显著关联的SNP位点：（i）位于1号染色体（chr 1）的SOVchr1_10780051（10780051 bp）；（ii）位于2号染色体（chr 2）的SOVchr2_68062488（68062488 bp）；（iii）位于4号染色体（chr 4）的SOVchr4_38323167（38323167 bp）、SOVchr4_188084317（188084317 bp）与SOVchr4_188084338（188084338 bp）；（iv）位于5号染色体（chr 5）的SOVchr5_70192260（70192260 bp）与SOVchr5_105368320（105368320 bp）；以及（v）位于6号染色体（chr 6）的SOVchr6_8139833（8139833 bp）、SOVchr6_90951127（90951127 bp）与SOVchr6_91175684（91175684 bp）。
本研究还应用基因组预测（genomic prediction, GP）模型对株高的基因组估计育种值（genomic estimated breeding values, GEBV）进行估算，在跨群体预测中，基于GWAS筛选得到的SNP标记构建的模型相关系数r达0.55。
全基因组关联研究与基因组预测的整合分析，可为解析菠菜株高的遗传结构提供新见解，并支撑优化作物管理、提升经济收益的标记辅助育种策略。