Genome-wide Association Analysis of Wheat (Triticum aestivum) Flour Pasting Properties and Candidate Gene Prediction
ZHENG Jie-Xin1, LI Bo2, ZHOU Bin1, RAN Hao-Jiang1, JIA Yao1, GONG Jin-Peng1, XU Fei-Xue1, XU Le1,*, XU Yan-Hao2,*
1 College of Agriculture/MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co-construction by Ministry and Province)/Hubei Key Laboratory of Waterlogging Disaster and Agricultural Use of Wetland, Yangtze University, Jingzhou 434025, China; 2 Institute of Food Crops/Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement/Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
摘要小麦(Triticum aestivum)粉的糊化特性是评价其加工品质的重要指标。挖掘调控小麦粉糊化特性的遗传位点和候选基因,对改良小麦加工品质具有重要意义。本研究以221份小麦材料为研究对象,在3年4个环境条件下,对峰值黏度、低谷黏度、衰减值、最终黏度、回生值、峰值时间和糊化温度7个小麦粉糊化性状进行了表型测定。结合90K SNP芯片基因型数据,采用一般线性模型(general linear model, GLM)、混合线性模型(mixed linear model, MLM)、多位点混合线性模型(multi-locus mixed linear model, MLMM)、固定与随机效应循环概率统一检验模型(fixed and random model circulating probability unification, FarmCPU)和贝叶斯信息与连锁不平衡迭代嵌套关键位点模型(bayesian-information and linkage-disequilibrium iteratively nested keyway, BLINK) 5种模型开展全基因组关联分析(genome-wide association analysis, GWAS)。结果显示,7个性状均表现出丰富的表型变异,其变异系数为0.92%~33.02%,广义遗传力为40.31%~70.36%。在多模型联合GWAS分析中,共鉴定到8个显著且稳定的关联位点,分别分布于2B、5A、6B和7A染色体,单个位点可解释2.09%~9.26%的表型变异。单倍型分析进一步明确了各高置信度关联位点的优势单倍型及其在群体中的分布频率。以高置信度关联位点上下游200 kb范围作为置信区间进行基因筛选,结合基因表达分析,筛选出TraesCS6B03G0474900 (编码甘氨酸裂解系统H家族蛋白)和TraesCS2B03G0671500 (编码40S核糖体蛋白S12) 2个与小麦粉糊化特性相关的候选基因。本研究可为小麦粉品质的遗传解析和分子育种提供参考。
Abstract：Wheat flour pasting properties are important indicators of processing quality. To dissect the genetic basis of these traits, in this study, 221 wheat (Triticum aestivum) accessions were evaluated for 7 wheat flour pasting traits, including peak viscosity, trough viscosity, breakdown, final viscosity, setback, peak time, and pasting temperature, across 4 environments over 3 years. Using 90K SNP array data, a genome-wide association study (GWAS) was conducted with 5 models, namely the general linear model (GLM), mixed linear model (MLM), multi-locus mixed linear model (MLMM), fixed and random model circulating probability unification (FarmCPU), and bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). All 7 traits showed abundant phenotypic variation, with coefficients of variation ranging from 0.92% to 33.02% and broad-sense heritability ranging from 40.31% to 70.36%. Joint GWAS analysis across multiple models identified 8 significant and stable association loci on chromosomes 2B, 5A, 6B, and 7A, each explaining 2.09% to 9.26% of the phenotypic variation. Haplotype analysis further identified the superior haplotypes of these high-confidence loci and their distribution frequencies in the population. Candidate genes were screened within the 200 kb regions upstream and downstream of the high-confidence loci. Combined with gene expression analysis, 2 candidate genes related to wheat flour pasting properties were identified, namely TraesCS6B03G0474900 (encoding a glycine cleavage system H family protein) and TraesCS2B03G0671500 (encoding a 40S ribosomal protein S12). This study provides a reference for the genetic dissection and molecular breeding of wheat flour quality.
[1] 康国章, 王永华, 郭天财, 2024. 小麦淀粉的理化特性及其合成的分子机制[J]. 作物学报, 50(11): 2665-2673. (Kang G K, Wang Y H, Guo T C.2024. Physicochemical properties of wheat starch and the molecular mechanisms of its synthesis[J]. Acta Agronomica Sinica, 50(11): 2665-2673.) [2] 商航, 程宇坤, 任毅, 等 . 2024. 冬小麦淀粉糊化性状的全基因组关联分析[J]. 中国农业科学, 57(18): 3507-3528. (Shang H, Cheng Y K, Ren Y, et al.2024. Genome-wide association analysis of starch gelatinization traits in winter wheat[J]. Scientia Agricultura Sinica, 57(18): 3507-3521.) [3] 宋韵琳, 蔡剑. 2018. 小麦籽粒淀粉理化特性与品质关系及其生理机制研究进展[J]. 麦类作物学报, 38(11): 1338-1351. (Song Y L, & Cai J.2018. Correlation between starch physiochemical properties and quality, as well as its physiological mechanism of starch formation in wheat grains[J]. Journal of Triticeae Crops, 38(11): 1338-1351.) [4] 尉春雪, 何飞, 许蕾, 等 . 2022. 紫花苜蓿甘氨酸脱羧酶H-蛋白基因MsGDC-H1功能分析[J]. 草业学报, 31(12): 95-105. (Wei C X, He F, Xu L, et al.2022. Functional analysis of glycine decarboxylase H-protein gene MsGDC-H1 in Medicago sativa[J]. Acta Prataculturae Sinica, 31(12): 95-105.) [5] 于海霞, 田纪春. 2012. 小麦淀粉糊化特性与DArT标记的关联分析[J]. 作物学报, 38(11): 1997-2006. (Yu H X, Tian J C.2012. Association between starch pasting properties and DArT markers in common wheat[J]. Acta Agron Sin, 38(11): 1997-2006.) [6] 王姗, 胡润雨, 于士男, 等. 2023. 小麦淀粉RVA特性的QTL定位及效应分析[J]. 核农学报, 37(10): 1957-1969. (Wang S, Hu R X, Yu S N, et al.2023. QTL mapping and effect analysis of RVA characteristics of wheat starch[J]. Journal of Nuclear Agricultural Sciences, 37(10): 1957-1967.) [7] 肖龙飞, 张培文, 李文阳, 等. 2025. 沿淮地区小麦籽粒淀粉粒度分布与糊化特性分析[J]. 麦类作物学报, 45(07): 932-941. (Xiao L F, Zhang P W, Li W Y, et al.2025. Analysis of starch granule size distribution and pasting properties of wheat grain in Huai River Region[J]. Journal of Triticeae Crops, 45(07): 932-941.) [8] Alexander D H, Novembre J, & Lange K, 2009. Fast model-based estimation of ancestry in unrelated individuals[J]. Genome Research, 19(9): 1655-1664. [9] Baslam M, Mitsui T, Sueyoshi K, et al.2021. Recent advances in carbon and nitrogen metabolism in C3 plants[J]. International Journal of Molecular Sciences, 22(1): 318. [10] Bates D, Mächler M, Bolker B, et al.2015. Fitting linear mixed-effects models using lme4[J]. Journal of Statistical Software, 67(1): 1-48. [11] Bradbury P J, Zhang Z, Kroon D E, et al.2007. TASSEL: Software for association mapping of complex traits in diverse samples[J]. Bioinformatics, 23(19): 2633-2635. [12] Clauw P, Ellis T J, Liu H J, et al.2025. Beyond the standard GWAS—A guide for plant biologists[J]. Plant and Cell Physiology, 66(4): 431-443. [13] Greene F C.1983. Expression of storage protein genes in developing wheat (Triticum aestivum L.)[J] Seeds, Plant Physiology, 71(1): 40-46. [14] Guo Y, Zhang G, Guo B, et al.2020. QTL mapping for quality traits using a high-density genetic map of wheat[J]. PLOS ONE, 15(3): e0230601. [15] Huang M, Liu X, Zhou Y, et al.2019. BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions[J]. GigaScience, 8(2): giy154. [16] Kale A, Ji Z, Kiparaki M, et al.2018. Ribosomal protein S12e has a distinct function in cell competition[J]. Developmental Cell, 44(1): 42-55. [17] Kaler A S, Gillman J D, Beissinger T, et al.2020. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize[J]. Frontiers in Plant Science, 10: 1794. [18] Kikuchi G, Motokawa Y, Yoshida T, et al.2008. Glycine cleavage system: Reaction mechanism, physiological significance, and hyperglycinemia[J]. Proceedings of the Japan Academy, Series B, 84(7): 246-263. [19] Liu L, Yang T, Yang J, et al.2022. Relationship of starch pasting properties and dough rheology, and the role of starch in determining quality of short biscuit[J]. Frontiers in Plant Science, 13: 829229. [20] Liu X, Huang M, Fan B, et al.2016. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies[J]. PLOS Genetics, 12(2) : e1005767. [21] López-Calcagno P E, Fisk S, Brown K L, et al.2019. Overexpressing the H-protein of the glycine cleavage system increases biomass yield in glasshouse and field-grown transgenic tobacco plants[J]. Plant Biotechnology Journal, 17(1): 141-151. [22] Neumann K, Kobiljski B, Denčić S, et al.2011. Genome-wide association mapping: A case study in bread wheat (Triticum aestivum L.)[J]. Molecular Breeding, 27(1): 37-58. [23] Peng D, Zhang J, Meng L, et al.2024. Penalties in granule size distribution and viscosity parameters of starch caused by lodging in winter wheat[J]. Agronomy, 14(7): 1574. [24] Rhazi L, Méléard B, Daaloul O, et al.2021. Genetic and environmental variation in starch content, starch granule distribution and starch polymer molecular characteristics of french bread wheat[J]. Foods, 10(2): 205. [25] Sandhu K S, Burke A B, Merrick L F, et al.2024. Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat[J]. Frontiers in Plant Science, 15: 1460353. [26] Santos C S dos, Sousa M B, Brito A C, et al.2022. Genome-wide association study of cassava starch paste properties[J]. PLOS ONE, 17(1): e0262888. [27] Segura V, Vilhjálmsson B J, Platt A, et al.2012. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations[J]. Nature Genetics, 44(7): 825-830. [28] Shiferaw B, Smale M, Braun H J, et al.2013. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security[J]. Food Security, 5(3): 291-317. [29] Tian Y, Liu P, Zhang X, et al.2024. Genome-wide association study and KASP marker development for starch quality traits in wheat[J]. The Plant Genome, 17(4): e20514. [30] Tian Y, Sang W, Liu P, et al.2022. Genome-wide association study for starch pasting properties in chinese spring wheat[J]. Frontiers in Genetics, 13: 830644. [31] Ullah R, Yin M, Li S, et al.2024. Genome-wide association study identifies loci and candidate genes for RVA parameters in wheat (Triticum aestivum L.)[J]. Frontiers in Plant Science, 15: 1421924. [32] Wang J, Li Y, Guo X, et al.2024. A Review of the impact of starch on the quality of wheat-based noodles and pasta: from the view of starch structural and functional properties and interaction with gluten[J]. Foods, 13(10): 1507. [33] Wang J, Yang J, Hua W, et al.2018. QTL Mapping reveals the relationship between pasting properties and malt extract in barley[J]. International Journal of Molecular Sciences, 19(11): 3559. [34] Wang J, Zhang Z.2021. GAPIT version 3: Boosting power and accuracy for genomic association and prediction[J]. Genomics, Proteomics & Bioinformatics, 19(4): 629-640. [35] Xu L, Zhao C, Pang J, et al.2022. Genome-wide association study reveals quantitative trait loci for waterlogging-triggered adventitious roots and aerenchyma formation in common wheat[J]. Frontiers in Plant Science, 13: 1066752. [36] Yang Y, Chai Y, Zhang X, et al.2020. Multi-locus GWAS of quality traits in bread wheat: mining more candidate genes and possible regulatory network[J]. Frontiers in Plant Science, 11: 1091. [37] Yu F, Fan X, Yuan H.2025. Effect of stem elongation waterlogging on wheat grain yield, grain traits, and quality of chinese southern-type steamed bread[J]. Agriculture, 15(5): 459. [38] Zhang S, Ghatak A, Bazargani M M, et al.2021. Spatial distribution of proteins and metabolites in developing wheat grain and their differential regulatory response during the grain filling process[J]. The Plant Journal, 107(3): 669-687.
