A genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016–2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits.

使用包含 225 个不同春小麦基因型的关联小组对 10 个产量和产量组成性状进行了全基因组关联研究 (GWAS)。该小组使用 10,904 个 SNP 进行基因分型，并评估了三年（2016-2019），构成了三个环境（E1、E2 和 E3）。不同性状的遗传力范围为29.21%至97.69%。分别使用来自每个环境的数据以及 BLUP 值来识别每个性状的标记-性状关联 (MTA)。使用四种不同的模型，其中包括三种单性状模型（CMLM、FarmCPU、SUPER）和一种多性状模型（mvLMM）。使用每个模型获得了数百个MTA，但经过Bonferroni校正后，使用CMLM仅可获得3个性状的6个MTA，使用FarmCPU可获得4个性状的21个MTA；在 Bonferroni 校正后，使用 SUPER 获得的 525 个 MTA 均不合格。使用 BLUP，可以使用 20 个 MTA，其中 5 个也属于为各个环境确定的 MTA。使用 mvLMM 模型，经过 Bonferroni 校正，可获得 15 个不同性状组合的 38 个多性状 MTA。涉及 28 对 MTA 的上位相互作用也适用于 10 个性状中的 7 个；GNPS、PH 和 BYPP 没有上位相互作用。使用所有 50 个 MTA（CMLM，3；FarmCPU，9；mvLMM，6，上位性，21 和 BLUP，11 MTA）鉴定了多达 164 个推定候选基因 (CG)，范围从 20 (CMLM) 到 66 (上位）CG。还在不同发育阶段的不同组织中进行了 CG 的计算机表达分析。事实证明，通过本研究产生的信息有助于更好地了解这 10 个性状中每一个性状的遗传学；该研究还为标记辅助选择（MAS）提供了新的标记，可用于开发具有改良农艺性状的小麦品种。