Research Group Resources Genetics and Reproduction, Department Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Corrensstr. 3, OT Gatersleben, D-06466, Stadt, Seeland, Germany.
Department of Plant Sciences, Crop Development Centre, University of Saskatchewan, Saskatoon, Canada.
Sci Rep. 2020 Feb 7;10(1):2098. doi: 10.1038/s41598-020-59004-4.
Revealing the genetic factors underlying yield and agronomic traits in wheat are an imperative need for covering the global food demand. Yield boosting requires a deep understanding of the genetic basis of grain yield-related traits (e.g., spikelet fertility and sterility). Here, we have detected much natural variation among ancient hexaploid wheat accessions in twenty-two agronomic traits collected over eight years of field experiments. A genome-wide association study (GWAS) using 15 K single nucleotide polymorphisms (SNPs) was applied to detect the genetic basis of studied traits. Subsequently, the GWAS output was reinforced via other statistical and bioinformatics analyses to detect putative candidate genes. Applying the genome-wide SNP-phenotype network defined the most decisive SNPs underlying the traits. Six pivotal SNPs, co-located physically within the genes encoding enzymes, hormone response, metal ion transport, and response to oxidative stress have been identified. Of these, metal ion transport and Gibberellin 2-oxidases (GA2oxs) genes showed strong involvement in controlling the spikelet sterility, which had not been reported previously in wheat. SNP-gene haplotype analysis confirmed that these SNPs influence spikelet sterility, especially the SNP co-located on the exon of the GA2ox gene. Interestingly, these genes were highly expressed in the grain and spike, demonstrating their pivotal role in controlling the trait. The integrative analysis strategy applied in this study, including GWAS, SNP-phenotype network, SNP-gene haplotype, expression analysis, and genome-wide prediction (GP), empower the identification of functional SNPs and causal genes. GP outputs obtained in this study are encouraging for the implementation of the traits to accelerate yield improvement by making an early prediction of complex yield-related traits in wheat. Our findings demonstrate the usefulness of the ancient wheat material as a valuable resource for yield-boosting. This is the first comprehensive genome-wide analysis for spikelet sterility in wheat, and the results provide insights into yield improvement.
揭示小麦产量和农艺性状的遗传因素是满足全球粮食需求的当务之急。提高产量需要深入了解与粒产量相关性状（例如小穗育性和不育性）的遗传基础。在这里，我们在八年的田间试验中检测到了二十个二倍体小麦品种中二十个农艺性状的自然变异。使用 15K 个单核苷酸多态性（SNP）进行了全基因组关联研究（GWAS），以检测所研究性状的遗传基础。随后，通过其他统计和生物信息学分析来加强 GWAS 输出，以检测潜在的候选基因。应用全基因组 SNP-表型网络定义了控制性状的最重要的 SNP。在编码酶、激素反应、金属离子转运和对氧化应激反应的基因内鉴定出六个关键 SNP，这些 SNP 物理上位于基因内。其中，金属离子转运和赤霉素 2-氧化酶（GA2oxs）基因强烈参与控制小穗不育，这在以前的小麦中尚未报道过。SNP-基因单倍型分析证实，这些 SNP 影响小穗不育，尤其是位于 GA2ox 基因外显子上的 SNP。有趣的是，这些基因在谷物和穗中高度表达，表明它们在控制该性状中起着关键作用。本研究应用的综合分析策略，包括 GWAS、SNP-表型网络、SNP-基因单倍型、表达分析和全基因组预测（GP），能够识别功能 SNP 和因果基因。本研究中获得的 GP 输出结果令人鼓舞，可通过对复杂的产量相关性状进行早期预测，来实现产量的提高。我们的研究结果表明，古老的小麦材料作为一种提高产量的宝贵资源具有实用性。这是首次对小麦小穗不育进行全基因组综合分析，研究结果为产量提高提供了新的见解。
