Table_7_Marker-trait association analyses revealed major novel QTLs for grain yield and related traits in durum wheat.xlsx

The growing global demand for wheat for food is rising due to the influence of population growth and climate change. The dissection of complex traits by employing a genome-wide association study (GWAS) allows the identification of DNA markers associated with complex traits to improve the productivity of crops. We used GWAS with 10,045 single nucleotide polymorphism (SNP) markers to search for genomic regions associated with grain yield and related traits based on diverse panels of Ethiopian durum wheat. In Ethiopia, multi-environment trials of the genotypes were carried out at five locations. The genotyping was conducted using the 25k Illumina Wheat SNP array to explore population structure, linkage disequilibrium (LD), and marker-trait associations (MTAs). For GWAS, the multi-locus Fixed and Random Model Circulating Probability Unification (FarmCPU) model was applied. Broad-sense heritability estimates were high, ranging from 0.63 (for grain yield) to 0.97 (for thousand-kernel weight). The population structure based on principal component analysis, and model-based cluster analysis revealed two genetically distinct clusters with limited admixtures. The LD among SNPs declined within the range of 2.02–10.04 Mbp with an average of 4.28 Mbp. The GWAS scan based on the mean performance of the genotypes across the environments identified 44 significant MTAs across the chromosomes. Twenty-six of these MTAs are novel, whereas the remaining 18 were previously reported and confirmed in this study. We also identified candidate genes for the novel loci potentially regulating the traits. Hence, this study highlights the significance of the Ethiopian durum wheat gene pool for improving durum wheat globally. Furthermore, a breeding strategy focusing on accumulating favorable alleles at these loci could improve durum wheat production in the East African highlands and elsewhere.

受人口增长与气候变化的双重影响，全球食用小麦需求持续攀升。通过全基因组关联分析（Genome-Wide Association Study, GWAS）解析复杂性状，可识别与复杂性状相关的DNA标记，从而提升作物生产效率。本研究针对埃塞俄比亚硬质小麦的多样化种质群体，利用10045个单核苷酸多态性（Single Nucleotide Polymorphism, SNP）标记开展全基因组关联分析，以筛选与籽粒产量及相关性状关联的基因组区域。研究团队在埃塞俄比亚的5个试验点开展了供试基因型的多环境试验。采用25k Illumina小麦SNP阵列进行基因分型，以解析种群结构、连锁不平衡（Linkage Disequilibrium, LD）及标记-性状关联（Marker-Trait Association, MTAs）。全基因组关联分析采用多位点固定与随机模型循环概率统一（Fixed and Random Model Circulating Probability Unification, FarmCPU）模型。广义遗传力估计值较高，区间为0.63（籽粒产量）至0.97（千粒重）。基于主成分分析与基于模型的聚类分析得到的种群结构结果显示，存在两个遗传分化显著且基因混杂程度较低的类群。SNP间的连锁不平衡衰减距离为2.02~10.04 Mbp，平均衰减距离为4.28 Mbp。基于供试基因型在各环境下的平均表现开展全基因组关联分析扫描，共在各染色体上鉴定出44个显著标记-性状关联位点。其中26个为新发现的关联位点，剩余18个为既往已有报道且本研究得以验证的位点。本研究还为这些新发现的位点筛选到了潜在调控对应性状的候选基因。因此，本研究凸显了埃塞俄比亚硬质小麦种质库在全球硬质小麦遗传改良中的重要价值。此外，针对这些位点聚合有利等位基因的育种策略，可有效提升东非高原及其他地区的硬质小麦生产水平。