Sheoran S, Jaiswal S, Raghav N, Sharma R, Gaur A, Jaisri J, Tandon Gitanjali, Singh S, Sharma P, Singh R, Iquebal M A, Angadi U B, Gupta A, Singh G, Singh G P, Rai A, Kumar D, Tiwari R
Indian Council of Agricultural Research-Indian Institute of Wheat and Barley Research, Karnal, India.
Indian Council of Agricultural Research-Indian Agricultural Statistics Research Institute, New Delhi, India.
Front Plant Sci. 2022 Feb 11;12:820761. doi: 10.3389/fpls.2021.820761. eCollection 2021.
Spike fertility and associated traits are key factors in deciding the grain yield potential of wheat. Genome-wide association study (GWAS) interwoven with advanced post-GWAS analysis such as a genotype-phenotype network (geno-pheno network) for spike fertility, grain yield, and associated traits allow to identify of novel genomic regions and represents attractive targets for future marker-assisted wheat improvement programs. In this study, GWAS was performed on 200 diverse wheat genotypes using Breeders' 35K Axiom array that led to the identification of 255 significant marker-trait associations (MTAs) ( ≥ 3) for 15 metric traits phenotyped over three consecutive years. MTAs detected on chromosomes 3A, 3D, 5B, and 6A were most promising for spike fertility, grain yield, and associated traits. Furthermore, the geno-pheno network prioritised 11 significant MTAs that can be utilised as a minimal marker system for improving spike fertility and yield traits. In total, 119 MTAs were linked to 81 candidate genes encoding different types of functional proteins involved in various key pathways that affect the studied traits either way. Twenty-two novel loci were identified in present GWAS, twelve of which overlapped by candidate genes. These results were further validated by the gene expression analysis, Knetminer, and protein modelling. MTAs identified from this study hold promise for improving yield and related traits in wheat for continued genetic gain and in rapidly evolving artificial intelligence (AI) tools to apply in the breeding program.
穗粒育性及相关性状是决定小麦产量潜力的关键因素。全基因组关联研究（GWAS）与先进的GWAS后分析（如用于穗粒育性、产量及相关性状的基因型-表型网络）相结合，能够识别新的基因组区域，是未来标记辅助小麦改良计划的有吸引力的目标。在本研究中，使用育种者35K Axiom芯片对200个不同的小麦基因型进行了GWAS，连续三年对15个表型性状进行了测定，共鉴定出255个显著的标记-性状关联（MTA）（≥3）。在3A、3D、5B和6A染色体上检测到的MTA对穗粒育性、产量及相关性状最具潜力。此外，基因型-表型网络对11个显著的MTA进行了优先排序，这些MTA可作为改良穗粒育性和产量性状的最小标记系统。总共119个MTA与81个候选基因相关联，这些候选基因编码参与各种关键途径的不同类型功能蛋白，以某种方式影响所研究的性状。在本GWAS中鉴定出22个新位点，其中12个与候选基因重叠。这些结果通过基因表达分析、Knetminer和蛋白质建模得到了进一步验证。本研究中鉴定出的MTA有望提高小麦的产量和相关性状，以实现持续的遗传增益，并应用于快速发展的人工智能（AI）工具在育种计划中的应用。
